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Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:12:02.
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Transforming Management
Using Artificial Intelligence
Techniques
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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Artificial Intelligence (AI): Elementary to Advanced Practices
Series Editors:Vijender Kumar Solanki, Zhongyu (Joan) Lu, and Valentina E Balas
In the emerging smart city technology and industries, the role of artificial intelli-
gence (AI) is getting more prominent. This AI book series will aim to cover the latest
AI work, which will help the naïve user to get support to solve existing problems, and
for the experienced AI practitioners, it will assist in shedding light for new avenues in
the AI domains. The series will cover the recent work carried out in AI and its asso-
ciated domains; it will cover logics, pattern recognition, Natural Language process-
ing (NLP), expert systems, machine learning, block chain, and big data. The work
domain of AI is quite deep, so it will be covering the latest trends which are evolving
with the concepts of AI and it will be helping those new to the field, practitioners,
students, and researchers to gain some new insights.
Cyber Defense Mechanisms
Security, Privacy, and Challenges
Gautam Kumar, Dinesh Kumar Saini, and Nguyen Ha Huy Cuong
Artificial Intelligence Trends for Data Analytics Using Machine Learning
and Deep Learning Approaches
K. Gayathri Devi, Mamata Rath, and Nguyen Thi Dieu Linh
Transforming Management Using Artificial Intelligence Techniques
Vikas Garg and Rashmi Agrawal
For more information on this series, please visit: https://www.crcpress.com/Artificial-
Intelligence-AI-Elementary-to-Advanced-Practices/book-series/CRCAIEAP
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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Transforming Management
Using Artificial Intelligence
Techniques
Edited by
Vikas Garg and Rashmi Agrawal
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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First edition published 2020
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
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© 2021 Taylor & Francis Group, LLC
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Reasonable efforts have been made to publish reliable data and information, but the author and
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Library of Congress Cataloging-in-Publication Data
Names: Garg, Vikas, editor. | Agrawal, Rashmi, 1978- editor.
Title: Transforming management using artificial intelligence techniques /
edited by Vikas Garg, Rashmi Agrawal & Hebatallah Adam.
Description: First edition. | Boca Raton : CRC Press, 2020. |
Includes bibliographical references and index.
Identifiers: LCCN 2020028983 (print) | LCCN 2020028984 (ebook) |
ISBN 9780367456375 (hbk) | ISBN 9780367608743 (pbk) | ISBN 9781003032410 (ebk)
Subjects: LCSH: Management—Technological innovations. |
Artificial intelligence. | Organizational change.
Classification: LCC HD45 .T685 2020 (print) | LCC HD45 (ebook) |
DDC 658./0563—dc23
LC record available at https://lccn.loc.gov/2020028983
LC ebook record available at https://lccn.loc.gov/2020028984
ISBN: 9780367456375 (hbk)
ISBN: 9781003032410 (ebk)
Typeset in Times
by codeMantra
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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v
ContentsPreface vii......................................................................................................................
Editors xi ......................................................................................................................
Contributors xiii...........................................................................................................
Chapter 1 Big Data and Artificial Intelligence: Revolutionizing the Indian
Retail Industry with Respect to Virtual Jewelry Stores 1
Richa Goel and Seema Sahai
.......................
Chapter 2 Unleashing the Potential of Artificial Intelligence in the
Education Sector for Institutional Efficiency 11
Priyanka Srivastava, Teena Hassija, and Aparna Prashant Goyal
.....................................
Chapter 3 Reinventing HR in the Era of Artificial Intelligence 23
Teena Saharan
.........................
Chapter 4 HR Trends in the Era of Artificial Intelligence 51
Shikha Kapoor
..................................
Chapter 5 The Rise of Automation and Robotics in Warehouse Management 63
Amandeep Dhaliwal
....
Chapter 6 Intelligent MIS for High-Quality Marketing Decisions 73
Anshu Goel and Munish Tiwari
.....................
Chapter 7 Contemporary Trends in Education Transformation Using
Artificial Intelligence 89
Simran Kaur, Nidhi Tandon, and Gurpreet Singh Matharou
.........................................................................
Chapter 8 Artificial Intelligence and Personalized Banking 105
Sonam Rani, Mukul Gupta, and Deepa Gupta
............................
Chapter 9 AI in Fashion: Present and Future Applications 129
Shalini Aggarwal, Priyanka Bhardwaj, and Jalaj Arora
..............................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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vi Contents
Chapter 10 Digitalization, Innovation, and Artificial Intelligence: A Road
Map to the Future of Industry 4.0 143
Vijay Prakash Gupta
....................................................
Chapter 11 A Review of Innovation Diffusion Modelling Literature 157
Gaurav Nagpal and Udayan Chanda
................
Chapter 12 Artificial Intelligence (AI) in Classrooms: The Need of the Hour 169
Neha Puri and Geeta Mishra
.....
Chapter 13 The Role of Social Media in Marketing: A Case Study of World
Vision Rwanda 185
S. N. Singh and Sunita Singhal
.................................................................................
Index 199 ......................................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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vii
PrefaceIn this era, we can observe the changes in the method when a product is being tai-
lored to a market. Sometimes these changes leave us in a surprise when the product
marketed to an individual matches his/her personal preference. It leaves us wonder-
ing how does the marketer know what exactly we need and how come the product
we searched online is everywhere on every screen when we open on our electronic
devices. This all happens because marketers are using big data analytics and artifi-
cial intelligence to analyze and understand their customers better. Today, marketers
are creating a demand for their product by showing the same product, again and
again, somewhere or the other. The day is not far when you just open a shopping site,
and depending on your mood, all the suggestions will be given to you and you might
even find the exact thing you were thinking in your mind. The future of online shop-
ping can be a virtual store where everything you can try in a virtual world before
buying it. Nowadays, technology skills are requisite for job acquisition, as one in two
jobs across the globe requires it. One of the reports of OECD mentions that future
recruiters will no longer pay for what people know – rather, what they can do with
what they know. Recruiters have highlighted that there is a skill shift, where students
need to focus more on knowledge usage rather than knowledge transference, and
artificial intelligence (AI) is playing a major role in bringing this transformation in
the mindset of recruiters, educational institutions, and students.
AI is a machine system device (computer) that can perform tasks that are associ-
ated with intelligent beings. It can understand languages, solve problems, keep cars
on traffic, diagnose medical problems, think, and play chess, and many more. In the
field of education, AI is applied in various spheres such as student affairs, learning
and instructions, administrative efficiency, and student acquisition. When AI can
deliver struggling students with personalized degree planning and also supply them
with features like additional tutoring and advising, it falls in the sphere of student
affairs. Learning and instructions through AI mean it can facilitate instructors with
grading and help in struggling students with resources that they need to succeed.
Administrative efficiency through AI can pull together information from multiple
campus systems and assist the students to decide on course offerings; understand
corporate hiring patterns; and create a curriculum that prepares students to face the
campus placement through such recruiters. AI also provides 24/7 personalized assis-
tance to students to motivate them to get enrolled in a certain course or institution;
hence, it enhances the student’s acquisition process for an institution. All stakehold-
ers essentially get benefitted by the smart decision-making through advanced AI
techniques.
At present, the application of AI is quintessential in the institutions for its success
and has generated curiosity in the arena of the research fraternity, where still many
questions need to be answered. With the recent technological breakthrough, there is
a lot of hype as well as uncertainty about its implication for society and the economy.
It will create a profound impact on making the students future-ready according to the
market needs. It will also develop competence requirements and teaching-learning
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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viii Preface
practices. This study particularly focuses upon its implications in the field of educa-
tion sector where researchers are trying to understand its impact on teaching, learn-
ing, and institutional efficiency. This study will critically review the relevance of AI
applications, specifically in the education sector for framing policies and plans, as
well as examine the future requirements.
In this challenging and competitive economy, the well-designed strategies need
to develop and address the talent crises. Reclassification and rebalancing of work are
required with automation. Today, machines can stimulate the intuitive and emotional
aspects of human behavior. Machines will simplify the comparative advantage of
workers with problem-solving, leadership, creative skills, and empathy. These work-
ers were doing routine, methodical jobs. Intelligent digitalization will alter jobs and
their perceived value. In this digital era there are endless possibilities to use the AI
applications will have huge gains when used in the recruitment and hiring process.
AI-based people analytics is used in the process.
In the past, material movement via manual methods in warehousing was a huge
labor drain. But with the advent of modern technology, there has been a big change
in the way the modern warehousing is carried out nowadays. In recent years, auto-
mated warehousing technologies and robotics have brought in huge transformation in
order fulfillment and material handling work. Robotics as a technology plays a major
role in this advancement. As these technologies become even more sophisticated,
it would further help in making operations and simplifying logistics. The robotics
solutions are also known as warehouse robotics and are of many different levels.
Some of the most commonly used warehouse automation solutions in operations of
businesses are systems such as articulated robotic arms, automated guided vehicles
(AGVs), automated storage and retrieval systems (AS/RS), automated guided carts
(AGCs), autonomous mobile robots (AMRs), many of goods-to-person technology
(G2P), and such machines.
Now, it’s time to locate that data to work in new techniques. The companies can
develop into aggressive if the marketing data could be organized in a reminiscent
approach like recognizing the target market and following the efficient method for
market development, additionally expectation of consumer behavior, to congregate
towards consumer appointment to improve their knowledge. Personalization with
customers now is the key to achievement. Marketers will enhance investment in data
management platforms (DMPs) that permit them to stock up, control, and inspect cus-
tomer data from numerous sources and attach with spectators throughout the modi-
fied marketing movements. The marketer is observing technology to fetch related
applicable marketing knowledge to customers; incorporation of data for the fulfill-
ment of approach is the main applicable feature. These incorporated data stages will
enhance the customer commitment. In disparity to the past years, nowadays getting
data, channelizing and processing is a difficult task, AI as a choice-making instru-
ment to give course to random, and classify data and information.
AI is becoming progressively feasible in each sector of the economy; what’s
more, advanced education is no exception. AI opens up the probability for advanced
education administrations to bring versatility at a phenomenal rate, both inside and
outside the educational premises. Thus, one cause to study is to learn more about
smart tools and the academic environment. AI strives to construct smart solutions
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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ixPreface
as best. Another reason to learn about AI is that these developed smart solutions
are interesting and useful in their own right. AI has produced many sizeable and
surprising products even at its developmental stage. Although no one can predict
the future in detail, it is clear that computers with human-level talent (or better)
would have a big impact on our everyday lives and the future route of civilization.
Often usage of computer is compared with AI within the education system, which
shows that computer seldom leads to disappointing results, whereas AI is having a
positive effect on education applications. Our daily life is encompassed by AI. AI
is also sensitized in education. We are moving with high speed from old methods
of teaching towards modern methods of teaching. AI and education go simultane-
ously. AI technologies support that all students achieve their academic excellence.
In the present scenario, educators got occupied with their valuable time in doing
various tedious tasks such as calculating the attendance of students, feedback pro-
cessing, and giving an assignment to students. Now this tedious task can be done
through AI technologies, and professors can spend their valuable time to students.
AI is a technology that plays an important role in every sector. It makes work more
effective and increases efficiency. Nowadays, the use of AI has been increasing
in the banking sector. The banking sector has been changing rapidly by using AI
for discharging its functions; providing service to customers and working of banks
have improved much as compared to traditional banking. The use of AI in bank-
ing provides ease of services to the customer. By doing a single click, a customer
can transfer money from one account to another. AI, the most buzzing word of the
time, is conceived to be important in every field of life activities, fashion being
one among them. Currently, innovative systems are needed to make suggestions
for fashion goods. These systems are based on sensory analysis, smart tracking
systems, clothing quality control, fashion forecasting, supply chain management,
or social networks that influence the e-marketing of fashion products. We also shall
illustrate and discuss the numerous benefits of the fashion industry using AI in the
current big data era.
Digitalization, or innovation, is not just about the introduction and implementa-
tion of imported technologies; it is about the transformation in the manufacturing
process. There is a need for organizations to change in business processing and the
manufacturing process, and smart ways to tailor to the changing demands of the
consumer, as well as to boost production in order to meet the expected demand to
compete and survive in the industry.
Birth of product innovation is a natural phenomenon that takes place as and when
the ever-changing consumer needs are addressed. The examples of such innovative
technology products can be our mobile handsets, the Wi-Fi routers, the microproces-
sors, the computational devices, the data storage devices, etc. Such innovations in the
product take time to get diffused in the market. While some innovations prove to be
successful, some may also fail if either the understanding of consumer needs is inad-
equate or the implementation of the innovation in terms of market entry strategy or
operational strategy is faulty. There is plenty of literature on the diffusion modeling
of innovations. This review aims to summarize the existing literature on the diffu-
sion modeling of innovations and bring about the research gaps that can be addressed
in the future. While putting forward the research gaps, the authors attempt to give
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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x Preface
due consideration to the contemporary needs of the industry in line with the evolving
consumer dynamics and supply dynamics.
AI can never substitute the role of a teacher in the class as it is profoundly differ-
ent from human intelligence; however, it has the potential to transform education in
allowing teachers to focus on more strategic aspects by handling more operational
tasks. Also in light of the changing needs of the millennial that are more comfort-
able in using mobile technology, it is vital to offer them the tools and enable learning
through mode which is need-based. AI-enabled tools can also be imperative in edu-
cation to reduce bias in interviews, in placements, and in handling routine queries. In
the times to come, AI can be a game-changer in how the education industry works
and processes data. In this book, we have explained the concept of AI and its signifi-
cance in higher education through various means and modes.
This research focuses on the role of social media in marketing efforts to reduce
poverty in Rwanda. In Rwanda, as per the Report of the Millennium Development
Goals (2008:09), the majority of Rwandan live in poverty, which is increasing, and
they are deprived of income to cover necessities such as food, clothing, and shel-
ter. Rwanda which can be called an East African country faces various challenges,
including persistent poverty. Both the state and social media are concerned and
working on reducing the problem. In the direction of reducing poverty in Rwanda,
several programmers and strategies have been applied, which also include the goal of
the Millennium Development and its poverty reduction strategies.
In Rwanda, both social media and government ministries collaborate in their
efforts to reducing poverty. This book studies about 11 organizations that are work-
ing in collaboration. Out of the 11 organizations, 7 are social media organizations
and the other 4 represent government ministries. The study discussed in this book is
qualitative. World Vision Rwanda data has been collected using the method of struc-
tured in-depth interviews. Besides, a social media analysis has been applied. The
findings of this study depict evidence of the role played by “social media”. Although
there was insufficient information concerning how World Vision International uses
social media in Rwanda, the findings from the interviews focus on the importance of
partnering social media with other organizations, as well as the role played by their
shared resources in the poverty reduction process.
This book will be quite useful for the people in the field of management and also
for the people in the domain of technology. Due to its coverage, it is quite useful for
the students and professionals in the area of AI. It endeavors to create sky-scraping-
quality, highly trained, and highly skilled graduates. This book is one such initiative
which is a collection of cutting-edge, high-quality chapters backed by a thorough and
systematic research. It aims to come up with scholarly articles that focus on current
business practices in all areas of management.
We especially thank the diverse and international Review Board members for
their special review. Feedback and suggestions are sincerely invited from our readers
to help further improve the quality.
We hope that this book enriches its readers with contemporary areas of interna-
tional business and understanding of the best practices from around the globe.
Wishing readers an enjoyable reading experience!
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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xi
Editors
Dr. Vikas Garg is a doctorate in commerce and management from CCS University,
Meerut. He is currently working as an Head, Research and Publication, Amity
Business School, Amity University Greater Noida Campus, India. He is UGC NET-
qualified. With academic experience of 15 years, he has an expertise in accounting and
finance. His areas of interests are financial markets, financial reporting, and analysis.
He is associated with several universities as an external guide for research schol-
ars. He is a lifetime member of Indian Commerce Association, Indian Accounting
Association, and Indian Management Association. He is certified in customer rela-
tionship management from IIM, Bangalore. As a professor of Amity University, he
has been an efficient researcher who has published many research papers in various
international and national journals. He is highly efficient in different spheres of work
and producing quality work. He has an in-depth knowledge in the area of finance and
accounting, and has been a consistent performer in delivering accuracy in his tasks.
He has organized many seminars and workshops at different places. He is a very
good team leader and always performs the task with creativity.
Rashmi Agrawal is working as a professor in Department of Computer Applications
in MRIIRS, Faridabad. Dr. Agrawal has a rich teaching experience of more than
18 years. She is UGC-NET (CS)-qualified, PhD, MPhil, MSc, and MBA (IT). She has
completed her PhD in the area of machine learning. Her area of expertise includes
artificial intelligence, machine learning, data mining, and operating system. She has
published more than 50 research papers in various national and international confer-
ences and peer-reviewed journals, and authored/edited many books and chapters. She
has organized various faculty development programs and participated in workshops
and faculty development programs. She is a lifetime member of Computer Society of
India and a Senior member in IEEE. She has been a member of the editorial board in
various journals and Technical Program Committee in various conferences of repute.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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xiii
ContributorsShalini AggarwalChandigarh University
Mohali, Punjab, India
Jalaj AroraChandigarh University
Mohali, Punjab, India
Priyanka BhardwajChandigarh University
Mohali, Punjab, India
Udayan ChandaDepartment of Management
Birla Institute of Technology and
Science
Pilani, Rajasthan, India
Amandeep DhaliwalManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Anshu GoelMangalmay Institute of Management &
Technology
Greater Noida, Uttar Pradesh, India
Richa GoelAmity University Noida
Uttar Pradesh, India
Aparna Prashant GoyalManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Deepa GuptaGL Bajaj Institute of Management &
Research
Greater Noida, Uttar Pradesh, India
Mukul GuptaGL Bajaj Institute of Management &
Research
Greater Noida, Uttar Pradesh, India
Vijay Prakash GuptaInstitute of Technology & Science
Ghaziabad, Uttar Pradesh, India
Teena HassijaManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Shikha KapoorAmity University Noida
Uttar Pradesh, India
Simran KaurManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Gurpreet Singh MatharouManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Geeta MishraAmity University Noida
Uttar Pradesh, India
Gaurav NagpalDepartment of Management
Birla Institute of Technology and
Science
Pilani, Rajasthan, India
Neha PuriAmity University Noida
Uttar Pradesh, India
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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xiv Contributors
Sonam RaniGL Bajaj Institute of Management &
Research
Greater Noida, Uttar Pradesh, India
Seema SahaiAmity University Noida
Teena SaharanDoon Business School
Dehradun, India
S. N. SinghUniversity of Kigali
Kigali, Rwanda
Sunita SinghalKCC Institute of Legal and Higher
Education
Greater Noida, Uttar Pradesh, India
Priyanka SrivastavaManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Nidhi TandonManav Rachna International Institute of
Research and Studies
Faridabad, Haryana, India
Munish TiwariMangalmay Institute of Management &
Technology
Greater Noida, Uttar Pradesh, India
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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1
1 Big Data and Artificial IntelligenceRevolutionizing the Indian Retail Industry with Respect to Virtual Jewelry Stores
Richa Goel and Seema SahaiAmity University
1.1 INTRODUCTION
Big data refers to the large volume of data that is created by people and machines,
and is also dependent on and independent of company. The data set is very huge,
and is constantly changing and increasing. This data set is so huge and complex to
understand that there is always a need for some of the other software packages
to analyze such data because traditional tools cannot analyze such kind of data.
The main purpose of using software on such data is to find the pattern in which
CONTENTS
1.1 Introduction 1 ......................................................................................................
1.2 Big Data and Artificial Intelligence 2 ..................................................................
1.3 Benefits of Big Data and Artificial Intelligence 2 ...............................................
1.4 Changes Brought by Big Data and Artificial Intelligence 3 ................................
1.5 Problem Statement 3 ............................................................................................
1.6 Challenges to Big Data and Artificial Intelligence 3 ...........................................
1.7 Big Data and Artificial Management in Marketing 4 ..........................................
1.8 Big Data Use in Marketing 4 ...............................................................................
1.9 Indian Jewelry Sector 4 .......................................................................................
1.10 Future of Jewelry Sector in India 5 .....................................................................
1.11 SWOT Analysis of a Virtual Store 5 ...................................................................
1.12 Literature Review 5 .............................................................................................
1.13 Future Scope of Big Data and Artificial Intelligence 8 .......................................
Bibliography 8..............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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2 Transforming Management Using Artificial Intelligence Techniques
data is gathered so that the data can be analyzed and the decision can be made
effectively.
Artificial intelligence (AI) is referred to as machine-based intelligence. It uses
a high level of technical support as it demonstrates the intelligence of the machine
in dealing with a problem. This system enables a computer to think like a human,
understand the problem like a human, and give feedback and suggestions. Through
AI, a computer showcases humans like learning and problem-solving skills. The
main purpose of this software is to help individuals with any kind of problems in an
organization or understand customers better.
Virtual store is a type of store that can be made available online or offline or
both. This type of store uses a high level of computerization and machine automa-
tion. These stores allow a customer to shop without even visiting a store or even
physically touching a product. These stores allow the organization to save a lot on
cost. These stores allow round-the-clock accessibility.
These types of stores are used by retailers to make their brand globally available
to save on cost as they do not require any brick-and-mortar store or even any inven-
tory to hold.
1.2 BIG DATA AND ARTIFICIAL INTELLIGENCE
An American computer scientist named John Mashey is believed to be the inventor
of big data, or the one who created the buzz about it in the 1990s.
Big data is dynamic and large in its size with respect to the amount of data cre-
ated by people and machines. To collect, host, and analyze such a kind of data,
there is a constant requirement of innovative and measurable technology that is
used to derive a meaningful insight from the data which can be used by the organi-
zation in order to decide for various aspects such as customers, risks, and strategy.
1.3 BENEFITS OF BIG DATA AND ARTIFICIAL INTELLIGENCE
The major and most important reason to use big data is to facilitate and improve the
decision-making process.
The three key areas where a wise use of big data and marketing strategy can do
wonders for the organizations are as follows:
1. Customer engagement: Big data provides an opportunity for the firms to
know better about the customers and understand their needs better. And it
also helps in identifying the means of serving the customer.
2. Customer retention and loyalty: If an organization wants to retain their
customers, it should satisfy all the needs and desire of their customers from
the organization, which can be studied from past interactions.
3. Marketing optimization and performance: By analyzing data, one can eas-
ily save a lot on marketing expenditures and gain the maximum out of the
less money spent.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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3Big Data and Artificial Intelligence
1.4 CHANGES BROUGHT BY BIG DATA AND ARTIFICIAL INTELLIGENCE
The world is transforming every day with the evolution of big data and the new
technologies that are being introduced regularly. With such transformations, the
marketing of goods is also changing because organizations are having broader
markets to deal with. All the information that the customer feeds in while shop-
ping online is saved and is used to study the behavioral patterns and preferences,
which are then examined by the marketer to give a personalized marketing expe-
rience to the customer. Big data makes data easily accessible by all through the
means of the Internet. All the data is made available in a matter of seconds over
the Internet.
Through big data, organizations can get an instant customer feedback through
social media of their customers; by asking a feedback form directly and analyzing
it will helps in making changes in the product or the organization’s operations. Big
data helps organizations plan, estimate, and expect from a campaign. Through big
data, it is much easier to forecast the future, and benefits and challenges that the firm
may face. Customers’ preferences can be well predicted through their past shopping
patterns or behaviors.
1.5 PROBLEM STATEMENT
Since there are new technologies like AI developing now and then, only a few mar-
keters are using it to understand their customers better. Jewelry sector is considered
an important part of the Indian economy, but there is still very little or no use of
such technological development. Every jeweler faces some problems when a piece of
jewelry is left unsold, such as an increase in inventory cost and also increase in the
holding cost of that particular piece. Every small unorganized jewelry designer faces
such issues. Being small businesses, they have a problem of money. This type of busi-
ness needs a lot of investment to create just one small piece of jewelry, and it may be
a burden for jewelers even if a piece of jewelry is left unsold because they might have
taken loans or mortgage something to get the money for investment.
1.6 CHALLENGES TO BIG DATA AND ARTIFICIAL INTELLIGENCE
1. Selecting the right information: It is extremely difficult to select the right
information from the huge pool of data.
2. Dynamic data: Data is constantly updated and changes, thus making it dif-
ficult to fully rely on the same information.
3. Lack of experts: To analyze such a huge amount of data, experts are
required, but it is extremely difficult to find the best person.
4. How to get value from unstructured data: One needs to decide how to use
the data to gain the maximum out of it.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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4 Transforming Management Using Artificial Intelligence Techniques
5. Security: The major concern with big data is its security as all the data is
available over the Internet, including classified information.
6. Software: Big data analytics use highly technical software, but it is very
difficult to master such software.
1.7 BIG DATA AND ARTIFICIAL MANAGEMENT IN MARKETING
In marketing terms, big data is widely used by many online shopping sites now. But
it was first used by Jeff Bezos, CEO of Amazon. He used the concept of contextual
marketing wherein he tried to interact with his customers constantly by asking a
variety of questions giving online shopping personal touch and remembering the
customer’s last choice, thus starting a revolution in the retail sector.
Big data is opening new channels of information gathering from customers and
markets, which can be analyzed and used by organizations for personal benefits.
1.8 BIG DATA USE IN MARKETING
Big data helps the marketers to enhance their knowledge about their customers, their
preferences, and their needs. Earlier, the marketers would work on guesses and intu-
itions, but now they can confirm their intuitions through various software packages.
A marketer can make use of cookies on their websites to study the pattern of custom-
ers it is tailoring to and what customers are preferring more or what the customer
wants from the marketer.
Big data allows the marketer to create a personalized promotion scheme for every
individual, which helps in attracting every customer and increasing the customer
base. Big data not only helps in promoting a product but also helps in determining the
cost for a new product or an existing product in the market. It also helps in boosting
the sales for the product which is losing its market.
Big data helps in the creation of newer platforms for promoting a product or a
brand. It helps in creating a safer and secure platform for the customers. It also helps
in exploring the capabilities of an organization’s product and different users of an
existing product. It aids in creating a new market for selling a product. For example,
a good sold online is accessible to people all across the world. It helps in broadening
the market for a product.
1.9 INDIAN JEWELRY SECTOR
About 7% of India’s total GDP comes from Indian Gems and Jewelry Sector. And
this sector also contributes 15% to India’s total merchandise exports. This sector
employs more than 4.6 million people. And it is expected to double its employment
by 2022. The jewelry sector is one of the fastest-growing sectors, and it is export-
oriented and labor-intensive.
India is considered the global hub of a jewelry market because India has a huge
number of highly skilled workers who can be employed for low wages. India is the
world’s largest cutting and polishing center for diamonds. India exports about 75% of
the world’s polished diamonds.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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5Big Data and Artificial Intelligence
The Indian gem and jewelry market comprises more than 300,000 players, which
mostly consists of small players. India contributes about 29% of the world’s total
jewelry consumption. India is one of the largest exporters of gems and jewelry. This
industry plays a very important role in the Indian economy, and it also contributes a
major share in India’s exports.
1.10 FUTURE OF JEWELRY SECTOR IN INDIA
The growth in the jewelry sector will be increased by the development of large retail-
ers and brands. Newer opportunities are opened by the established brands to grow.
Online jewelry sales are expected to increase by 2021. The demand for jewelry will
be increasing rapidly.
Virtual stores: A virtual store is a store wherein by the use of technology, a cus-
tomer can buy goods without seeing the product physically but in the virtual world.
Each product is given a barcode or a unique identification code that is scanned by
the customer’s phone and that particular good is added to the virtual shopping cart,
which can be edited before confirming the final order. After the order is given, the
retailer delivers the goods to the customer’s address in a few hours or days depending
upon the availability and the nature of the product.
1.11 SWOT ANALYSIS OF A VIRTUAL STORE
1.12 LITERATURE REVIEW
Moes and Vliet (2017) aimed at understanding how a customer reacts to a physi-
cal store starting its operations online. They studied the benefits an organization
can get by displaying its products in the virtual world. The researchers conducted
TABLE 1.1 SWOT Analysis
Strengths Weaknesses
• Quality processes • Lack of marketing expertise
• Advanced technology skills • Undifferentiated products or services
• Strong communication skills • Poor-quality communication or services
• Time management • Lack of training or technical skill sets
• Professionalism • No clear direction
• Reliability • No systems
• Cost advantage • Weak communication
Opportunities Threats
• A developing market • Competitor’s price
• New market segments • The entry of new players
• Networking • Changing technological trends
• Emerging markets • Lack of technological infrastructure
• Increased consumer’s technical knowledge • Slow growth
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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6 Transforming Management Using Artificial Intelligence Techniques
an experiment on the people for analyzing the study where people were shown a
store existing in a virtual store and all the products were represented in the shop.
The customers were made to experience the store without even visiting it physically.
The target audience was made to experience a virtual shop using photographs,
360-degree view of the shop. Customers were shown all the products in the virtual
world. The researchers gave the virtual store the same look like the physical store.
The findings show that the people who were made to visit the virtual store were more
inclined to shop and had a higher purchase intention, and were also more willing to
visit the physical store.
Keng Siau (2017) aimed at studying the impact of new advancements in technolo-
gies such as AI, robotics, and machine learning on the organization’s overall sales
and marketing. They also studied how these technologies are affecting the various
organizations and their competitors. The researcher refers to the online retail giant
Amazon to show the impact of newer technology used by retailers and how they
are benefitting them over other retailers both online and offline. This review shows
the dynamism of technology in the context of marketing and sales in the past few
years. And it also shows the developments that every new retailer is coming up with
to gain the maximum out of it. The finding shows that with the development of new
t echnologies, the way of marketing and sales will be changed and change will be
faster than expected.
Breen (2017) conducted the market research using a virtual store and also showed
the benefits of conducting market research using a virtual store. The researcher
tries to show the thoughts and experiences of shoppers by taking into consideration
more than 20 consumer goods. The customers were shown various advertisements
for the products, and all their thoughts were then noted. This research showed that
when marketing research is conducted virtually, it gives a better response; it is
highly reliable and is highly cost-effective. Grishikashvili, Dibb, and Meadows (2014) aimed to show the impact of big data on digital marketing. Their study also
showed how marketing has evolved with constant technological advancements and
how organizations are coping up with the change. The authors try to show the way
the organizations are coping with the constant change in technology and how they
are being benefitted by adopting such technologies. They also showed the positive
impact of big data as well as the challenges that are ahead of digital marketing.
Their research shows that big data has some positive impact on digital marketing and
also has brought challenges to digital marketing. But these challenges can be faced
by the proper implementation of technology and knowledge. Peter Smith (2017) through this paper tries to understand the concept of big data and big data analytics.
The author also wants to study the current impact and future trends related to big
data. The author tries to understand how retailers are using this information and how
they are benefitted from this analysis. The author conducted interviews with random
employees of two companies, and the secondary data was collected to get a better
understanding of the concept. The result of the studies conducted showed that the
main benefit to the retailers is that they can understand their customers better now,
thus allowing them to personalize the product and offering them to meet customer’s
needs and demands.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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7Big Data and Artificial Intelligence
Aarti Mittal (2018) tried to give a better understanding of the concept of virtual
stores, showcased the future and current trends related to the virtual stores glob-
ally, and also studied the impact of virtual stores on customers and organizations.
The author showcased various advantages and disadvantages to customers and firms
while incorporating virtual stores in their lives. Harjit Singh (2017) wants to study
the difference in the approach in the context of AI in India and the rest of the world.
The author shows the various fields where AI is already in use and the fields where
AI can be used. The author shows the benefits and challenges of AI in India. The
research shows that India is lagging in terms of usage of AI when compared to the
usage of AI in the rest of the world. And AI has a long way to go in India.
Yand and Siau (2018) aimed to create a detailed analysis of changes in marketing
and sales and jobs as there are advancements in AI. They provide a detailed sum-
mary to the marketers and academicians about the changes in the field of AI.
Marinchak, Forrest, and Hoanca (2018) aimed towards understanding the way
marketing has impacted because of the introduction of AI and virtual personal assis-
tant. Their research is about the various technological advancements that impact
marketing. They take into consideration how the marketers are trying to cope with
the changes in the technology and what all they are trying to incorporate in their
organization to beat the competition. Their research shows that AI is having a major
impact on marketing as some are considering it to be an opportunity and some are
considering it as a threat. Goolsbee (2018) wants to study the change in public policy
in the changing phase of the economy. This paper shows the business practices of
large organizations which use AI to understand their customer better. The author also
tries to find how this data is used by such organizations. The study conducted showed
that these organizations use data to create products and services for their customers.
It also shows that some individuals are fine with the organization using their data and
some are not.
Belarbi et al. and Bradlow et al. (2014) try to predict the future of the retail
industry by the use of big data and big data analytics. The authors show various
techniques and tools for big data that can be used to improve the retail industry. They
also showcased the challenges and barriers that the retailers are likely to face. The
researcher tries to touch upon the issues related to ethics and values of using big data,
and states that there is still a need for a regulatory framework to regulate such issues.
Tykheev and Cannella (2018) create a better understanding of big data and how
it can be applied in the field of marketing. This study helps in understanding the cur-
rent application and future application of big data in organizations. The researchers
use the example of Amazon to show how it can be used and how the data can be used
for organizational benefits. This study also shows the advantages and disadvantages
of big data in an organization.
1.13 FUTURE SCOPE OF BIG DATA AND ARTIFICIAL INTELLIGENCE
Big data is currently in use by various organizations, but in the future, it will be used
by all. The one with the all or most of the information would rule the market for sure.
Table 1.1 shows the SWOT analysis. Following are some of the assumptions:
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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8 Transforming Management Using Artificial Intelligence Techniques
1. The volume of data will increase even more and would be different from
now.
2. Software for big data will improve and more diverse. It will be free-of-
charge, easily accessible, and adjustable.
3. The privacy issue will become more important. As it was mentioned already,
more companies and common people start to worry about the privacy of
their data.
4. Big data volume and complexity require an extreme speed of analysis;
that is why new technologies like machine learning will become more
necessary.
More challenges will arise in the future.
BIBLIOGRAPHY
Agrawal, R. “Technologies for handling big data.” Handbook of Research on Big Data Clustering and Machine Learning, edited by F. P. García Márquez, IGI Global, Hershey, PA, 2020, 34–49.
Benke, K. & Benke, G. (2018). Artificial intelligence and big data in public health. International Journal of Environmental Research and Public Health, 15, 2796. doi:10.3390/ijerph15122796.
Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in the internet of things. CAAI Transactions on Intelligence Technology, 3. doi:10.1049/trit.2018.1008.
Goel, R., Sahai, S., Krishnan, C., Singh, G., Bajpai, C., & Malik, P. (2017). An empirical study to enquire the effectiveness of digital marketing in the challenging age concerning Indian economy. Pertanika Journal of Social Sciences and Humanities Open Access, 25(4), 1569–1584.
Goel, R., Sahai, S., Vinaik, A., & Garg, V. (2019). Moving from cash to cashless economy: A study of consumer perception towards digital transactions. International Journal of Recent Technology and Engineering, 8(1), 1220–1226.
Hussain, M. & Manhas, J. (2016). Artificial intelligence for big data: Potential and relevance. International Academy of Engineering and Medical Research, 1(1), 1–5.
Kibria, M., Nguyen, K., Villardi, G., Ishizu, K., & Kojima, F. (2017). Big data analytics and artificial intelligence in next-generation wireless networks. arXiv:1711.10089v3.
Malik, P., Singh, G., Sahai, S., Bajpai, C., Goel, R., & Krishnan, C. (2017). Consumer awareness of digital payment with special reference to the village area. Pertanika Journal of Social Sciences and Humanities, 25(4), 1585–1600.
O’Leary, D. (2013). Artificial intelligence and big data. Intelligent Systems, IEEE, 28, 96–99. doi:10.1109/MIS.2013.39.
Sahai, S., Goel, R., Garg, V., & Vinaik, A. (2019). Impact of digitization on impulse buying: What makes the customer bite the bait. International Journal of Innovative Technology and Exploring Engineering, 8(7), 2948–2952.
Sahai, S., Goel, R., Venaik, A., & Garg, V. (2019). Impact of digital commerce on the fashion industry to gain customer loyalty. International Journal of Engineering and Advanced Technology, 8(5), 730–740.
Sun, Z. (2017). Big data analytics and artificial intelligence (Report number: BAIS No. 17001). PNG University of Technology. doi:10.13140/RG.2.2.24741.29926.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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9Big Data and Artificial Intelligence
Sun, Z. & Wang, P. (2017). Big data, analytics, and intelligence: An editorial perspective. New Mathematics and Natural Computation, 13, 75–81. doi:10.1142/S17930057170 2001X.
Tyagi, A. (2016). Essay: Artificial intelligence: Boon or bane? SSRN Electronic Journal. doi:10.2139/ssrn.2836438.
Vinaik, A., Goel R., Sahai, S., & Garg, V. (2019). The study of interest of consumers in mobile food ordering apps. International Journal of Recent Technology and Engineering, 8(1), 3424–3429.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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11
2 Unleashing the Potential of Artificial Intelligence in the Education Sector for Institutional Efficiency
Priyanka Srivastava, Teena Hassija, and Aparna Prashant GoyalManav Rachna International Institute of Research and Studies
2.1 INTRODUCTION
Economic development is largely influenced by the development of education and the
literacy rate of a nation. In India, almost half of the population is under the age of
25 years; so, it is imperative to establish a proper support system to boost the literacy
rate in population, which can be done with the help of advanced technologies like
artificial intelligence (AI).
CONTENTS
2.1 Introduction 11 ....................................................................................................
2.2 Meaning of AI 12................................................................................................
2.3 AI and Education Sector 13 .................................................................................
2.3.1 AI in the Indian Education Sector 13 ......................................................
2.4 Benefits of Using AI in Education Sector 14 .......................................................
2.5 Challenges with Indian Education Sector 15.......................................................
2.6 Various AI Tools Used in the Education System 16 ............................................
2.6.1 AI Tools for Students Affairs 17 .............................................................
2.6.2 AI Tools for Learning and Instructions 18 ..............................................
2.6.3 AI Tools for Administrative Efficiency 19 ..............................................
2.6.4 AI Tools for Students Acquisition 20 ......................................................
2.7 Limitation of AI 20 ..............................................................................................
2.8 Conclusion 21 ......................................................................................................
References 21................................................................................................................
Web Links 22 ................................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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12 Transforming Management Using Artificial Intelligence Techniques
AI is accepted and adopted by the education sector at a global level. A report shared
by Niti Aayog (2018) highlights that schools at the global level are spending approxi-
mately USD160 billion on education technologies for better system management.
In this regard, the scenario of the Indian education sector is not very appreciative;
the number of enrollments for admission is increasing day by day, but once the
students have experience within the system, to retain them is a tough task (Niti
Aayog, 2018). According to Popenici and Kerr (2017), education is a human-
centric undertaking; if we want to be corporate AI in the education system in the
near future, then we have to align it with effective pedagogies and better course
curriculum.
2.2 MEANING OF AI
Niti Aayog (2018) defined AI in a very simplified manner: “a collection of various
technologies that facilitate machines to act with elevated levels of intelligence and
imitate the potential of human beings to sense, understand and act”. One of the most
popular definitions of AI has been proposed by John McCarthy in 1956:
The study [of artificial intelligence] is to proceed on the basis of the assumption that
every aspect of learning or any other feature of intelligence can in principle be so
precisely described that a machine can be made to simulate it.
According to Murphy, AI is defined as “an application of software algorithms and
techniques that allow computers and machines to simulate human perception and
decision making processes to complete tasks”.
Popenici and Kerr (2017) defined AI as “computing systems that can engage in
human-like processes such as learning, adapting, synthesizing, self-correction, and
use of data for complex processing tasks”. The usage of AI is not limited to usual
tasks of the banks like sending automated phone calls and text messages, it also
handles most complex tasks, for example, to assist automated driving system in auto-
mobiles. The basic function of AI is to conduct data processing through statistical
algorithms, drive predictions, and give recommendations and suggestions (Bass &
Huet, 2017).
Zeide (2017) has defined AI as “nothing more than to create some software to
accomplish some human tasks”. Present AI has become narrower as it is task-specific.
While talking about the concept of AI, we cannot ignore the importance of “machine
learning”. As we know, AI is a processor for available data, but the advance AI tools
have an innate competence to discover patterns and make estimations. AlphaGo
p roduced by Google is one of the best examples of machine learning (Gibney, 2017).
Miailhe and Hodes (2017) defined “Artificial Intelligence as a system which is
turned from old computer set up to the convergence of three components as big data,
machine learning, and cloud supercomputing”.
Best and Pane (2018) have mentioned that there are two categories of AI. One is
weak AI, which is usually capable of performing only one task or limited tasks such
as Chatbots. It is designed to respond to students’ queries and questions. On the other
side, narrow AI applications are widely popular to handle a range of commercial
transactions as Apple’s Siri, IBM’s Watson, Google’s Alexa, and many more.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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13AI in the Education Sector
From the above definition of AI, it is quite clear that today to make a mark in any
field, one cannot overlook the importance of AI and the role it plays for easy acces-
sibility and faster functioning of complex tasks within an organization.
2.3 AI AND EDUCATION SECTOR
It is believed that AI has a positive influence and progressing outcome on higher
education as it is assisting institutions to scale quality education. There is a variety of
regulatory, societal, and organizational concerns that must be dealt with for taking
advantage of the application of AI tools in the education industry.
Klutka, Ackerly, and Magda (2018) have mentioned that AI is responsive, deci-
sive, adaptive, and independent. Responsive concerning AI means engaging in the
interface from humans or other machines, interpreting its meaning, and preparing an
appropriate response. Decisiveness means AI can interpret supplied information and
take necessary action to attain its goals. Adaptive implies that AI can maximize the
usage of new information and adjust its behavior accordingly to gain effectiveness.
Independent means that AI can conduct most of its decision-making process without
the support of human input.
Woolf et al. (2013) have illuminated that education sector has a big potential for
this technology as it requires new software that can customize the needs and wants of
the student community. It can positively affect the needs of the learners by providing
access to digital materials, connecting learners, and also keeping students engaged
in a very significant manner digitally.
In the era of advanced technologies, the existing pedagogies of teaching like class-
room lectures, printed textbooks, and handouts have become obsolete, especially for
the techno-savvy students.
AI and education are two sides of the same coin: education provides support and
base to develop new software, and AI helps students to enhance their knowledge and
utilize the knowledge for the well-being of the society (Figure 2.1).
From Figure 2.1, it is quite clear that the large-scale industries like high tech
and telecommunications, automotive and assembly, media and entertainment, and
financial services have more digital experience as these industries are more inclined
toward digital expertise, technical skills, and integration between resources and AI
tools. Small- and middle-level industries, on the other hand, are still striving for
digitalization because of the high resource cost and low awareness for adopting AI
in business processes. From the graph in Figure 2.1, it is disappointing to see that the
education sector is lagging way behind in allocating budget on AI tools. So policy-
makers should take necessary action for allocating budget for applying and imple-
menting AI tools within a system.
2.3.1 AI IN THE INDIAN EDUCATION SECTOR
The primary requirement of the Indian education sector is to align with AI tools to
achieve sustainability and growth. The entire education system need to be skill based
and industry integrated; this is an urgent need for higher education. In India, many of
the skill development programs are going on very effectively for both the faculty and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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14 Transforming Management Using Artificial Intelligence Techniques
the student, like MOOCs (Massive Open Online Courses), bridge courses in cross-
domains, training and workshops by central/state government organizations, and GI
clouds. Government and public policies are coping with the speed of implementation
of AI tools in the education sector. AI tools are widely accepted in the private sector,
and new agencies and institutions within the public sector are also welcoming AI
tools for their sustainable development.
2.4 BENEFITS OF USING AI IN EDUCATION SECTOR
AI tools can bring a techno flood in the education sector, which will change the level
of thinking and approaches of the system. But at the same time, we cannot fully rely
on technologies, as human interferences need to be there to identify risks and prob-
lems for which better solutions can be achieved with the aid of AI tools (Popenici
and Kerr, 2017).
Many AI tools can read learners’ cognition, metacognition, motivation, and emo-
tions. Woolf et al. (2013) have pointed out that some software is designed to give
personalized instructions to the learners by understanding their personality, state of
mind, preferences, and other psychological factors. The benefits of AI tools are not
limited to technological and smart classrooms; it brings a change in the teaching
approaches to make the students ready to serve in the competitive world (Schleicher,
2015).
FIGURE 2.1 AI and education. (McKinsey Global Institute AI Adoption and Use Survey;
McKinsey Global Institute Analysis.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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15AI in the Education Sector
2.5 CHALLENGES WITH INDIAN EDUCATION SECTOR
In order to make the education system more secure, lucrative, omnipresent, reach-
able, focused, and invasive, a few obvious challenges need to be overcome by the
industry for better development of learners. The challenges are prevailing in the
entire higher education system in India: these include poor mentoring system, poor
learning outcomes, lack of interactive pedagogy, ineffective remedial instructions,
and lack of training, and many more. The faculty members are not able to spare much
time to mentor every student. Hence, the young generation is losing their interest in
studies; it becomes really difficult to attract their attention and keep them engaged,
especially in developing concepts. Another challenge the industry is encountering is
poor learning outcomes. The average score of students of higher education is below
50%, which compels the system to put regress focus on the improvement of the qual-
ity of education.
A low retention rate is a big problem of colleges and universities in India, the
major cause being its quality of education.
Because of its multidimensional sociocultural facets, India usually has
heterogeneous groups of students; the classroom has students from diversified
ba ckgrounds, which is the biggest challenge among teachers and also one of the
causes of weak learning mechanisms among students. The pedagogies used by fac-
ulty members are not effective accordingly and there is a lack of interaction among
teachers and students. Most of the teaching is done in India with the help of text-
books and it is highly routine based, which makes the classrooms very boring and
non-interesting.
Another problem in the system is the lack of training/refresher courses for faculty
members for the usage of advanced technologies. The teaching staff is not trained
enough in using AI tools as their teaching pedagogies. Even the training so provided
is not of much benefit as it is not requirement based. A recent survey found that the
level of adoption of technology in education is mostly limited to the use of computers
only, other technological teaching tools are lacking like audio/video display, ERP
software, and many others.
The education quality of most of the institutions is very poor, not acceptable.
There are very few institutions that have global recognition, such as IIMs (Indian
Institute of Management) and IITs (Indian Institute of Technology), but these are
very less in number. Singh (2011) mentions that universities and colleges offer very
limited course options to the young generation for a skill-based career. Hence, it is
observed that due to lack of skilled-based educators, the result is not so attractive and
only 25% of the professional graduates get direct placements. Most of the educators
are not trained to use advanced technology for applying it in their teaching pedagogy.
This is making the education system laggard.
From the above discussion, it is quite clear that there are still many obstacles
in the Indian education system; to improve its functioning, the policymakers, aca-
demicians, and government should ponder on these issues and implement various
advanced technologies for the uplift of this sector for quality education as students
are the future nation-builders.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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16 Transforming Management Using Artificial Intelligence Techniques
2.6 VARIOUS AI TOOLS USED IN THE EDUCATION SYSTEM
The education sector broadly uses two categories of narrow AI applications.
The first category is rule-based applications to support instructional and admin-
istrative systems, and the second category is machine-based learning (Jalota and
Agrawal, 2019) to score student essays and provide feedback in an automated
manner.
Rule-based expert system was first developed and used in the U.S. defense in the
1950s to enhance the decision-making ability of human beings (Brynjolfsson and
Mitchell, 2017). Its commercial utility began in the 1970s to provide multifacilities
to many industries like oil-drilling operations, maintenance management for heavy
machinery, technical support for service technicians, and assessing personal credit
risk. Intelligent Tutoring Systems (ITS) is the most common and one of the initial
examples of this system.
The report of DFKI (2015) mentions that ITS is being used in the education sec-
tor since 1980 to provide instructions to the students as a human tutor. ITS is getting
more popular nowadays and is being widely applied by top learning platforms such
as Achieve3000, STMath, Dreambox Learning, Mathia, ALEKS, and many more.
ITS provides concept-based learning for an individual with a monitoring system and
self-pacing and it adapts itself as per the knowledge level of every individual; in addi-
tion, the application improves with the continuous feedback of students by taking
their demonstration of learned content and domain.
Similarly, machine-based learning is a technical aspect of AI tools that uses sta-
tistical algorithms to make a prediction model by analyzing large data. In India,
the most recent example of using a machine learning app is the one adopted by the
education system of Andhra Pradesh to understand the learning experience of that
state. Machine learning methods are widely used as warning mechanisms in higher
education for those students who might not be able to complete their higher education
on time. It is also able to predict about those students who might need some extra
effort to complete their studies in time.
Gleason and Dynarski (2002) have mentioned that the main function of these
applications is to find out the relationship between various kinds of data like stu-
dents’ attendance and credits earned, students’ attendance and their health record,
and the like. The most appropriate and accepted machine learning tool across the
world is Google’s “deep learning”.
AI can bring advancement in the education sector by making changes in the
existing teaching pedagogies and by giving smart solutions to the administrative
tasks. Various technologies have been used in schools, colleges, institutions, and
u niversities to keep digital the records of students. However, implementation of
AI must be preceded by efforts to digitize records of teacher performance, student
performance, and curriculum. Several AI tools are being successfully used in other
parts of the world, and they can be adapted to the Indian context to target specific
challenges. The AI tools being used in the education system are broadly divided into
four categories:
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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17AI in the Education Sector
a. AI tools for students affairs
b. AI tools for learning and instructions
c. AI tools for administrative efficiency
d. AI tools for students acquisition
Let us discuss these four categories of AI tools being used in the education system.
2.6.1 AI TOOLS FOR STUDENTS AFFAIRS
With the help of AI tools, students can get a degree without physical presence to any
university or college. A study conducted by Klutka et al. (2018) projected that, with
the help of AI tools, the idea of creating “star faculty” can become a reality. The
researchers explained this concept by laying down that a faculty can teach across
the university with AI tools by only giving some instructions, interventions, and
grade assessments.
To have a long-term association with the students, it is required to provide them
advice in selection of their career plan, build some small connecting committees to
deal with a large group of people, and offer them an appropriate course to make them
skillful and marketable.
The biggest hurdle faced by students today is to select courses that affect the eli-
gibility for future courses in their career path and its impact on career goals. Klutka
et al. (2018) have cited AI tool to resolve this issue by the name “Stella”. It allows
students to form their career plans with the assistance of career templates of former
students and various career-affecting projects.
Student’s remedial classes and additional tutoring can also be provided effectively
with the help of AI tools without putting extra pressure on the faculty members.
One such AI tool that deals with such tutoring and remedial classes is by Carnegie
Learning and is named as “Mika”. Primarily, Mika provides customized feedback
to students about their performance in maths and then categorizes their needs in a
precise way that supports faculty in identifying a specific issue in performance and
can provide a tailored solution to the individual student.
Virtual assistance is a boom in the education sector, especially in higher educa-
tion. Many AI tools like Alexa, Siri, and Cortana are getting popular nowadays and
highly adopted by many universities worldwide. Klutka et al. (2018) have expressed
that a fleet of Amazon Alexa Dots are installed across the Saint Louis University
by the management to provide easy access to students-related information (campus
activities, library hours, sports hours, etc.). A few more universities like Arizona
State University and Northeastern University are also experimenting with Alexa on
campus to tackle more personalized requirements.
Popenici and Kerr (2017) have mentioned “Watson” as a supercomputer devel-
oped by IBM, which is getting very popular among many universities across the
globe because it can provide advice to students at any time of day during the year.
Watson is very helpful in administrative tasks, which positively can improve the
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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18 Transforming Management Using Artificial Intelligence Techniques
quality of services and also increase productivity. This tool is being used by the
Deakin University of Australia. Agrawal and Gupta (2017) also reviewed various
tools which are widely used in Educational data mining. These tools can be utilized
to enhance the teaching quality.
2.6.2 AI TOOLS FOR LEARNING AND INSTRUCTIONS
AI provides many technological innovations for teachers by providing content devel-
opers having better classroom experience. AI tools can assist teachers in managing
the multigraded classrooms by judging the levels, contingents, and psychology of
individual students and by developing customized content as per the requirement of
the students. Assessment of students can be taken very easily and more appropriately
with the help of technologies. A smart tutoring structure can do a lot in increas-
ing students’ proficiency level, learning speed, and style. To seek their attention and
make the system more interactive, some quizzes, pop-up questions can be tailored as
per the knowledge level.
Some applications help in knowing the level of attention of the learners toward the
studies and also provide various remedial instructions. AI tools can provide various
professional pieces of training to fill the gap between knowledge and skills. To deal
with a large group of students, a new Chatbot is developed by Georgia Tech in 2016
(Borge 2016). It provides administrative affairs and deals with many queries about
lectures, content delivery, and assignments.
Dodgson and Gann (2017) have suggested that universities such as the Technical
University of Berlin have been using Chatbots as intelligent agents to give answers to
the queries of the students about the understanding of their using course content. The
researchers also exemplified one more AI tool that has been used by Carnegie Mellon
University for long. The name of the tool is the Open Learning Initiative (OLI) that
supports the universities and education sector in comparing learning outcomes for
students.
Klutka et al. (2018) have highlighted that the University of Michigan has devel-
oped an “E-Coach” program to collect feedback of the students, especially in the
STEM (science, technology, engineering, and maths) field. The application helps in
tracking the students’ progress during class and also provides basic instructions to
avoid some common mistakes.
AI provides various appropriate tools that give advice, solve problems, and provide
additional tutoring, skill training, and certification. Klutka et al. (2018) have men-
tioned that the most time-consuming tasks for the teachers are the assessment of stu-
dent’s work and providing timely feedback. To overcome this problem, the University
of Michigan has developed one most user-friendly application named M-Write. This
application is efficient to tackle all writing activities, grades, and individual and class
performance. In other words, one can infer that it enables comparative data analysis
at a dashboard to make a review and take necessary corrective measures by faculties
at the right time.
An another AI tool named “Peerceptiv”, developed by the University of Pittsburgh,
also provides a double-blinded peer-review structure to evaluate long essays and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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19AI in the Education Sector
projects within the evaluation criteria settled by the examiner. AI tools can analyze
results and other performance records of the students in a very efficient manner,
which supports the system to take preventive actions.
2.6.3 AI TOOLS FOR ADMINISTRATIVE EFFICIENCY
Data collection has increased exponentially over the last decade; the credit for this
transformation goes to technology and IT systems on campuses across the country.
There is a problem that such data are collected and saved at multiple systems and
are controlled by different parts of the campus. Many times during the time of need,
such information cannot be communicated or harnessed in a meaningful manner.
For example, an institution can help students for library visits in one system, access
their grades in the second one, and make them appreciate to interact with learning
management system in a third one; but the data cannot be easily exported in a way
that draws right conclusions. This problem is very much solved with the help of AI.
AI facilitates creating a more connected campus because it helps in drawing mul-
tiple data systems, assisting students and administrators leading to administrative
efficiency. A college/university can also be benefitted by using AI as data analytics.
Information can be pulled from various online/offline resources and the data can be
utilized to take many administrative decisions related to, for example, transporta-
tions, resources management, and industry requirements. University can update its
curriculum as per the demand of local hiring needs.
For attaining administrative efficiency, AI tools are applied in the field for
planning for future course offerings and the ability to create hyper-learning abil-
ity. Planning for future course offerings means making an effort to understand the
requirements of final-year students or the pass-out students who are waiting to get
admission in certain courses in their major. It is to identify their needs and make
offers accordingly to them so that they are retained. This requires effective and
efficient AI tools; for example, Stella is an AI application that helps with students’
degree planning. This tool helps the administrators to determine which courses
to offer in the future, when to offer, and the number of sections required to meet
the demand. Further, this makes administrators calculate budget accordingly for
staffing and course development.
The ability to develop hyper-learning ability means to connect diverse students
and administrative systems together, by creating a process in which institutions can
make use of data to make complex decisions. For example, if student support service
could be used to connect with course calendars, which course requires add-on tutors?
Such data can help the administrators to make concrete decisions and necessary
improvements. One such case is the Deakin University of Australia, which has cre-
ated an AI tool by the name Genie. Genie is a proactive agent that can access multiple
databases, answer student’s questions, and coach them throughout their programs.
This system records when students have accessed course materials and libraries and
even track their locations to access how long they have been studying in one place. It
learns and answers student’s queries by accessing multiple campus databases, which
are growing daily as data points are added.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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20 Transforming Management Using Artificial Intelligence Techniques
2.6.4 AI TOOLS FOR STUDENTS ACQUISITION
AI tools are also used by colleges and universities for student acquisition. With
its help, the university can define the phrase “ideal student”, and AI tools help in
acquiring such students. The growth of marketing automation and predictive analyt-
ics helps the universities in selecting the best candidates. Prospective students also
get customized information and awareness about why such an institution is best for
them. Predictive analytics in education also expedite the process of understanding
the students’ interest at an early age concerning their career path, this may be as early
as elementary school. It helps in targeting and segmenting the prospective students at
a very early stage. At present, the practice of college is to target students in secondary
school. Such institutions can start their recruitment process much sooner.
New ways of fee payment have also emerged which also leads to some additional
cost. Students who take majors are less likely to switch majors and this allows the
application of AI tools to focus more on student outcomes, either by design or focus-
ing more on regulatory roles like job placements and return on investments. They
also focus on programs like income-sharing agreements, employer-funded degree,
and deferred tuition. This helps the institutions to develop the skills of students
according to market needs. This also enhances the quality of students as it adds value
to their profile, attracting the best recruiters not only to hire them but to establish a
relationship with such institutions for hiring future employees in their company.
Dodgson and Gann (2017) exemplify a few AI tools being used in universities to
keep the students engaged and make the curriculum delivery more interactive. The
AI tool Galaxy Zoo allows people to participate in scientific discovery and projects
at a global level. Another example is Massive Open Online Courses (MOOCs) by
which over thousands of people can learn and get skill-based certification to enhance
their employability.
2.7 LIMITATION OF AI
AI is a boom in every sector; it can change the realm of the education sector as well.
However, before implementing AI in this sector, one should also consider its limita-
tions. In other words, there is a need for skilled manpower in using this application;
we cannot be fully dependent on technologies. The story of Microsoft is a real-time
example of the limitations of AI. Microsoft was very confident in the capacity of
its recently developed bot namely “Tay”, but it soon turned into a bigoted, dog-
matic, and hate-spewing account and had to be closed for many hours (Perez, 2016).
Subrahmanyam and Swathi (2018) have highlighted a few challenges in implement-
ing AI tools in Indian education sectors that were observed by the researchers as the
unbiased and clean data required to use data analytical function of AI machines. The
problem found was that the students are not well-trained to handle AI tools. There
is no exact estimation of its ROI, no one can predict its returns perfectly. There is no
doubt that AI is a costly affair, as it incurs a heavy cost of installation, maintenance,
and repair. Only financially sound universities and colleges are taking benefits from
it. It should be incorporated across the education system to ascertain its potential.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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21AI in the Education Sector
2.8 CONCLUSION
According to Popenici and Kerr (2017), big giant IT companies like Google, Apple,
Microsoft, and Facebook currently invest a lot in research and new applications of
AI, which is going to affect the pace of the education sector. Thus, now the time has
come up for institutions and universities to have a look upon their curricula, pedago-
gies, and other administrative functions from the perspective of incorporating AI
for attaining efficiency and effectiveness. From the above discussion, it is clear that
AI is going to emerge as a dark horse shortly for the success of any enterprise. The
organization that is going to implement AI in its system is unquestionably going to
conquer its industry by leaps and bounds; it will have the potential to emerge as a
market leader if it uses AI cautiously and effectively. AI will do good not only to the
organization or industry but also to the society at large by eventually accelerating the
economy of a nation.
Hence, AI has unleashing potential across industries, particularly in the education
sector for institutional efficiency. The need of the hour is that the academicians,
policymakers, and the government take necessary action to incorporate AI for better
utilization of resources for the evolution of the human race.
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Singh, J. D. (2011). Higher education in India–Issues, challenges and suggestions. Higher Education, 1, 93–103.
Subrahmanyam, V. V., & Swathi, K. (2018). Artificial intelligence and its implications in edu-cation teaching and learning in higher education. Research and Practice in Technology.
Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Policies for the future, available at: http://publications.jrc.ec.europa.eu/repository/bit-stream/JRC113226/jrc113226_jrcb4_the_impact_of_artificial_intelligence_on_learn-ing_final_2.pdf.
Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66.
Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5(2), 164–172.
WEB LINKS
https://www.forbes.com/sites/cognitiveworld/2019/07/12/ai-applications-in-education/ #3e46f81d62a3
https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-educationhttps://www.indiatoday.in/education-today/featurephilia/story/6-ways-to-tackle-with-current-
technical-education-system-problems-1281082-2018-07-09https://www.blog.epravesh.com/artificial-intelligence-ai-in-indian-classrooms-a-need-of-the-
hour/https://49hk843qjpwu3gfmw73ngy1k-wpengine.netdna-ssl.com/wpcontent/uploads/2018/11/201811-AI-in-Higher-Education-TLH.pdfhttps://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdfhttps://www.weforum.org/agenda/2017/08/artificial-intelligence-will-transform-universities-
here-s-how/https://www.vaughn.edu/wp-content/uploads/2018/10/Artificial-Intelligence-and-Chatbots-
in-Higher-Education.pdffile:///C:/Documents%20and%20Settings/Admin/My%20Documents/Downloads/https---
bernardmarr_com-pdf_asp-contentID=1541.pdfhttp://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-
Discussion-Paper.pdf.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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23
3 Reinventing HR in the Era of Artificial Intelligence
Teena SaharanDoon Business School
3.1 INTRODUCTION
Today, than ever before, companies are facing greater threats in terms of competi-
tion, operations, and innovations. And in the present time, companies look to human
resources (HR) professionals to provide strategic solutions to resolve these problems,
that too at the speed of light with minimum costs in a pressured climate. Apart from
this, HR has to confront many other issues that make their work more challenging
and difficult: low employability skills, scarcity of specialized people, competition for
bringing intelligent and top talent, the pressure to minimize costs and time to hire,
the requirement of continuous skill upgradation, providing a personalized experience
to employees, connecting with candidates, hiring a diverse workforce, cultivating
inclusiveness and bias-free environment, need of retaining top talent and reducing
overhead costs, offering customized, innovative, and competitive compensation, and
flexible work environment, to name but a few. The list is quite exhaustive and it is a
must for HR to incorporate AI in every aspect related to employees.
Most companies are not able to concentrate on major issues and are unable to cater
to the need of the diversified workforce, inject creative and critical talent into system,
CONTENTS
3.1 Introduction 23 ....................................................................................................
3.2 What Is AI 24......................................................................................................
3.3 Importance of AI in HR 26.................................................................................
3.3.1 Robotic Process Automation 27 ..............................................................
3.3.2 AI and Recruitment 28 ............................................................................
3.3.3 AI and Workforce Planning 30 ................................................................
3.3.4 AI and Brand Building 31 .......................................................................
3.3.5 AI and Compensation Management 33 ...................................................
3.3.6 AI in Performance Management 35 ........................................................
3.3.7 AI and Learning and Development 38 ....................................................
3.3.8 AI and Employee Engagement 42...........................................................
3.3.9 AI and Employee Relationship Management 45.....................................
3.4 Conclusion 46 ......................................................................................................
Bibliography 47 ............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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24 Transforming Management Using Artificial Intelligence Techniques
locate people-related deadlocks and resolve them, create an attractive employer
brand, and synchronize all functional activities with HR for colossal growth of the
organization. In such a scenario, machine learning, popularly known as artificial
intelligence (AI), is available and capable of reducing the HR burden by handling
their important but monotonous and repetitive tasks. AI is capable of improving the
functions by bringing best into the system with the use of metrics, data analytics,
and machine learning. It is capable of reducing recruitment errors and costs by iden-
tifying key skills required to better perform a job and identifying it on the available
market database. AI can easily assess the developmental needs of an employee by
analyzing the performance datasheet of the candidate. Not only this, it can also bet-
ter suggest the apt module for removing the skill gap and improving the overall effi-
ciency and effectiveness. AI can regularly monitor the performance of the employees
and better appraise their contribution to the organization by minimizing the impact
of behavioral errors and human biases.
Human resource management (HRM) is an integral part of every organization
handling people, and its dimensions are not limited to talent acquisition and
development. HR is continuously evolving and increasing its horizon and projecting
its performance with the use of advanced analytics, AI, and cloud computing.
Many companies such as Google, Oracle, Sysco, and Best Buy have understood the
met hodology of ensuring improved employee engagement, critical and top talent
retention, and finally indemnifying high productivity. These companies can repli-
cate their success by ensuring better performance from their top employees with
the use of predictive analytics instead of gut instincts. These organizations have
kept synchronization between their organizational strategic objectives in tune with
key talent requirements, development and improvement of skill set, due recogni-
tion to performing talent, and enabling a system to identify and retail critical and
performing talent.
HR executives believe that merging of HR administrative functions and machine
intelligence will enhance employee performance and experience. The focus of the
chapter is to provide a better understanding of the current status of HR functions and
the challenges it is facing in the 21st century. This chapter will further focus on the
role of AI in improvising HR functions. It will present how people, processes, and
AI can be combined to bring transformational value within the organization at an
optimized cost. This chapter will also present the ways to integrate technology in HR
functions and reap maximum benefits out of it.
3.2 WHAT IS AI
The ability to perform intelligent tasks by computers, systems, robots is usually
associated with human beings. The machine can learn, adjust, and perform human-like
tasks. This is possible due to deep learning, learning through experiences, natural
language processing, processing of a large amount of data, adjusting to new inputs,
and recognizing patterns in the available data. Due to this process, companies can
launch self-driving cars and chess-playing robots.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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25Reinventing HR in Era of AI
The term was first coined in 1956, but it became popular in the 21st century and
over the last 4 or 5 years, it has started becoming the lifeline of many businesses due
to large data availability, advanced algorithms, and improved capacity of storage
and computing powers. Defense Advanced Research Project Agency produced the
intelligent personnel assistant much before Alexa, Cortena, or Siri in the year 2003.
AI is evolving to provide multiple benefits to every individual now. According to
John McCarthy,
every aspect of learning on any other feature of intelligence can in principle be so pre-
cisely described that a machine can be made to stimulate it. An attempt will be made
to find how to make machines use language, formal abstractions, concepts and solve
kinds of problems which are now reserved for humans and improve them.
AI is evolving to provide multiple benefits to every industry. Now it has not only
paved the way for formal reasoning and automation, but it has also designed smart
screen systems and intelligent decision support systems to augment and complement
the human abilities. AI systems are developed to train machines that can perform
human tasks. They are divided into properties like a human.
According to Stuart Russell and Peter Norvig in their book Artificial Intelligence: A Modern Approach, there are four approaches that have defined the field of AI:
i. Thinking humanly
ii. Thinking rationally
iii. Acting humanly
iv. Acting rationally
The first two approaches talk about reasoning and thought processes, while the rest
two focuses on behavior. The Ford Professor, Patrick Wintson at MIT, defined AI as
‘algorithms enabled by constraints, exposed by representations that support models
targeted at loops that tie thinking, perception and action together’. At the Japan AI
Experience in 2017, Jeremy Achin, CEO Data Robot defined AI as follows:
a computer system able to perform tasks that ordinarily require human intelligence.
Many of these artificial intelligence systems are powered by machine learning, some
of them are powered by deep learning and some of them are powered by very boring
things like rules.
AI is broadly categorized into two forms:
i. Narrow AI
ii. Artificial General Intelligence (AGI)
Organizations and people are surrounded by narrow AI. Narrow AI is a simulation
of human intelligence and operates in a limited context, whereas AGI is much like
the general intelligence of human beings that applies the intelligence to solve various
problems. Google Search, Alexa/Siri, Image recognition software, self-driving cars,
and IBM’s Watson are a few examples of narrow AI.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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26 Transforming Management Using Artificial Intelligence Techniques
AI has major subfields such as cognitive computing, computer vision, machine
learning, neural networks, deep learning, and natural language processing. The use
of AI has many benefits as it automates repetitive learning, adds intelligence to exist-
ing products such as Siri, adapts through progressive learning algorithms, able to
analyze much deeper data, provides incredible accuracy from deep learning, and can
get the most out of the data to create competitive advantage.
Where AI has bought tremendous changes in all aspects of business in the last
25 years, it has created a seismic shift in HR also. It has helped richer profession-
als in data-driven decision-making, save time and money, and increase employee
engagement.
3.3 IMPORTANCE OF AI IN HR
AI provides support to HR professionals by automating repetitive and low-value
work so that the HR team can focus on human interactions and strategic issues by
crunching and extracting meaningful information from large data sets. AI solutions
provide insights for better decision-making to handle important but repetitive and
time-consuming tasks, HR professionals are using AI. Simultaneously, to provide
a more personalized touch and another crucial edge, AI is helping HR to become a
strategic partner by providing them the information advantage.
Even though it’s observed through studies and surveys that many employees find
themselves to be less known to use AI efficiently at their workplace, AI is after all
not as complicated as it seems to be. One of the simplest forms of AI’s implication in
the HR domain is the introduction of the chatbox. Let’s imagine employees working
on their desks and still being able to communicate with their managers and team with
the help of the chatbox interface. It makes communication simpler as well as helps
save time to be implied in carrying out long-hour meetings.
Talking even in more HR terms, many employees might feel that they aren’t being
heard well from their managers, are hesitant to communicate their problems and
expectations clearly and efficiently to their managers, etc.; in such scenarios, when
an AI element like chatbox comes into the picture, all the data inputted by the staff
are stored, recorded, and analyzed further into useful information, all by a machine
itself – that too in no time. This ultimately also helps the managers to understand
and analyze their staff well and nothing is left out; it helps or guides the managers in
decision-making too.
This was just a glimpse of how such a small interface of AI can help improvise
the work culture through optimizing HR functions in an organization (Pathak and
Agrawal (2019). Going further in depth of AI’s implications, AI plays a major role in
the following fields:
i. Recruitment using AI
ii. Workforce planning
iii. Robotic process automation (RPA)
iv. Performance management
v. Compensation management
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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27Reinventing HR in Era of AI
vi. Learning, and development of brand building
3.3.1 ROBOTIC PROCESS AUTOMATION
RPA programs and software can help not only to reduce tedious tasks and speed up
the process of doing business and completing projects but also to free up employees’
time and energy, allowing them to think more strategically and creatively. HR
considers multiple factors while using robotics in process automation. The first step
is to decide which process is to be automated. Deciding the process to be auto-
mated will help the HR professionals to choose RPA programs and engage their
success down the line. A sharp hike can be predicted in productivity and a smoother
organizational flow can be expected with the use of RPA processes. Most of the
activities in the HR department are repetitive and errors often derail the streamlin-
ing of departmental activities. This adversely affects payroll management, talent
management, and employee onboarding. RPA provides a fully functional virtual
workforce to an organization that handles these tasks with greater efficiency and
accuracy. According to Deloitte Global Human Capital Trends 2017 study, approxi-
mately 50% of leading HR firms’ executives believed that RPA can reduce the cost
of the HR functions by 15%–20%. RPA can be utilized to automate the processes
that are repetitive, rule-based, prone to error, time-critical and seasonal, and involve
digital data.
Deloitte has identified many HR processes that can be automated. They identified
process categories – strategic, talent management, total reward, and HR operations.
Under the strategic process, managing workforce planning is easy for RPA, whereas
establishing and implementing HR programs and policies, managing employee sat-
isfaction, and managing organizational designs still need more human interactions
than automation. In talent management, RPA manages recruitment, hiring, onboard-
ing, and integration to a greater extent and employee training and development, man-
aging employee performance and competency to a certain extent.
More human touch is required in the area of global employment and managing
career and succession planning of employees. Robotics application can be completely
utilized in the total reward process and is completely efficient in handling compensa-
tion and managing benefits. In the area of HR operations, robotic applications can
perform employee data administration and manage positions, reporting, payrolls, and
time booking along with employee health, safety, and separation processes. However,
employee and labor relations are still in a nascent phase for involvement of robotics
applications.
Human Resource Software Buyer Report highlighted many benefits of RPA
in HR. According to the report, RPA can streamline many HR processes such
as recruitment, onboarding, compliance management, compensation manage-
ment, retention, retrenchment, and data management. Simultaneously, it frees HR
professionals to focus more on value-added activities, improving communication
for better and corrective hiring and supporting the organizations in developing
culture.
viii. Employee engagement
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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28 Transforming Management Using Artificial Intelligence Techniques
RPA is capable of performing all human actions that are rule-driven. It is software
bots that perform with higher speed and greater accuracy and is best suited for low
complexity and high-volume tasks such as payroll administration, benefits enroll-
ment, and compliance. According to Forrester’s research, one board is capable of
performing the task of three to four full-time employees in such work.
3.3.2 AI AND RECRUITMENT
Companies have started using AI systems into recruitment; with the help of AI solu-
tions, HR professionals are trying to automate the recruitment processes and discover
new approaches to hiring talent. AI helps companies to simplify the complicated and
time-consuming task by providing virtual assistants. AI allows recruiters to fill uni-
fied profiles from the largely unorganized and unstructured data, matches the skills
required with the position, and provides output sheet to HR at a lightning speed.
Although the capability of AI is threatening the recruiters, they need to understand
that it is not here to replace them; instead, it will enable them to accelerate the hir-
ing process so that recruiters can spend their valuable time in building relationships
and developing problem-solving capabilities. It is time-saving, capable of automat-
ing high-volume tasks, and improve quality. However, many types of research have
claimed that AI is capable of learning human biases in recruitment and it requires
a lot of data to be useful. Simultaneously, the organization needs to overcome the
employee skepticism of adopting new technology.
For lower profiles in the organization, AI is providing a helping hand to resolve
issues related to human capital management. Due to innovations in AI, available
solutions can mine data and the use of standardized job matching can provide the
best results in the form of screening screened candidates for recruiters. Further,
recruiters can interview and evaluate the candidates, convince them, and negoti-
ate the prospect to bring them on the same platform. AI enables chat boards to ask
questions and resolve queries of candidates so that recruiters can focus on build-
ing relationships instead of resolving regular queries. However, one of the greatest
challenges is that AI is still unable to handle difficult and complex conversations
that are required to help people at middle and senior levels with greater human
interventions.
By automating all the tasks, AI reduces the load of recruiters with which they can
save their time for other important business activities. AI also reduces the chances
of errors such as misplacing of resumes in case of job posting attracting numerous
applications. AI helps in sorting them and finding the relevant ones as per the job pro-
file. It reduces the chances of bad hires, which usually occur due to manual errors. In
addition, manual resume processing becomes difficult in case of urgent hiring where
recruiters have to fill the vacancy in the stipulated period. According to the Harvard Business Review, 96% of the senior HR professionals trust that AI has the potential to
improve the experience of talent acquisition and employee retention. AI is capable of
establishing the roles that a successful employee performs being in a specific position
and look for the candidates matching the defined profiles. It’s capable of identifying
the top performers for an organization by assessing the available data based on their
qualifications, experience, and other factors.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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29Reinventing HR in Era of AI
The CEO of AI firm Skymind, Chris Nicholson told CIO the importance of AI:
the smartest recruiters and hiring managers would start gathering resumes, perfor-
mance reviews, work products, any information at all about highly successful people
that already worked for them and plug that into an algorithm to figure out what you are
looking for.
In talent acquisition, AI can improve the communication between candidates and
recruiters, thus enhancing the candidate engagement. It can provide candidate guid-
ance, update, and feedbacks and resolve their queries in real time. According to the
talent board North American Candidate Experience Research Report, the regular
communication with the candidate has a significant impact on their experience. For
Mya, an AI system developed for recruitment has averaged 9.8 out of 10 and overall
candidate experience.
AI also helps companies in the onboarding of new joiners as it is capable of
providing all relevant information to the candidate that can make their work easier.
This offers a resource to new employees to which they can turn while having queries.
These queries may vary from ‘wherein the conference room’ to ‘sign up for the
health insurance’. It enhances and enriches new employee experience by providing
relevant information which they are looking for, instead of dropping males to HR
and then waiting for their replies. It reduces confusion to a larger extent. That is how
AI in recruitment saves the time of HR people, which they can spend in solving the
complicated organizational issues where creativity and human emotions are required.
With the use of online application management, AI solutions are helping com-
panies in talent acquisition. It tends to use many keywords, workflows, and related
data points to help recruiters prioritize and scrutinize important resume from a pile
of resumes. This helps speed up the selection process of the candidates. AI can also
help companies to make job profiles more effective using analytics. According to the
forbes.com, ‘AI can search to find matching candidates, connect with them, conduct
the preliminary interviews, access performance, and present the best candidate for
further interviews by recruiters’.
AI can provide many benefits to recruiters, some of them are as follows:
i. Better quality hiring: With the use of unique algorithms, more information
can be collected for each candidate to assess their skills and experience.
ii. Bias-free: It removes partiality and biases from the recruitment procedures
and screen candidates only based on their qualifications, experience, and
skill sets.
iii. Time-saving: AI takes only seconds to provide acceptable results by analyz-
ing a significant amount of data.
iv. Better candidate assessment: AI can help companies predict the stability of
the candidate by knowing about their job changing patterns with the use of
predictive analytics. AI significantly improves the experience of recruiters
by assessing the right candidate and promoting smart recruitment systems.
AI is helping to improve writing for preparing JDs, assessing the online applica-
tion, speeding up a shortlisting of applicants, providing a level playing field,
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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30 Transforming Management Using Artificial Intelligence Techniques
finding the best-fit candidates, conducting interviews, and providing advanced data
analytics and recruitment automation for choosing the best candidate for the business
where Mya and Texito are a few examples.
3.3.3 AI AND WORKFORCE PLANNING
Workforce planning is a process to ensure that organization has the right set of people
at the right time and right job. To fulfill the organizational mandate and its strategic
objectives, workforce planning includes analyzing, forecasting, and determining the
demand and supply of the workforce to fulfill the talent gap. Workforce planning has
different phases, including strategic direction, supply analysis, demand analysis, gap
analysis, and solution implementation and monitoring process.
To have a competitive advantage, organizations are intensifying their efforts in
accurate workforce planning and the current agile environment, Traditional SWP
(strategic workforce planning) is unacceptable. The traditional SWPs are very slow,
inaccurate, and quite complicated. The solution provided by humans does not pro-
vide justice to the complexity related to workforce planning, and AI is an answer
to most of the recurring problems. The application of AI can eliminate the errors
committed due to guesswork of managers by making employment-related decisions
along with the proper workforce management by effective utilization of available
talent pool.
Effective management of employees requires detailed analysis and understanding
of people’s needs, behavior, and skills. Managers can handle it to a certain extent
while working with smaller teams; however, it becomes difficult for managers to
keep track of all employees when the company grows. Decision-making becomes
difficult and a vast amount of data are to be collected, updated, analyzed, and
maintained to drive correct information. AI enables managers to focus on effective
decision-making and the rest of the tasks related to data gathering. Analysis and
making inferences are automated with the use of AI.
Workforce optimization using AI is gaining importance in the business world as
organizations are becoming more dynamic and cost-conscious. AI is providing the
solution to companies which they are looking for, helps in assigning the right people
the right task at right time and right place to provide better employee experience
and a high level of job satisfaction. Workforce planning is divided into the following
categories:
i. Staff rostering
ii. Job assignments
Staff rostering includes preference fulfillment, auto-scheduling, auto-assignment,
schedule, change, and charges handling. Auto-scheduling and auto-assignment are
about fulfilling the shifts automatically with the required right staff. AI allows to
pre-assign tasks into schedules such as training and auto-generated rosters for each
shift requested matching the availability of staff, their skills, and experience. It also
complies with the legal requirements and latest regulations of the state. AI allows
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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31Reinventing HR in Era of AI
effective collaboration between the organization and its employees by automating the
time-consuming, repetitive, and routine tasks such as forecasting.
AI provides preference fulfillment to enhance employee satisfaction. It considers
the preferences of the people related to day-off, shifts, and many other considerations
such as fulfilling duties and swap requests by identifying the exact match. AI tries
to satisfy employee preferences without disturbing the fairness criteria. AI provides
multiple benefits:
i. Reduce the time spent on scheduling and rescheduling
ii. Efficient assignment of staff improving productivity and overall employee
experience
iii. AI ensures fair and right treatment with all staff by maximizing the prefer-
ence fulfillment
iv. It improves the service quality by identifying adequate staff with the
required experience, skill sets, and potential
v. AI is quite fast in fixing issues quickly and correctly without being anxious
and capable of handling unexpected events successfully.
3.3.4 AI AND BRAND BUILDING
Any individual selects to be part of a company’s recruitment process based on the
perceived image and employer brand. This perception takes further clear shape once
these candidates become part of the process and later on join the organization. As
technology is becoming an imperative tool in the communication between the indi-
vidual and the organization, representing the work and workplace is becoming an
important part in creating and promoting the organization brand. In this dynamic
environment where employees are highly dependent upon technology, AI is present-
ing and providing itself a flexible, viable, and vibrant tool to help and assist organiza-
tions in sharing their brand stories cohesively.
HR technologists provide three stages to enable the implementation of employer
branding strategies using AI to enhance employee experience:
i. Connecting with the right talent using AI: According to the chief prod-
uct officer of monster.com, Chris Cho, AI can analyze the behavior of job
seekers to synthesize a better understanding of their intentions: low intent,
actively searching or rigorously applying. With this information, recruit-
ers can match job seekers with prioritized and relevant branding contents
of employers to maximize outcomes for both – job seekers and employers
with adequate chatbots are better able to show their presence and create a
strong employer brand. These chatbots can clarify all the doubts and answer
all the queries of candidates and provide relevant information about the
organization and job profile. This intelligent conversation and regular com-
munication improve the individuals’ experience and help the organization
in acquiring the best talent. Google, Microsoft, Alibaba, Amazon, Apple,
Facebook, Tencent are leveraging AI to enhance brand management.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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32 Transforming Management Using Artificial Intelligence Techniques
According to the survey conducted by Growth Everywhere, organizations
with great company culture have a major employee and a meager employee
turnover rate of 13.9%, whereas the probability of employee turnover is
48.4% in low company cultures.
ii. Rethinking about employee engagement: AI brings personalization in
pro viding services that help HR teams to focus more on relationship-
building, eventually leading to brand activation and employee engagement.
Organizations are better able to predict trends and have better access to
people’s behavior with AI enabling to better align with the workforce, work,
and workplace riddle. The employer brand is defined by the functioning
of the organization daily. Employee engagement and employer brand are
highly co-related. AI enables organizations to provide its people a platform
to connect, communicate, and collaborate well in real time with the use of
digital tools, removing the barriers of distance, diversity, and time zones.
It enhances the overall work experience of the employees in general and
supports organization builds the brand that it has promised to self and
employees. Organizations are better able to engage employees with the
use of deep data, machine learning, neuro-linguistic language, and feeling
personalization in their everyday experience.
iii. Employee retention: AI can enable many processes that can enhance
employer branding and employee experience simultaneously. It can make
performance appraisals unbiased and ethical, can make feedbacks more
productive and fulfilling, and suggest relevant rewards and recognitions.
This is possible with the introduction of conversational and collaborative AI
productivity and by proactively deploying the AI strategic initiatives. Today,
employee retention is very challenging due to multiple options available in
the market. With the right AI solutions, an employer can introduce brands
that employees can relate to, enjoy working with, and want to stay with.
Building an agile brand assure employees that the organization is capable
of keeping pace with the change, ready to transform, and is positioned well
to lead change. This develops a sense of pride among employees and the
boost about the bigger picture with which they are working. AI can become
an important pillar in employer branding strategies other than its policies,
procedures, and promotions.
Today, the employees too are bored of the same boring stereotyped working patterns.
They look forward to finding some interesting tasks and activities and methods to
get their work done. In such scenarios, we see the huge impact that AI can make in
the organizational culture. Cloud-based and mobile platforms for communication
have made the work–life balance of employees to be easier and less hectic. Many
of the companies have already been using their email platforms to communicate
with their employees, which makes deployment of information a lot more structured;
companies also have introduced mobile phone applications with a basic user inter-
face, which helps employees go through their work easily as well as in an organized
manner even when they are not in their office. This also helps the work get done a bit
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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33Reinventing HR in Era of AI
quicker. The usage of chatbox has already proven to be disruptive in all the industries
undoubtedly.
AI can also be used to create software for new joining employees to make their
onboarding easier; they can be guided through all the departments, tasks, and func-
tions of the organization. This shall make it easier for the employee too to blend well
in the new environment and will also save time and efforts consumed and invested
by the organization in the process of onboarding. Also, the records and track of it too
can be kept well by the managers.
Chatboxes cannot just be used as a good communication tool but can also work
as reminders to the employers. The chatboxes can pop up with the to-do list of the
managers, which will help them stay updated with their tasks and reduce the amount
of left-out work as well as avoid omission of important tasks.
3.3.5 AI AND COMPENSATION MANAGEMENT
Compensation and benefits administration is an important function of any organiza-
tion. To keep the management of payroll on track is quite complex as it is a highly
data-based process. However, AI is disrupting the old way of payroll management
by handling it efficiently and keeping no space for the errors occurs due to human
involvement and their efforts. AI has come up as a boon for the organizations that are
struggling with payroll management as it provides absolute accuracy, timeliness, and
orderliness. Companies can optimize mundane activities like salary calculations and
record-keeping of employees with the use of AI. AI can disrupt the traditional payroll
administration, including payroll processing. AI is efficient to handle a large amount
of data in daily routine by keeping effective coordination with other related aspects
of human resources. Into the traditional system, HR people are always on their toes.
Due to the unavailability of a proper and documented compensation management
system, lots of issues and problems arise due to mismanagement of process.
To date, most of the company professionals are facing trouble in tracking the
actual employee expenses. Many tech-based global firms have started understanding
the process related to the present conventional payroll management system and have
started introducing AI systems. Although these AI technologies are quite costly ini-
tially, in the long run, these technologies can handle multiple critical issues without
human interventions. A proper explanation is provided of how AI is handling and
streamlining compensation management along with resolving burning employees’
payroll related issues.
Payroll administration is assumed to be an isolated function that performs and pro-
cesses the compensation benefits of employees. However, in reality, this is a depart-
ment that receives a large number of queries every day from all levels of employees.
It takes a toll on the majority of their time and creates a burden on the department,
leading to mistakes and errors. The introduction of AI can handle efficiently this
influx of queries without committing any error.
As presented in Figure 3.1, by introducing AI-powered chat bots in the compensa-
tion department, most of the employees’ queries can be swiftly responded without
engaging humans. Chatbots can be prepared and trained to respond or converse like
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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34 Transforming Management Using Artificial Intelligence Techniques
HR people integrated with employees’ self-service portals to enhance interactions
and improve the satisfaction level of people due to accurate and timely response of
chat bots.
With continuous learning and more data gathering, these chatbots can answer
critical and complex queries. These systems are capable of sorting the queries based
on complexity and can direct employees to the concerned HR representative for faster
and easy resolution. This reduces the pressure and workload of HR teams, which they
can utilize in making AI systems more interactive and on strategic business issues.
Payroll management is an inclusive process, highly interrelated with other depart-
mental functions. Due to this, it becomes quite critical to integrate with all the other
functions. It becomes quite difficult for the conventional system to integrate all the
complexity of the interrelationship between functions and overlapping in tasks into
a single solution. With the introduction of AI, organizations and HR teams are bet-
ter able to administer functions with less manpower and minimal errors. AI helps
companies to resolve dissimilarities between functions to promote integration and
reduces the burden on HR people. The payroll claims and reimbursements releases
become quick due to the integration of various functions, reducing employee dissat-
isfaction and enhancing their experience.
AI provides various algorithms to keep a check on employees’ activity and accord-
ingly release the salary and incentive benefits. Most of the organizations have bio-
metric punch-in systems and employees have been found with fraudulent punching
and clocking issues, leading to a loss in productivity, time, and inaccurate calculation
FIGURE 3.1 AI-based payroll administration.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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35Reinventing HR in Era of AI
of compensation. To save companies from loss and such fraudulent practices, AI
with the help of machine learning can enhance the clocking and punching system
enormously. A dedicated algorithm and systems can be developed that can inform
managers and senior people about the presence or absence of the employee on his or
her work. With the help of such systems, organizations can calculate the compensa-
tion based on the real-time presence of employees, making the system more robust
and transparent.
Organizations have amalgamated rosters and payroll management with AI,
which will help managers to make roster management more effective. With the
automation of the system, software is able to check employee performance level,
work pattern, their attendance record, and stress level along with other important
insights for cost and production optimization. Simultaneously, this system can
forecast the staffing requirements by analyzing internal and external environ-
mental factors such as seasonal demand, turnover rate, and upcoming talent
requirements.
AI helps managers to manage the costly and time-consuming affairs of payroll
administration. It can reduce the chances of inefficiency and mistakes when the
organization is large, where management of timesheet, changes in payroll systems,
and leave requests of employees can become quite challenging for conventional
s ystems. Simultaneously, it is easy with AI to incorporate any legislative change
that can be quite frustrating and time-consuming for a human being. Although the
human touch cannot be ignored, AI can better understand the organizational need
and is capable of creating a payroll system that is trustworthy, reliable, and able to
engage people.
3.3.6 AI IN PERFORMANCE MANAGEMENT
Performance management is a process that ensures that the organization can
co mmunicate and connect its vision with the job of employees. It broadly focuses
on two activities: evaluation of employee performance, and assisting employees
to develop their action plans to improve performance. Effective performance
management systems have multiple benefits and positive outcomes such as clarifica-
tion of organizational expectations from employees and setting job responsibilities;
improving the productivity of individual as well as of group; enhancing the employee
capability through regular feedbacks; aligning the employee role and behavior with
the organization’s values and mission statements; transparency in HR decisions and
improving the communication between managers and their teammates.
Multiple performance management tools are available with companies such
as appraisal forms. An effective performance review tool is relevant to the orga-
nizational outcomes that it wants to achieve. Process form should be tailored to
measure and achieve the mission and value of the organization. These review
forms must be inspired by the organizational performance appraisal systems to
which the company wants to follow. Competency-based performance manage-
ment tools are effective in making the connection between employees, knowledge,
behaviors, skills, and abilities and with the vision and mission of the organization.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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36 Transforming Management Using Artificial Intelligence Techniques
The performance of individuals and teams should be evaluated based on the orga-
nizational vision and mission. HR has to ensure the terminology and all aspects
of the appraisal process and make specific reference to competencies, values, and
descriptions of those things. Most important to the organization is to connect the
mission to work.
Every performance appraisal tool should be directly tied to the employee’s job
description. Organizations and managers have to decide upon the elements that they
want to keep in the performance appraisal system. The organizations try their level
best to make forms that can capture all employee performance-related information;
however, despite so many considerations as well as study focus, approximately 75%–
80% of the people are not satisfied with their current performance system. According
to Bryn Hancock, partner with McKinsey, commented that managers and employees
alike see the old annual review approach as too subjective, too bureaucratic, and too
backward-looking. According to Mercer Global Survey of HR leaders, only 2% of
these leaders feel that their current performance management systems are relevant
and quite effective. This shows that the traditional appraisal and performance man-
agement systems are unable to evaluate the actual performance of employees and do
not work anymore. Most of the employees are fed up of this time-consuming tradi-
tional performance method where they have to fill subjective, lengthy, and boring
forms. To add to the frustration, all this happens at the end of the year and by that
time, people and their senior might forget about their performance and contribution
across the year until and unless some systems are maintained that capture all the
data related to employee engagements and performance. This lending question takes
a toll on the employee’s time and when it comes to seniors for review, it becomes dif-
ficult for them to review all the performance and rate them accurately, which leads to
biasness and demotivation. Besides, this traditional performance management (PM)
methods and related processes are quite expensive and do not capture the real-time
data. Due to this, organizations are shifting to AI-based systems, which can resolve
all the primary issues.
Ninety-nine percent of the millennial expect real-time feedback and immediate
appreciation of their efforts and do not want to fill these traditional forms. So the
future of performance management and appraisal lies in real-time feedback, which
is simply impossible for a human being to capture. However, AI using machine
l earning and neural linguistic systems can resolve these issues by capturing
data throughout the day, providing immediate feedback to the individuals, and
appreciating their efforts whether performed individually or in a group. It works in
the following ways:
i. AI intelligently detects the meaningful discussions and conversation of an
individual, captures all the feedbacks and appreciations from the regular
team conversations, and provides real-time feedback. It helps individuals to
rectify problems if occurred. It appreciates the good and productive efforts
of the employees.
ii. AI is able to integrate with all communication tools and programs that
teams use to converse.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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37Reinventing HR in Era of AI
Performance management automation helps the organization to:
i. Streamline performance management and appraisal feedback processes
with the help of chat bots
ii. Automate data collection, processing, and analysis
iii. 360 degree and real-time feedback to employees
iv. Set objectives, track performance, and measure the goal achievement in real
time
In addition, chat bots using AI help organization to:
i. Help recognize teams and individuals’ performance in relevance to the
company’s core values
ii. Keeps a score of all the positive and negative feedback through a leaderboard
iii. Provides suggestions on improvement areas
iv. Give auto-reminders to seniors to appreciate their teammates and provide
timely feedback
v. It provides the facility a quick recap of last week’s activities and perfor-
mance trends
AI-based technology automatically:
i. Connects insight
ii. Finds easy-to-miss patterns
iii. Highlights gap hidden within the data
iv. Provides trends and updates related to improvement in new feedbacks,
engagement level, recognition, and performance risk alerts against past
week’s performance.
Researchers have found that chatbots using AI have enhanced the experience of the
people, teams, seniors, and organizations. It has helped organizations increase the
exchange of appreciation and feedback by four times; a 70% increase in the number
of engaged employees saved 20–22 hours per month per manager. Many organiza-
tions such as General Cable, dun and Bradstreet, Live Health, DBS, and iflix are
using AI-based performance management systems to improve overall performance
management system (PMS) along with enhancing the employee experience. These
chatbots can easily integrate with the applications used by organizations such as
Skype, MS office, telegram, and Microsoft teams.
Performance management is quite challenging and time-consuming in medium
and large organizations. It becomes more difficult for the companies that have dis-
tributed, non-desk-based, and remote workers. With the introduction of AI, employ-
ees can visualize their work objectives and performance in real time, which can
transform their productivity, streamline their manager’s review, and provide business
leaders with new insight into organizational performances. AI helps managers to
consistently monitor the performance of their people in real time and simultaneously
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-08 21:13:06.
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38 Transforming Management Using Artificial Intelligence Techniques
enables the workers to see and track their performance to identify areas where they
need assistance and improvement. Managers can track the performance of their
teams from one place and can update comments and recommend members for
further training and skill development.
AI enables organizations to measure objectives for performance, development,
or culture across the world without limiting boundaries. It provides organizations
with simple systems to align organizational goals with employees’ objectives. It
makes the employee performance system consistent and easy throughout the year.
It provides the flexibility to managers and individuals to review any place and time.
This whole process can be completed in a few minutes, freeing up managers and
teams with more time for personal development. Financial rewards and awards are
easy to trigger, based on metrics analyzing best practices and performance scales.
Companies are increasingly adopting AI for performance management across
different verticals. It provides a platform to companies that are much better, faster,
and smarter in reviewing performance. It has the ability to process a large amount
of information with lightning speed, providing accurate and timely feedback while
eliminating human bias. The AI-based performance review process provides three
major benefits to an organization:
i. Seamless collection of performance-related information from different
sources
ii. Enable managers to extract the right insight from the real-time collated
information
iii. Eliminate all performance reviews related to psychological biases
These systems do not dictate managers. They simply advise and suggest on the basis
of available data. The final call still lies with the managers. According to IBM’s
CHRO Diane Gherson, bosses who follow the AI system’s advice usually get better
results.
3.3.7 AI AND LEARNING AND DEVELOPMENT
Learning and development (L&D) encompass the range of activities that enable
employees to perform their jobs effectively and efficiently now and in the near future.
Learning helps individuals in acquiring both explicit and tacit knowledge related to
the subject matter. Employees get trained through formal and informal programs and
other experiences. Training instills competencies in employees through a systematic
process. It provides an opportunity to acquire the skills, knowledge, and abilities
required to perform their jobs effectively. The motive of developmental activities
is to prepare employees for future responsibilities in addition to their regular job.
Development plans are part of succession planning where individuals are prepared
to get ready for performing roles of higher level. Both training and development
activities help the organization to equip its employees to gain and sustain the com-
petitive advantage. An effective training program results from a systematic process
as follows:
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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39Reinventing HR in Era of AI
i. Training Need Analysis (TNA): Training has tremendous potential to
improve the performance of the employees, but it is often time-cons uming
and costly. To avoid these issues, managers conduct a training needs
a ssessment to understand the impact areas by conducting the training
a ctivities. Training needs assessment identifies the gap between what
employees are expected to be performing versus what they are doing. TNA
is used to fill the gaps between the expected and the actual performance.
The need assessment starts with the organizational analysis. This analysis
is conducted to determine the organization’s performance with reference to
its goals and objectives. Scanning the environment for opportunities and
threats and evaluating strengths and weaknesses internally help an orga-
nization in identifying the training gaps. After conducting organizational
analysis, task analysis is performed to identify the gaps between KSAs
(knowledge, skills, and abilities) required to perform the job to achieve
the organizational objective and the current key result areas (KRAs) of the
employees.
In organizational analyst, the focus is on identifying training gaps across
the company’s workforce, whereas task analysis helps in identifying the
specific training contents needed to close the gap of what employees know,
what they do, and what KRAs they should possess to make a value-added
contribution to the organization. After task analysis, a person analysis is
performed to identify the individuals who need the training. The informa-
tion to make this decision can come from a variety of sources, including
observations, performance appraisals, recommendations by employees,
peers, and mentors.
ii. Training design and development: Training should be designed effectively
to have value. A designer must have a clear understanding of the training
goals at the beginning of the designing process. It helps in ensuring that
the training program meets its results and is oriented toward supporting the
organization’s mission. The training design broadly considers three aspects:
learning objectives, lesson plans, and location of the training. Organizations
and trainers have to understand that different people have different learning
styles. Learning styles affect the employee’s preference for absorbing and
processing new information. Auditory, kinesthetic, and visual are different
approach categories of learners, famously known as the VAK model.
iii. Implementation and facilitation of training program: Different approaches
are used by trainers to make training programs more effective, such as
on-the-job and off-the-job training. Organizations have also designed
many computers-based learning programs to save the cost and time of the
company. These computer-based training programs are so designed that the
trainee can understand the concept and visit it anytime as and when required
in the job. Organizations use many other methods to deliver training, such
as classroom training, which uses discussions, role plays, and other simula-
tion activities along with lectures to enhance the learning experience and
overall effectiveness of the training program. Advances and accessibility of
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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40 Transforming Management Using Artificial Intelligence Techniques
technology have changed training for the better. It uses online, virtual, and
simulation-based learning, which is accessible to all the employees all the
time.
Multiple training methods are available to increase the effectiveness of
the training programs such as experiential learning, on-the-job training,
case study, discussions, demonstrations, lectures, role plays, and activities.
It is the responsibility of the trainer to ensure that methods of training are
appropriate and in accordance with the content and selected participants.
iv. Evaluation and follow-up: Training is incomplete without evaluation and
feedback. Training programs are expensive and time-consuming, so it is
critical to make sure the delivery results. Professionals have identified five
levels of evaluation, which is famously known as Kirkpatrick’s training evo-
lution model: participants’ reaction, learning, behavior, results, and return
on investment to calculate the value of training program.
A survey conducted by Boston Consultancy Group, in 112 countries across 21 indus-
tries, interviewed more than 3,000 business executives, analytics, and managers and
found that training and development didn’t get any place when these organizations
were asked to anticipate the areas that will be affected by AI. However, because
of new technologies, trainers are being challenged to find out new ways to use in
instructional designs. Shifts can be noticed that who leads the training program,
instructor or employee, and where the learning is taking place, from the workplace
to mobile learning.
In the era of AI and to optimize the L&D experience and their outcomes, the
L&D professionals are challenged to stay on the top of the fast and ever-changing
technology to develop methodologies and strategies to remain competitive. The L&D
industry is going to be heavily impacted by AI. Companies can make their training
and development programs better by analyzing the huge amount of data available
with them in the form of training need analysis, training materials, and feedback
shared by the participants.
AI is changing and optimizing the overall learning experience by personalizing
the training content suitable to learner’s needs. It can focus on the weaker portion
of the individual by analyzing past behavior and performance gaps and is capable of
recommending suitable content. The output generated by analyzing these data pro-
vides input to the L&D department to design training programs that enable adaptive
learning and drive value for a person as well as the organization.
In a survey, training and development were rated as the number 1 job attraction by
the millennial. The organization has to understand the demand of the time and start
using AI to train the people by optimizing learning content as per learners’ preferred
style. This will help organizations to improve job performance and retain knowl-
edge along with making the learning experience more enjoyable. AI-based training
could be delivered via written content, video tutorials, or audio presentations and
gamification.
The AI-based training programs can cater to the diversified needs by modifying
the program to suit employees’ needs. Being adaptive, the system can offer video
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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41Reinventing HR in Era of AI
t utorials to some and serve auto-transcription of videos to other employees. AI
enables the creation of videos of text-based articles and suggests the employee’s per-
sonalized training and schedules for the area which they are struggling with. Oracle
study has confirmed that 27% of the corporate HR leaders admitted that AI in orga-
nizational learning will positively impact the training and development and its expe-
rience. AI can reduce the challenges of traditional L&D system, which are as follows:
i. Limited availability of content: Due to PowerPoint presentations, lecturing,
or classroom seminar styles, the traditional system has a limited variety of
training content, which requires freshness.
ii. No scope for personalization: Due to the time-consuming preparation of
training content, the traditional training sessions are not tailored to fit the
individual’s training needs.
iii. Non-user-friendly: Most traditional learning management system (LMS)
solutions are very complicated, which makes their navigation difficult for
employees.
iv. Difficulty in measuring ROI: What is the actual impact of training on the
employees and how much performance has been improved cannot be mea-
sured with the traditional training and development and evaluation systems.
v. Not suitable for the digitized workforce: The people who are away from
their offices and who are always on their computer training, L&D becomes
difficult for them, so AI can provide a solution to this.
AI-based L&D has its benefit and can create smart and intelligent content, which is
adaptive and responsive to the learners’ need and their journey. Some of the benefits
of using AI in training and development are as follows:
i. Online assessment: It is important to evaluate the effectiveness of the
training program and quizzes, assessments, and other forms of tests that are
part of e-learning. e-learning has its limitations as that of traditional training
mechanism such as ‘one size fits all’ approach. With the introduction of
AI-based L&D system, the static assessment formats can be designed adap-
tively to assess the employer’s ability and progress. This will further help
the system to tailor future training content based on the test assessments.
ii. Shorten the learning process: Organizations spend a lot of their precious
time and money on training their employees. AI can dramatically reduce
the length of the overall training by suggesting the required module to
improve employees’ skills. After analyzing the candidate’s strengths and
weaknesses, the AI system can suggest a specific capsule, thus subsequently
improving training effectiveness and reducing time and cost investments.
This will help organizations in enhancing employees’ training experience, motivating
and improving the training completion rate, accessibility to all learners, anywhere
accessibility, designing, training model based on past data, current expectations and
future requirements, and creating digital tutors.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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42 Transforming Management Using Artificial Intelligence Techniques
In today’s environment, it’s important to understand employees first and then
design training programs for them. Obtaining data to understand employee’s behav-
ioral patterns is not a big task anymore with the existence of AI. For example, usual
entertainment platforms like Netflix, can be used to collect such data to understand,
track, and design a better learning experience.
In this fast-changing and the ever-competitive world, organizations have to be
proactive in terms of L&D. It is required on behalf of the HR team to ensure that the
relevant trading models and knowledgeable resources are available for employees
24 × 7 so that they can learn whenever they want. AI has the potential to transform
learning and ultimately leaving for greater alignment between learning outcomes
and business values.
3.3.8 AI AND EMPLOYEE ENGAGEMENT
The motive of employee engagement is to create a workforce that is committed,
involved, and enthusiastic about their work and organization. To create an envi-
ronment where people flourish, employers need to understand what it means to
be engaged employees and how to manage people to make an engaged workforce.
Employee engagement is all about the individuals’ better performance leading to
organizational performance. Employees who are engaged based on key workplace
elements predict important organizational outcome elements.
Gallup’s employee engagement index is based on the worker’s response to 12
workplace elements with proven links to performance outcomes. The index provides
a high level of insight into the workplace in the form of a highly engaged, non-
engaged, and disengaged percentage of employees. Overall, engaged employees are
always enthusiasts about their work and get involved in it deeply. The employers
in the category of non-engaged have checked out or unattached themselves from
the work and organization. The disengaged people at the workplace are resentful
and feel unhappy at work and potentially undermine what engaged coworkers might
accomplish.
Organizations focus on employee engagement due to many reasons. Engaged
employees stay long with the organization, they outperform their targets, treat their
customers/clients with royalty, don’t waste their and others’ time, always try to
improve their skill sets, learn new skills, take fewer leaves, and think about the orga-
nizational goals along with their personal goals. The trait that differentiates engaged
employees from other people at work is motivation.
Intrinsically, every person is motivated and wants to work, but it’s the responsibil-
ity of the organization to understand what is killing the motivation of their people and
the reasons due to which they feel disengaged. Multiple reasons have been recorded
from disengaged employees such as job is not interesting, no guidance and support
in completion of tasks, no visionary managers, or no clarity of their job roles. It is a
responsibility of the organization to understand the factors that motivate an employee
to happily engage in their tasks. Employees feel engaged and motivated when the
organization can make them see a picture of their future where they can reach with
the use of their potential. Organizations need to develop a culture of engagement by:
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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43Reinventing HR in Era of AI
i. Focus on culture: Organizations that make culture as part of their overall
strategy have engaged people.
ii. Develop leaders: Organizations develop their leaders who can inspire and
mentor people.
iii. Investment in people: Organizations invest in the skill and development of
their employees to keep them motivated.
All these factors create an environment of prosperity and engagement among people
and make them more productive for overall organizational growth.
Organizations are facing the burnout of employee turnover and trying their level
best in engaging the people. According to Gallup’s global survey, almost 85% of the
employees feel disengaged to a certain extent at the workplace, which has its economic
consequences of a whopping amount of $7 million loss in productivity. Sixty-seven
percent of the people are disengaged, which is a concern for organizations. The
disengaged people are indifferent toward their company due to any given reason: lack
of motivation, lack of acknowledgment and appreciation, lack of time and money,
and many more.
After all efforts, organizations are unable to keep their employees motivated and
engaged and that is where HR professionals are intimidated by the application of AI.
Every organization expects its employees to feel motivated and enthusiastic about the
work, growth-oriented, and self-driven. However, employees can’t expect much more
from the organization than only the annual performance review meetings and monthly
report presentations. They want real-time handholding feedback and appreciation,
which is not possible or feasible for organizations. Hence, organizations are looking
for options that can help managers in engaging their teammates effectively, and AI
is the solution to that.
The advances in AI can modernize the process of organization, starting from
onboarding to performance management to talent development to retirement from
the services. AI can help improve the engagement index and workplace experience
of employees in the following ways:
i. Behavior mapping, using predictive analytics or sentiment analysis: By
behavioral mapping using predictive analytics, AI can better synergize the
training programs and employee engagement. As already discussed, AI can
develop personalized training modules required for employees’ current and
future needs. By analyzing the past performance data and the laps/gap in
the performance presentation reviews shared by clients, team members, and
seniors, AI is capable of designing training capsules to assist employees in
completing their tasks without errors or delays. This will help organizations
in keeping their employees motivated and engaged as they no longer would
be hesitating and waiting for guidance.
AI can provide adaptable training courses to help employees in real time
for better job engagement. Simultaneously, AI can predict the quitting ten-
dencies of the employees using behavioral maps. Behavioral maps are based
on data capture during employees’ everyday interactions with different
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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44 Transforming Management Using Artificial Intelligence Techniques
categories of people. By analyzing the communication and response patterns
while interacting with clients, peers, and seniors can provide a clear intention
of employees. By addressing issues on time, organizations can better avoid
such circumstances using AI. This will engage people and motivate them to
stay long with the organization and thus help them to grow profit.
ii. Chatbot integration for improving employee experience: By 2020,
it is predicted that 85% of the consumer interaction will be without
human interventions and 80% of the organizations are getting ready to
bring chatbot automation for better customer and employee experience.
Communication is the key to resolve all problems. As per the Internet
user chart, it has been noticed that people are using more Internet-based
applications in their personal lives in comparison to the workplace. People
don’t feel comfortable confronting their real emotions at the workplace,
which is the major cause of employee disengagement. Chatbots can pro-
vide a solution by offering a platform for more informal conversations
where employees can share thoughts and feelings without carrying a sense
of getting a judgment. Chatbots can provide personalized assistance to
employees where they can get information in a second on anything: ‘What
is HR policy on coming late?’ ‘Where can I find the first-aid box’, or ‘where
the admin manager sits’.
iii. Responsive and real-time feedback: Real-time feedback using AI not only
help the organization to understand the emotions of the people, but it will
also help people to improve their performance. Real-time feedbacks moti-
vate people as their performance is getting appreciated immediately after
the task is finished. It provides information about the gap areas in perfor-
mance and recommends the training modules to improve the performance
and experience. This is a good way to keep employees engaged. Advanced
AI tools are based on sentiment analysis, which provides better insight into
employees’ mood and their satisfaction level with seniors, colleagues, work-
places, to address issues immediately as they arise to enhance employee
experience.
iv. AI tools for team collaborations: AI can save managers from forming teams
that can’t work together. With the help of data available and using predictive
data analytics and machine learning, managers can make effective teams
that suit best to work together. It will drive engagement due to more cohe-
siveness and better collaborations. AI can look into the activity calendar and
can suggest the availability of people that can form effective teams to work
on a project. It will avoid the prejudice and bias of the managers in forming
teams that are unable to perform together.
However, AI is still in a nascent stage to actively indulge in such operations.
Nevertheless, it is continuously improving and can bring a major shift in workplaces
and employee experience. It can help organizations and managers to understand
employee behavior better. Just like the usage of little interfaces like chatbox, AI helps
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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45Reinventing HR in Era of AI
in improvising employee experience well, which will eventually lead to higher job
satisfaction of employees leading to increased and improved productivity for the
organization.
3.3.9 AI AND EMPLOYEE RELATIONSHIP MANAGEMENT
Employee relationship management is a relatively new concept. Customer relationship
management and supplier relationship management are tools that are regularly used
in today’s world to understand and improve relationships and external connections.
However, employee relationship management is still new to the game. Employee
Relationship Management is a new breed that unleashes the potential of people
net works within the internal workforce. With the emerging need for AI, people
net work today is not just about friends and followers at the workplace or HR organi-
zational structure, it is much more. Today, these organization networks are defined by
how people interact and what people create within different business systems.
AI can crunch numbers as compared to humans. It is also able to process a large
amount of data and make faster data-driven decisions. Such has been the influence of
AI in the field of human resources today that people relationship data have enabled
organizations to see beyond their chart and address real-world HR business problems
in a data-driven way. Today, AI in the workplace is a breakthrough that is enabling
good managers to be great. There are many applications of AI in organizations, from
improving relationships with employees and customers to finding patterns in extreme
data volume to performing repetitive tasks.
The introduction of the new models such as Domain Nexus has taken the AI game
in HR to another level. These AI-based systems not only take care of the people
relationship management but are also responsible for building employee engage-
ment, measuring company health, managing talent and team, and also optimizing
organizational collaborations. These platforms apply network and people analytics
to Enterprise Collaboration Systems. It intelligently analyzes the data from people’s
interactions within Collaboration tools and builds an entirely organic pattern of the
organization. Closeness, centrality, social structures, community effects, and net-
work effects are all made visible. Along with it, the AI-based program provides a
technology platform that offers People Relationship management services to partners
and customers. Predefined Integrations and interfaces are its selling point and make
it possible to connect Data from the HR and Enterprise Collaboration System. The
system even offers ready-to-use services, which allow customers and employees to
utilize the full potential of their data and drive business outcomes. The system works
in collaboration with data visualization, people analytics, and social network analyt-
ics and data management.
Domain Nexus might be the newest addition in this technology-driven era of
human resources but it’s not the only one. WebLife is also an AI-driven People
Relationship Management software. It is a powerful new breed of software that
builds upon the existing concepts of CRM, which are also known as Customer
Relationship Management. In this world, keeping track of people’s information and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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46 Transforming Management Using Artificial Intelligence Techniques
effectively managing it is getting harder and harder. Along with it, recognizing the fact that organizations have people needs that goes beyond their boundaries of cus-tomers and employees – such as volunteers, mentors, and donors, is also a task. Thus, these kinds of systems have become a robust platform that addresses these needs seamlessly. It provides complete ‘people management’ from web applica-tions through customizable workflows. A distinctive strength of this software is recognizing that every organization is unique. Such software is completely configu-rable to define the company’s application forms and processes, person types, levels of access, etc. Today, eWebLife’s PRM is being utilized for various activities, from capturing to tracking volunteers, mentors, employees, and prospective recruits and clients – a tribute to how this configurable tool meets the needs of diverse organiza-tions. It also allows clients as they can select from a variety of modules to configure the PRM to best meet their needs, by adding on additional modules.
AI in People Relationship management is a relatively new concept today but if we can use it right, then it will be a breakthrough: from building a cross-functional team to encouraging social interactions to understand how employees feel, the possibilities of improving HRM using AI seems endless today.
3.4 CONCLUSION
AI is increasingly finding applications across many business operations. While functions like production, sales, and managerial departments in companies have been benefiting from AI solutions to strengthen their productivity, AI is also han-dling diverse facets of HR workflow. Various traditional methods have already been practiced by companies for talent acquisition, performance management, employee engagement, and L&D, but the involvement of technology has made it even simpler. Employers now have various platforms where they can look for talent themselves without moving out of the offices. Cloud-based and mobile platforms for communication have made the work–life balance of employees easier and less hectic.
Various models have come into existence, specifically for the HR domain to understand human resources better. Like the Predictive Models, which would help the managers predict an approximate outcome or productivity of the employee given his past performance data, Natural Language Processing has been working on continuous listening of employees and thus working toward processing a balance between managers and employees’ demands. Organizational Network Analysis, on the other hand, is an interface that very vividly tracks and understands email habits of employees to one another, which can help in maintaining a balance of work within the organization.
AI has made personalization possible for every organization. It has made the approach move from ‘One size fits all’ to ‘One size fits ONE’. Also, knowledge and learning are endless. AI has made life-long learning now possible. People have understood the life of skills is shorter and thus with AI, the reskilling culture can be adapted efficiently. But meanwhile, one cannot forget the comfort of the users,
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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47Reinventing HR in Era of AI
The above-mentioned aspects are just a few of many major and vital roles AI has
been playing in the HR domain. It is not the end yet, though AI industry is to be seen
as one of the prominently growing industries in not just HR but in all aspects too.
Nevertheless, it will be improvising the HR domain a lot more in upcoming decades
than it is today. Thus, at the end of the day, AI seems to have no end toward its appli-
cation in HRM.
BIBLIOGRAPHY
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Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3), 139–159.Brown, L. (2018). Two-thirds of job hunters favour face-to-face recruitment over
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Dua, A. (March 8, 2019). The growing role of Artificial Intelligence in HR tech. Your Story. Available at: https://yourstory.com/2019/03/growing-role-of-artificial-intelligence-in-hr-tech-dbmgzercm1
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HR.COM. 2019. The 2019 state of artificial intelligence in talent acquisition. HR.COM. Available at: https://www.oracle.com/a/ocom/docs/artificial-intelligence-in- talent-acquisition.pdf?elqTrackId=1279a8827f3d4548ae3f966beeeef458&elqaid=83148&elqat=2
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51
4 HR Trends in the Era of Artificial Intelligence
Shikha KapoorAmity University Noida
4.1 INTRODUCTION
Success in the coming years in an organization is by its ability to adapt and meet
the changing needs of market realities, including new technologies and evolving
c ustomer expectations. Today, organizations have to reinvent their working methods
to realize their productivity gains. They have to respond to the demands of a chang-
ing workforce.
CONTENTS
4.1 Introduction 51 ....................................................................................................
4.1.1 What Is Artificial Intelligence 52 ............................................................
4.2 Upcoming Technologies and HR 53 ....................................................................
4.2.1 Rehumanizing and Rethinking HR with AI 54 .......................................
4.2.2 Upskilling and Training 54......................................................................
4.2.3 Artificial Intelligence for Right Employee Engagement 54 ....................
4.2.4 Employee Brand Sentiment 54................................................................
4.3 Need to Prepare HR Trends 54 ............................................................................
4.3.1 AI Plus Human Intelligence Enhances the Candidate Experience 54 ....
4.3.2 Artificial Intelligence to Be Developed for the Future
Organization’s Workforce 55 ...................................................................
4.4 Preparing for the Future of Work: Call for Action 56 .........................................
4.5 AI in Recruitment 56 ...........................................................................................
4.5.1 AI and Employee Development 56 ..........................................................
4.5.2 AI in Employee Attrition 57 ....................................................................
4.5.3 AI and Engagement and Employee Self-Service 57 ................................
4.6 HR and Deep Learning 59..................................................................................
4.6.1 Amazon 59 ...............................................................................................
4.6.2 Google 60 .................................................................................................
4.6.3 IBM 60 .....................................................................................................
4.7 Remember the Human Touch 61 .........................................................................
Bibliography 61............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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52 Transforming Management Using Artificial Intelligence Techniques
The future of work in the organization will have internal and external pressures,
which will create challenges and opportunities in equal measure. Human resources
(HR) has to respond to these workforce pressures on four main fronts:
1. Diversifying workforce demographics: Corporations have to manage
different levels of groups in the workforce, with the requirement of their
needs and motivators. These will shape the culture and deliver the employee
value proposition.
2. The rise of contingent labor: The contingent workforce will complicate the
workforce planning. Different ways have to be created to achieve an optimal
workforce size, shape, or composition.
3. Shift to a consumer mindset: Employees are increasingly aligning with their
personal goals and values and hopping on jobs; this mindset changes the
talent attraction and hiring strategies.
4. Intelligent automation in the workplace: Automation technologies are
impacting the workforce. To increase production and streamline manual
work, work process needs to be automated and employees need to be reskilled,
and this creates demand for new roles and new technical specializations.
New trends and developments have impacted the term HR. HR in future will focus
on the use of a combination of human and digital work. A new priority is driven for
HR, which requires leaders to develop proficiency in artificial intelligence (AI) and
HR to be more personal, human, and intuitive.
With consumerization taking HR, HR is tasked with using digital technologies
to keep up with competitors. Digitally advanced companies are playing key roles by
establishing a value-creating strategy, cross-functional leadership teams, a robust
digital platform, and an employee-driven experience. An effective digital HR strat-
egy empowers the workforce to self-direct and learns in a flatter, more agile design,
instead of one siloes in hierarchies and controlled by directive leadership. The work-
force is now using digital intelligence, where a team of men and learning machines
is used. AI can speed up the processes of HR and help in saving more time for the
HR personnel.
“AI forces HR to rethink its added value and license to operate”, Robert Charlier
PwC.
AI is the talk in the business world. There is a continuous advancement in HR due
to digitalization and data analysis.
4.1.1 WHAT IS ARTIFICIAL INTELLIGENCE
AI solves the intellectual problems that are related to human intelligence. This is
a new field of computers. Machines here “think like humans” and accomplish the
jobs that are related to solving the problems of language processing, learning, and
reasoning.
Sundar Pichai, CEO of Google, in the World Economic Forum Annual Conference
in 2018 mentioned AI as more profound than electricity or fire. AI is an algorithm;
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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53HR Trends in the Era of AI
it takes data and can do beyond what is was coded without human intervention and
can provide more information, capacity, and budget to make an important decision.
Automation, robotics, and AI are advancing and causing changes in the nature
and number of jobs available and the process of organizing work relations.
Role of AI at three levels of intelligent digitalization:
1. Assisted intelligence: It provides assisted intelligence of what an organi-
zation and its people are doing by automation, standardization, and time-
consuming tasks, e.g., GPS providing drivers in cars the navigation for
directions to adjust the road conditions.
2. Augmented intelligence: This technology takes decisions for both man and
machine. For example, AI helps in assisting car ride service for sharing,
with the help of a combination of organized service human traits, emotional
intelligence, innovation, and creativity.
3. Autonomous intelligence: This is an advanced form of technology. In
this technology, AI is used where machines reach the subconscious level
of information by acting on their own, e.g., self-driving vehicles. Data
governance and data science are used to create data in this technology.
AI helps in improving employee engagement, eradication of repetitive jobs, speeds
up talent search, and reduces attrition of employees. To reimagine the worker’s expe-
rience, the algorithms train themselves to simulate human behavior. AI will help to
provide better time, capacity, budget space, and information by responding to the
intuitions and interpretations. As per the outcome report of PwC’s 20th CEO Survey
(edition 2017), in future, global business leaders will speed up and increase the value
of their management using AI.
4.2 UPCOMING TECHNOLOGIES AND HR
The latest upcoming technologies introduced in HR are robotic process automation
(RPA), AI, and virtual reality (VR).
The CIO magazine defines RPA as: “RPA is an application of technology, which
is directed by business logic and structured inputs. This is for the purpose for auto-
mation of business processes”. RPA is used for supporting data-driven processes.
The robotic software interprets application transactions by processing and manipu-
lating data, and the responses are triggered by communicating with other digital
systems – automatically RPA is used in HR for saving time effectively by focus-
ing on important part of their work. For example, the RPA can replace the manual
recruitment process and update the tracking system for a new hire, and it records the
database of employees. HR compliance is double-checked by RPA.
VR, a technology that creates a realistic 3D image or environment, helps humans
to interact and perceive in realistic ways. VR enhances the HR capabilities within
recruitment and hiring, onboarding, learning, and development.
British Army has used VR in HR to overcome its recruitment challenges based on
combat adventure and tank and parachute training.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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54 Transforming Management Using Artificial Intelligence Techniques
4.2.1 REHUMANIZING AND RETHINKING HR WITH AI
Today, organizations are providing better experiences to employees through cul-
ture, workplace design, and technology. AI is making a significant impact in HR
and recruitment in an enterprise. They reduce recruitment time by streamlining
the screening process and also predict when to start the hiring process. AI helps in
improving the recruitment process and employees’ experiences in the process right
from job application. AI can also track the operations and help HR to create insights
by advising strategic actions based on the data. AI can do something that it’s not
coded to do. The incorporation of AI has helped HR to focus on the best. HR with
AI can be used to provide a personalized employee experience as now workforce
expectations are getting more personalized with individual requirements.
AI-based tools will provide an employee with enriching, enabling, and empower-
ing experience, and this will be used in our daily lives.
AI when used in the recruiting process will save a lot of time and energy of the
employee and the company when used with an integrated Application Tracking
System (ATS). The company’s use of AI-based sourcing, screening, skill-matching,
and outreach can help uplift the impression of the company.
4.2.2 UPSKILLING AND TRAINING
Millennial employees prefer interactive training that teaches critical job skills.
AI-based learning used in training sessions helps employees to have an enjoyable
and appropriate experience, thus breaking the traditional eLearning systems and out-
dated organizational training.
4.2.3 ARTIFICIAL INTELLIGENCE FOR RIGHT EMPLOYEE ENGAGEMENT
AI tools are used for understanding the mindset of employees and for active listen-
ing. It helps the company to know the needs and wants of employees and provides the
right direction; it trains them for career progression and growth.
4.2.4 EMPLOYEE BRAND SENTIMENT
Today’s workforce is looking for a brand story for better credibility in their careers.
AI tools also help to provide the first impression to candidates with a personalized
technique for upskilling and an engagement process (Figure 4.1).
4.3 NEED TO PREPARE HR TRENDS
4.3.1 AI PLUS HUMAN INTELLIGENCE ENHANCES THE CANDIDATE EXPERIENCE
For many companies AI is in talent acquisition. This technique helps the company
reduce time and save energy in hiring, and it enhances the productivity of recruiters.
DBS Bank uses AI. A virtual recruitment company, JIM (Jobs Intelligence Maestro),
created by the talent acquisition team of DBS, is powered by AI. It uses AI to screen
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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55HR Trends in the Era of AI
candidates applying for the post of wealth planning manager, which is a high-paying
job in a consumer bank.
The World Economic Forum estimates that about 75 million current routine jobs
will be performed by AI. Though new jobs will be created for 133 million people,
technology programming and design will be important, and skills of emotional and
technical intelligence will be needed.
4.3.2 ARTIFICIAL INTELLIGENCE TO BE DEVELOPED FOR THE FUTURE ORGANIZATION’S WORKFORCE
In 2021, upskilling will be needed for employees not using AI. Five initiatives are
required for developing AI workforce:
• AI used to solve business problems by collecting data on the current state of
the problem and the key KPI to be impacted with AI.
• Educate on the benefits of using AI to the key stakeholders to solve the key
business problems by building a cross-functional team.
• Use AI in key job roles of HR, i.e., recruitment, New Hire, corporate learn-
ing, and onboarding.
• Use of AI in developing skills and performance. Role of HR in the life cycle
of employees.
Gartner predicted that by 2022, one out of the five workers engaged in nonroutine
jobs will be using AI. But this will be done by shifting the mindsets of employees and
the company. Skills-based hiring will be required.
FIGURE 4.1 AI and data analytics used in HR to improve workforce decisions.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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56 Transforming Management Using Artificial Intelligence Techniques
4.4 PREPARING FOR THE FUTURE OF WORK: CALL FOR ACTION
As per Deloitte Capital Trends, the key challenge for business leaders in harmony with the experts as the pace of change is accelerating, all executives should have a shared vision in the age of AI. New roles coming up are Chief Digital Officer and Chief Ethical Officer.
Six digital operative model practices should be used by the Organization:
1. Redefine the digital model for an enterprise 2. Appoint a Chief Digital Officer 3. Focus on capabilities 4. Digital hubs to be developed 5. Delivery pods to be built 6. Highlight the talent recruitment and retention
4.5 AI IN RECRUITMENT
Hiring candidates as per the job is a difficult task and requires a lot of time and energy.
AI algorithms reduce time and simplify the task of hiring candidates by search-ing resumes, identifying the right candidate for the right job, and identifying the top performers by analyzing their data.
• Draw top talent: Selecting the best candidate as per the talent for the job is quite a task. LinkedIn, Glassdoor, Indeed, etc., the job searching websites, use machine learning algorithms to collect data and scrutinize them as per the user activities. Data collected is as per the post, search history, clicks, page visits, and other activities that are monitored to give the best offers to aspiring candidates.
• Shortlist resumes: Applicant tracking system (ATS) is a recruitment-based software that filters the resumes. The system screens thousands of resumes as per the task requirement, and thus the task is simplified and the candida-tures are kept as per the order of priority (best person with maximum skills for the task to meet job requirement).
There are more than 80,000 employees worldwide in L’Oréal Group, and they recruit around 15,000 candidates each year. L’Oréal has around five million visits on their website for job possibilities. So, an average of 134 candidates apply for jobs. Thus, to meet the demand and provide a great experience to a candidate with review AI is used.
4.5.1 AI And emPloyee develoPment
Training is the most important element for employee growth and development, and it also affects organizational development. For a job to become productive, AI is used. Algorithms pull data on parameters of age, learning experience, culture, educational
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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57HR Trends in the Era of AI
background, work experience, behavior, and activities, and they analyze data to cre-
ate custom learning. This has made learning interesting and enjoyable (like watching
a movie) through gamification.
4.5.2 AI IN EMPLOYEE ATTRITION
AI helps in identifying and addressing future problems of individuals/departmental
issues, thus predicting future sources of turnover before it becomes a bigger problem.
4.5.3 AI AND ENGAGEMENT AND EMPLOYEE SELF-SERVICE
AI helps in identifying the cause of stress that is affecting the performance of the
employee in a timely manner. Intelligent chatbots are used to make interactions easy.
By acting as a virtual assistant, the HR queries regarding employees can be met, and
employees can be employed as per HR policy.
Seedlink is built with the latest AI and its algorithm identifies the verbal and written
pattern to make predictions on behavioral traits and culture. So the organization
can use the full capacity and data-driven talent acquisition and internal mobility.
By knowing the expectation of individuals, their engagement can be analyzed and
personalized goals can be set (Figure 4.2).
AI has also increased the expectations of HR. AI helps to transform the perception
of employees about their job, relations in office, and contribution toward the growth
of the company. It also helps to engage employees in strong, smart, workplace culture
(Figure 4.3)
AI is the technology that solves the intellectual problems associated with human
intelligence. It helps machines to “think like humans”, and accomplish tasks
associated with learning, reasoning, problem-solving, and processing of language.
In AI, the fundamental technologies are machine learning and deep learning.
Machine learning empowers machines to make predictions by learning from
training from recorded data by recognizing the data and algorithms. This is done by
not getting programmed.
HR and Machine LearningIrregularity detection: Observing and identifying as per the dataset the noncon-
firming patterns.
Background verification: The structured and the unstructured data points can be
raised from applicant resume by using machine learning–powered predictions by
raising red flags.
Employee attrition: Helps proactively to engage and retain employees having a
high risk of attrition.
Content personalization: Predictive analytics is used for professional develop-
ment programs for career paths.
Deep learning is an advanced form of machine learning. It trains the computer to
learn through neural network architecture from a large amount of data. Here the data
is broken down into layers of abstraction. Basic parameters are set about the data and
the computer is trained to learn on its own by recognizing the pattern for processing
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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58 Transforming Management Using Artificial Intelligence Techniques
FIGURE 4.2 AI and its usage in HR.
FIGURE 4.3 Skills required in AI for success.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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59HR Trends in the Era of AI
using multiple neural network layers. The deep learning algorithms after the training
can make interpretations/predictions of very complex data.
4.6 HR AND DEEP LEARNING
• Image and video recognition: Deep learning algorithms can categorize and
organize candidates from given videos and photos of thousands of appli-
cants based on objective data.
• Speech recognition: Deep learning algorithms use speech recognition algo-
rithms to identify the human voice and reply accordingly.
• Chatbots: AI systems automate HR service delivery with chatbox. Chatbots
are trained by natural language processing (NLP) for understanding human
tone, language, and context.
• Recommendation engines: As per the interest of individual employees, big
data and deep learning are used to identify learning platforms.
• AI is an important factor in this new technology-enabled employee experi-
ence and includes pioneering components such as machine learning, chat-
bots, pattern detection algorithms, analytics, and much more. But how can
such innovations be utilized in a department that was once considered less
than tech-savvy?
• Next, we look at how some of the most successful tech giants have adopted
AI and made it work for their organizations – and how CHROs can apply
these ideas to their own HR departments.
4.6.1 AMAZON
• Amazon’s powerful recommendation engine knows what its customers have
purchased, and what other people who resemble those customers have pur-
chased, and it can infer what those customers may want to purchase in the
future. Its software has amassed a vast trove of data – tracking its 152 mil-
lion customers peruse 1.5 billion items in its store – and can now suggest
products that might be useful to customers the instant they begin shopping.
• HR departments can employ the same sort of recommendation engine to
push relevant items to employees. By studying the usage patterns of employ-
ees as they interact with the organization, a company can leverage that
information and learn from it to make proactive suggestions. For example,
an employee wants to take maternity leave and needs more information on
how to begin that process. A search of the company’s intranet can automati-
cally show details about the topic, as well as reveal additional resources that
other employees who previously searched for the same information would
find useful.
• Sometimes, the information an employee may be seeking from the HR
department can be sensitive, such as changing departments or report-
ing inappropriate behavior. That can cause a surge of emotions, including
stress. If an HR department can make finding the requested information
as seamless as possible, employees may feel as though their organization
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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60 Transforming Management Using Artificial Intelligence Techniques
is empathetic to their wants and needs. That could lead them to connect
with their company on a deeper level and enjoy a more satisfying employee
experience.
4.6.2 GOOGLE
• When typing a phrase into Google’s search box, before one has the chance
to complete the query, several iterations of what he or she might be looking
up automatically populate – this is based on the search engine’s pattern rec-
ognition of previous searches. As a result, while a few keystrokes may only
take a few seconds to put on the page, Google’s algorithm takes predictive
analytics to the next level and makes the “Googling” process even faster and
easier for the user.
• HR systems are starting to do the same, capturing employees’ interactions
with HR and revealing patterns. AI will use those patterns to improve future
interactions and create relevant information for other employees. For exam-
ple, the same employee mentioned above is searching for information on the
maternity leave policy and uses a chatbot to request it. If the chatbot has not
encountered this request previously, it will send the employee to a human
agent for resolution. But as more and more employees request this same
information, a base of knowledge is automatically created by recognizing
the queries, gathering the appropriate responses and providing the informa-
tion requested without the help of a human agent. Instead, the chatbot is now
equipped to address the subject moving forward.
• Using AI in this fashion has at least two immediate and significant ben-
efits: Employees will receive the information they need immediately, and
human agents can focus on more important and productive tasks rather than
answering the same repetitive questions day in and day out.
4.6.3 IBM
• When Big Blue wants to hire someone, it now uses AI software to harness
job applicant’s vast digital output. Different iterations of this software –
made by a variety of small vendors – can monitor work models, posts from
the social media, expressions of the face, patterns of speech, and more for
signs of bigotry, violence, curiosity, empathy, and compassion, among oth-
ers, to identify employees who are the best fit for the culture and overall
organization.
• Advocates of AI argue that employers could use the technology to hire
a more diverse, empathetic, and dynamic workforce. That, in turn, can
improve the overall employee experience, as companies can gain control
over their culture through their hiring. With new hires, a company can cre-
ate a workforce whose traits are celebrated, creating a sense of inclusion and
belonging for their employees.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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61HR Trends in the Era of AI
4.7 REMEMBER THE HUMAN TOUCH
• For all the values that AI can bring to the process, HR must remember to
focus on the “human” in HR.
• Similarly, while AI can transform the entire HR field, technology can only
go so far in providing people with what they want and need. Companies that
go the extra mile and include human interaction, involvement, and emotion
when working on the overall employee experience will be able to take full
advantage of the additive benefits that AI offers to the HR department.
• That’s what the smartest companies in tech understand: AI enables better
decisions while making life easier for their customers. HR professionals in
any field can do the same, making their own lives as well as the lives of the
employees easier.
BIBLIOGRAPHY
Agrawal, R. (2020). Technologies for handling big data. in Fausto Pedro Garcia Marquez (ed.), Handbook of Research on Big Data Clustering and Machine Learning. Hershey, PA: IGI Global, 34–49.
Amla, M., & Malhotra, P. M. (2017). Digital transformation in HR. International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), 4(3), 536–544. Retrieved from http://www.ijims.com
IBM Watson Analytics. http://watson.analytics.ibmcloud.comKapoor, S., & Meachem, A. (2012). Employee engagement: A bond between employee and
organisation. Amity Global Business Review, 7, 1–10.Merlin, P., & Jayam, R. (2018). Artificial intelligence in human resource management.
International Journal of Pure and Applied Mathematics, 119(14), 1891–1895. Retrieved from http://www.acadpubl.eu/hub/
Oliver Pickup. (2018). AI in HR: Freeing up time to be more human. https://www.raconteur.net/hr/ai-hr-human
Pathak, S., & Agrawal, R. (2019). Design of knowledge based analytical model for organizational excellence. International Journal of Knowledge-Based Organizations (IJKBO), 9(1), 12–25.
Strohmeier, S., & Piazza, F. (2015). Artificial intelligence techniques in human resource management—A conceptual exploration. in C. Kahraman, Ç. Onar (eds.), Intelligent Techniques in Engineering Management. Cham, Switzerland: Springer International Publishing, 149–172.
https://www.raconteur.net/wp-content/uploads/2018/11/HR_p14_3.jpghttps://www.digitalhrtech.com/ai-in-hr-impact-adoption-automation/
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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63
5 The Rise of Automation and Robotics in Warehouse Management
Amandeep DhaliwalManav Rachna International Institute of Research and Studies
5.1 INTRODUCTION
There has been a disruptive evolution of the retail market supported by the immense
growth of technology, smartphones, and smart devices. It has changed the way people
purchase goods nowadays. Online shopping and transactions have been on a steady
rise. Customers are much more demanding and looking for instant gratification by
ordering and expecting delivery as soon as possible. A report by 10 e-commerce
trends found that presently 10% of U.S. retail sales are online, while 56% of in-store
purchases are again e-commerce/digital-influenced. Online sales are expected to
grow by nearly 15% annually. What this means is that just having a brick-and-mor tar
store would not mean survival without the presence of online, which is slowly
becoming the customers’ preferred channel. The retailers’ understanding of these
changing trends has integrated digital e-commerce in all aspects. This has further
CONTENTS
5.1 Introduction 63 ....................................................................................................
5.1.1 Why E-Commerce Businesses Adopt Automation Technologies? 65 .....
5.1.2 The Technologies 66 ................................................................................
5.2 Advanced Technologies 67 ..................................................................................
5.2.1 Automated Storage and Retrieval System (ASRS or AS/RS) 67 ...........
5.2.2 Goods-to-Person Technology (G2P) 68 ...................................................
5.2.3 Automated Guided Vehicles 68 ...............................................................
5.2.4 Autonomous Mobile Robots 69 ...............................................................
5.2.5 Articulated Robotic Arms 70 ..................................................................
5.2.6 Automated Guided Carts 70 ....................................................................
5.3 Conclusion 70 ......................................................................................................
References 71 ................................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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64 Transforming Management Using Artificial Intelligence Techniques
changed the way goods are distributed from distribution centers to customers. To meet
the ever-changing expectations of customers, the world of logistics and warehouse
management is constantly evolving and changing. “Globalization has caused many
supply chains to be visible to automation and several other technologies (big data
(Agrawal Rashmi 2020), robotics, artificial intelligence, 3D printing, and so forth).
The shift towards creating a new intelligence is now being witnessed over several
fields” (Lorentz & Hilmola, 2012).
The rise of e-business leads to the growth of new and innovative business models.
Supply chain professionals of the present day are dealing with “omni-channel
ret ailing”, “complex global supply chains and consignment inventory” (Da Silveira &
Cagliano, 2006). Therefore, the e-commerce companies are making major
investments in adopting robotics and automation-based supply chain technology.
There is an internal pressure of increasing throughput, handling of greater vol-
umes of inventory while cutting costs, reducing inventory cycles, and maximizing
productivity; at the same time, they have to meet the customers’ expectations such
as faster delivery by next day shipping, commoditization as well as Amazon effect.
The immense growth of the online business has forced that warehousing to adopt
advanced technologies in order to solve challenges of real-time tracking, process-
ing, and on-time delivery of packages more effectively. This is where automation
steps in to help meet the demands of online retailers or “e-ret ailers”. Online retailers
are nowadays adopting some forms of automation in their warehouses to improve
product movement for efficient order fulfillment, storage, and reducing the faulty
return pickups to keep delivery costs to a minimum and remain competitive in the
online market.
The use of robotics and other IT-supported technologies has become the foun-
dation for warehouse automation. In the age of robotics, they are categorized into
two categories: behavioral-based robots and industrial robots. Industrial robots are
specifically used for warehouse management (Nolfi & Floreano, 2001).
Warehouse automation means the use of various IT-based technologies that allow
a warehouse to operate much more effectively and efficiently in order to achieve
greater outcomes with contributing significantly fewer efforts, which revealed that
“59% of IT and operations personnel in manufacturing, retail, transportation, and
wholesale market segments planned to expand process automation between 2017
and 2022” (Zebra’s Warehouse Vision Reports, 2019). Retailers face an increased
number of online orders and package shipments daily, and this number becomes
larger, which almost doubles up at the time of special sales around Diwali, Holi, or
other festivals when they launch special big billion day sales or great Indian festival,
etc. To fulfill a large number of orders while maintaining low costs, the e-retailers
require the support of automation; especially, it becomes impossible to deliver on
time without any form of automation.
Thus, the application of automation in warehousing and distribution helps
e-commerce businesses in tackling the high logistics demands without high main-
tenance costs (Nolfi & Floreano, 2001). A range of technology options is avail-
able for e-retailers to consider and choose the technologies for their warehouse
management.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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65Automation and Robotics in Warehouse Management
5.1.1 WHY E-COMMERCE BUSINESSES ADOPT AUTOMATION TECHNOLOGIES?
E-commerce is growing rapidly around the globe, which causes drastic changes in the
retail landscape. As the customer’s expectations are changing, it requires manufac-
turers, distributors, retailers, and logistics services providers to change how customer
orders are fulfilled for which they need to reconfigure their backend fulfillment and
warehouse operations as well.
The warehouse is not only a back-end operation but rather plays a critical role in
the supply chain management. Any issues in the operations of a warehouse can lead
to delays and impacts on the customer satisfaction and cash flow of the organiza-
tion. Efficient warehouse management has become an art in itself. It can provide
greatly enhanced efficiencies, improve performance, and support the growth of the
company. Thus, automation comes in handy improving warehouse management. “In
the beginning, when logistic and e-commerce companies considered to use robots to
carry out their business practices, there wasn’t enough technology to carry out the
tasks of carrying and handling a wide array of various shapes” (Deutsche Post DHL
Group, 2016).
Warehouse automation helps e-commerce businesses to experience several of the
following benefits:
To meet the accelerated fulfillment demands, the fast growth of e-commerce has
compressed order fulfillment times. Customers expect expedited delivery – same
day or next day delivery. Automation thus helps in fulfilling agile and nimble order
delivery.
Lower costs: Automation helps in reducing operating expenses and unneces-
sary errors; reducing overhead costs; and reducing the costs related to safety, labor,
equipment, and maintenance. It also reduces the costs associated with energy con-
sumption and storage space. It leads to enhanced warehouse space utilization and
flow.
Workforce productivity and retention: Automation can increase the efficiency and
productivity of human resources. The organizations do not need to employ more
people; rather, they attain greater productivity out of each employee without increas-
ing the headcount. This makes the work of a warehouse team easier by minimizing
manual processes and makes it further safer, which further leads to an increased
retention of employees.
Healthier inventory: Automation process leads to enhanced inventory data col-
lection and sharing among different functional areas. This leads to better inventory
management and control, making it almost 99.9999% accurate. It helps in the reduc-
tion of lost inventory, shrinkage and misplacements, and lesser shipping errors. It
uses a just-in-time (JIT) methodology for order fulfillment.
Sustainable “green” practices: Automation helps in contributing to environmen-
tal protection. It reduces land use as inventory is minimized, produces lesser wastage,
reduces the energy requirements for running the facilities, and overall lowers the
costs. In the case of refrigerated or temperature-controlled facilities or warehouses
that deal with special products or hazardous wastes, these green practices make
benefits to the environment. These benefits become especially impactful to handle.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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66 Transforming Management Using Artificial Intelligence Techniques
Building brand and reputation: Automation leads to an efficient handling of
orders and timely delivery of orders. This creates a brand image and reputation of
the organization among customers and suppliers. This leads to repeat business for
the business.
5.1.2 THE TECHNOLOGIES
The success of e-commerce business depends upon the speed and accuracy with
which items are picked as it eventually influences the fulfillment lead time, that is,
the time when the order is first being placed to the time when the item is delivered
to the customer. To ensure the speed and accuracy, warehouse automation comes in
very handy.
Warehouse automation is mostly of two types: process automation and physical
automation.
Process automation is also known as system automation, which consists of digi-
tization of manual processes like inventory data collection, and which integrates
that data with the database or ERP system of the organization. Process automation
consists of the usage of the ecosystem of barcoding and wireless barcode scanners,
which is used for inputting data about the items and tracking the items. This data
is further shared and saved in the central database of the organization where it can
then be used across all the functional areas such as marketing, logistics, and pro-
duction of the organization. Physical automation includes all kinds of mechanized
technologies or machines used for automation. It consists of the usage of robots
and robotic systems in the warehouse. These technologies that are used in physical
automation are much more expensive and costly to implement as compared to pro-
cess automation. Examples of physical automation technology include autonomous
mobile robots (AMRs), goods-to-person (G2P) technology, and automated guided
vehicles (AGVs).
Barcode labeling is a part of process automation. It is the most basic level of auto-
mation, which consists of the usage of printed paper, specific scanners, and IT-based
applications. Out of the many warehouse automation solutions, the use of barcode
labeling is the cheapest and easiest to carry out. The products are barcode-labeled,
which are then tracked with the help of scanners at various points in supply chains.
The usage of these labels helps in the correct entry of data and product into the sys-
tem, which further reduces the chance of errors in inventory tracking or shipping of
goods.
Barcode labeling further serves as the basis for physical automation. This tech-
nology becomes the fundamental identification of products, based on which further
operations of robotics and mechanized technologies work.
Barcode labeling helps in inventory tracking. Using barcode, the items are tracked
inside the facility at arrival during receiving, its storage and handling, and its ship-
ping stage. Therefore, it forms the foundation of the entire warehouse automation.
It thus prevents picking or ordering errors. It also helps in automatic data collec-
tion, which can lead to better arrival planning for goods. For example, operators
can easily ensure the same time delivery of cargo and shipment inventory. It also
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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67Automation and Robotics in Warehouse Management
improves traffic flow and picking efficiency within the facility. Since the required
data is easily available to operators in machines in their hands, it ensures an easier
identification of shelves and areas for unloading and picking, speedy and correct
unloading and picking, and timely departure and delivery of every item. Based on
the data collection, reallocation of employees to high-priority areas can be easily
done, which prevents the staff shortage and delays. All these advantages of bar-
coding ensure that no “out-of-stock” situations occur as the software monitors and
tracks all the items through all the stages in the facility. This information about the
items is synched with warehouse management system (WMS) of the company for
further rapid and transparent data sharing across company systems to enhance deci-
sion-making across management levels. By tracking the number of goods through
barcode labels, the reordering of items can be automated. Whenever the amount
falls closer to the predefined threshold levels, systems automatically reorder ensur-
ing no “out-of-stock” scenarios.
Amazon was the leader in innovation of the warehouse management with robotic
technologies. Amazon was the first one to use the robots called Kiva Systems, which
were the AGVs used in the warehouse and fulfillment centers. Later in 2012, Amazon
bought over the Kiva Systems and renamed the company as Amazon Robotics. As of
2019, they are using almost 100,000 robots in their warehouses for their fulfillment
operations (Donna, 2015). These robots are very advanced; they can pick up entire
shelves of products and deliver them to packing stations situated in specific areas of
a facility or a warehouse. These robots are based on the algorithms that can identify
the most popular items and what is their closest supply point. They also have sensors
that can prevent collisions on the way.
5.2 ADVANCED TECHNOLOGIES
The advanced technologies are being produced and used in warehouse management
all over the world. “Recent studies show that China has overtaken Japan to become
the world’s largest consumer market for industrial robots” (Munford, 2015).
5.2.1 AUTOMATED STORAGE AND RETRIEVAL SYSTEM (ASRS OR AS/RS)
ASRS consists of a variety of PC-controlled frameworks for automatically unload-
ing or setting as well as picking or recovering loads from specified areas in a facility
or a warehouse. These systems are especially useful in situations where there is a
movement of an extremely high volume of items within the facility or where there are
high storage and efficient space requirement. A proficient AS/RS framework assists
organizations with cutting costs by limiting the measure of pointless parts and items
away, and improving the association of the substance of a distribution center. Because
of robotized forms, it likewise takes into account more extra room because of high-
thickness stockpiling, smaller passageways, and so forth. Automation decreases
work costs while bringing down workforce prerequisites and expanding security.
These systems enable an efficient and cost-effective handling and coordination for
picking, packing, and shipping of items from a warehouse.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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68 Transforming Management Using Artificial Intelligence Techniques
5.2.2 GOODS-TO-PERSON TECHNOLOGY (G2P)
This technology is best suited for the present-day high-volume operation-based
e-commerce where online orders consist of individual products. Hence, G2P is espe-
cially useful for split-case order fulfillment, which consists of individual units that
have to be picked from individual locations and placed into a shipping tote or carton.
The traditional picking consisted of persons to a good model where individual pick-
ers are going to a specific product to pick it. This wasted time and was labor expen-
sive and led to a decrease in the number of orders processed. In contrast to the P2G
model, in G2P instead of a person going to goods, the good is brought to the person.
The stored individual items are automatically picked from storage and brought to
the picker or a pick station. “Since the picker does not have to walk, the focus at the
pick stations and pack stations is on ergonomics and high productivity. Model mini-
mizes the wasted time between picks and increases the number of orders processed
per person” (Kim, 2015). There are many different kinds of G2P technology but they
are all based on some fundamentals where the automated system brings SKUs to a
stationary pick station and not vice versa.
At present, a large number of extremely efficient G2P systems are available in the
market. Some of the companies that provide G2P-based solutions include Swisslog’s
AutoStore, Dematic’s Multishuttle, TGW’s Commander, SmartCarrier and Kiva,
which facilitate agile and precise order fulfilling for smaller-order quantities based
on e-commerce.
“This has been largely influenced by heightened processing capability and fully-
integrated controls architecture developments, making these high-SKU-count, high-
speed systems possible” (Diankov & Kuffner, 2008). These G2P solutions can include
multiple component-based technologies such as pallet-based or tote-/carton-based
systems, high-density storage systems, robots, horizontal and vertical carousels, and
vertical lift modules. And these solutions also have the flexibility to be scalable for
changes in increased or decreased products.
5.2.3 AUTOMATED GUIDED VEHICLES
Automated guided vehicles or automatic guided vehicles (AGVs) are the most com-
monly used technology-based solutions, which are used for moving materials in a
warehouse or other manufacturing facilities. The AGVs are the mobile robots that
follow markers or wires in the floor, or use vision or lasers. They are often termed as
“driverless” vehicles, which include robotically controlled industrial lift trucks that
were earlier manually operated, and used in manufacturing and distribution settings.
The present-day AGVs provide safe, efficient, and cost-effective movement of goods.
They allow better allocation of employees to tasks and deal with labor shortages.
AGV’s technology is known by other names as well such as laser-guided vehicle
(LGV) or Fahrerlose Transport system (FTS) in Germany or förarlösa trucker in
Sweden. The cheaper or lower-cost magnetic tape-based versions are called auto-
mated guided carts (AGCs). Many different models of AGCs are available in the
market, which can be used to move the products and carry and deliver loads across a
warehouse or a manufacturing plant usually guided by the magnetic tape.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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69Automation and Robotics in Warehouse Management
The first commercially produced AGVs were launched by Barrett Electronics, a
U.S.-based company in 1950. In its earlier version, these AGVs used to be like a tow
truck that used to follow a floor-based wiring system instead of a rail. In the next
version came the AGVs based on invisible UV markers. One of the first practical
applications was the delivery of mails in offices at the Sears Tower in Chicago. With
further technology development, the AGVs have become even more sophisticated
and are based on laser technology, and they are also called LGV (laser-guided vehi-
cle). The present-day AGVs have the capability of communicating with other robotic
machines in a warehouse in ensuring the smoother and safer movement of products
for storage or shipping to their intended destination.
5.2.4 AUTONOMOUS MOBILE ROBOTS
AMRs are the latest and advanced version of AGVs. They are much more
soph isticated and efficient. They are faster, smarter, and easier to set up. They are a
form of AGVs. They can work without any supporting infrastructure like precisely
located laser targets or wires or magnets implanted in the floor. They have map-
ping and obstacle avoidance capability with a human–robot interface. They consist
of powerful artificial intelligence (AI) technology-based laser sensors, sophisti-
cated camera systems, and computer hardware, which allow them to operate and
navigate dynamically using a map by understanding their surrounding of opera-
tion. They are not restricted to fixed routes; rather, they can plan and replan their
paths, and travel faster. The AI technology converts AMRs into smart devices that
can identify and react to other machines, forklifts, cars, people and other mate-
rial handling equipment, and can work safely in their busy environment.(Fuchs,
Haddadin, Keller, Parusel, Kolb, & Suppa, 2010) AMRs even have advanced capa-
bilities like following a specific person wherever they go. These newer systems are
cos t-effective and scalable for future expansions. Some of the organizations provid-
ing AMR solutions in the market include Veridian, Fetch Robotics, AGVNetworks,
and Quicktron.
AMRs can majorly be of two types:
Based on fleet management – AMRs deal with bigger loads. They route the
robots from a starting point to an endpoint.
Based on picking optimization – robots under this category try to increase
picking throughput by assimilating the movement of people and machines
working in a process flow design. This type supports smaller loads as it sup-
ports picking to cartons and totes.
“The pick optimization segment, driven by the growth of e-commerce, is by far
the faster-growing segment” (Futch, 2019). Two of the renowned suppliers of this
technology are 6 River Systems and Locus Robotics. AGCs engage carts underneath
and transport them safely. The load remains above or on the AGC; for this reason,
AGCs are also called lurking AGV, under cart AGV, AGV transfer cart, or tunnel-
type AGVs.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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70 Transforming Management Using Artificial Intelligence Techniques
5.2.5 ARTICULATED ROBOTIC ARMS
Robotic arm is one of the most important technology solutions that can be used in
warehouses, especially with high product movement as in the case of e-co mmerce-
based ventures. These arms are the standalone arms that are typically used in case of
repetitive tasks. They follow the predetermined movements for specifically located
objects. They have onboard controllers, sensors, or translators through which – based
on their speed, position, direction, and distance – their working is controlled. This
allows them to be sensitive to and interact with their environment. In case of complex
situations such as when object position is not predetermined, the sensor processing
of robotics arms additionally collaborates with AI and machine vision to identify the
object position and further control the movement of the arm. Robotics arms are also
called the joint robot manipulators or even full robots.
Robotic arms are of many different types, but based on their mechanical structure,
they are categorized into four major categories.
Cartesian robots are also known as Gantry robots having three joints with the
standard X-Y-Z Cartesian axes.
Cylindrical arms do not have a specified number of joints; rather, they operate
on a rotating cylindrical axis on one fixed rod.
Spherical (polar) arms are those arms having joints that allow full rotation
spherically.
SCARA robots are used for “pick-and-place” work. They consist of two paral-
lel rotary joints that allow full movement through a plane.
5.2.6 AUTOMATED GUIDED CARTS
AGC is the most cost-effective solution, which is in demand these days especially by
small- and mid-sized manufacturers in various industries where the material han-
dling is at a smaller scale. AGCs, also called SmartCart, is a flexible version of
AGV, which is based on following the magnetic tape. Magnetic tapes are the easiest
as well as faster to install or modify the guide path of AGVs as compared to laser-
guided AGV systems (Rebecca, 2014). Therefore, the SmartCart AGCs are auto-
matic, durable, and reliable, and at the same time, they are cheaper and easier to
install. Therefore, for small manufacturers, AGCs are appropriate for point-to-point
moving and transporting goods in a warehouse and plant.
AGC like AGVs are driverless transport vehicles that can carry goods between
two or more picking stations on a determined route. SPS controls the load for AGCs.
Small companies are buying AGCs to increase the floor efficiency and reduce their
operating costs.
5.3 CONCLUSION
As online sales and e-commerce grow worldwide so is the demand for faster
movement of products through the supply chain. The ever-increasing demands of
customers for faster fulfillment of orders along with the severe competition and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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71Automation and Robotics in Warehouse Management
the growth of newer business models have pushed the companies, especially the
e- commerce companies, to look for innovative technology solutions to cater to their
c ustomers. This leads to a growth of a new generation of technology solutions, which
are intelligent, smart, autonomous, and mobile robots which can be useful in their
supply chains and especially their warehouses. These solutions are helping these
companies to rapidly expand and meet the increased demand while ensuring lower
operating costs, stay competitive, and further manage the worker shortages and effi-
ciency. There is a huge increasing demand for logistics robots. “A recent report by
Tractica Research estimates that the worldwide sales of warehousing and logistics
robots will reach $22.4 billion by the end of 2021, with robot unit shipments reaching
620,000 units per year by 2021. There are more than 50 existing and emerging firms
vying for customers within this space. An estimated amount of $10.34 billion revenue
will be generated by the global warehouse robotics market by 2020” (Bogue, 2018).
This reflects that robotics-based modern logistics is the future of supply chains
across the globe. By automating the basic functions of product movements in
a ssembly chain, manufacturing plant or a warehouse, the organizations can handle
product movement much more effectively and efficiently removing the possibility of
human error. “Mobile robotics solutions reduce the probability of a product being
improperly received, stored, transferred, picked, packed, or shipped to the customer”
(Barks 2017). These robots make life and businesses stress-free and harmless.
At the end of the day, the implementation of automation to the warehouses needs
not to be an upfront one-time investment as one goes. Progressive adoption of
automation to update the current warehouse system can be a viable option (Starship,
2018). With a well-planned integration of appropriate equipment, e-retailers can have
a complete warehouse system suitable for their operation, addressing even complex
picking challenges.
REFERENCES
Agrawal, R. 2020, “Technologies for Handling Big Data.” In Eds. Fausto Pedro Garcia Marquez, Handbook of Research on Big Data Clustering and Machine Learning, pp. 34–49. Hershey, PA: IGI Global.
Barks, C. 2017, Caution: Robot crossing. A show with robots so advanced, when they dance, they ‘do the human, Electrical Apparatus, May 2017.
Bogue, R. 2018, Growth in e-commerce boosts innovation in the warehouse robot market, The Industrial Robot, vol. 43, no. 6, pp. 583–587.
Da Silveira, G. J. C. & Cagliano, R. 2006, The relationship between inter-organizational information systems and operations performance, International Journal of Operations and Production Management, vol. 26, no. 3, pp. 232–253.
Deutsche Post DHL Group. 2016, Robotics in logistics: A DPDHL Perspective on implications and use cases for the logistics industry, DHL Trend Research, DHL Customer Solutions, and Innovation, viewed December 18, 2019, https://www.dhl.com/content/dam/ downloads/g0/about_us/logistics_insights/dhl_trendreport_robotics.pdf
Diaknov, R. & Kuffner, J. 2008, Openrave: A planning architecture for autonomous r obotics, Robotics Institute and Technology, viewed December 15, 2019, https://pdfs. semanticscholar.org/c28d/3dc33b629916a306cc58cbff05dcd632d42d.pdf
Donna, T. 2015, Meet amazon’s busiest employee – The kiva Robot, viewed October 15, 2019, https://www.digitalpulse.pwc.com.au/infographic-evolution-robots-ai/
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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72 Transforming Management Using Artificial Intelligence Techniques
Fuchs, S,, Haddadin, S., Keller, M., Parusel, S., Kolb, A., & Suppa, M. 2010, Cooperative Bin-picking with time-of-flight camera, and impedance controlled DLR Lightweight Robot 3, Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4862–4867.
Futch, M. 2019, Rise of warehouse robotics, viewed November 14, 2019, https://www. mhlnews.com/technology-automation/article/22054632/rise-of-the-warehouse-robots
Kim, E. 2015, Amazon is now using a whole lot more of the robots from the company it bought for $775 million, Business Insider, viewed November 12, 2019, https://www. businessinsider.com.au/amazon-doubled-the-number-of-kiva-robots-2015-10?r=US&IR=T.
Lorentz, H. & Hilmola, O. 2012, Confidence and supply chain disruptions, Journal of Modeling in Management, vol. 7, no. 3, pp. 328–356.
Munford, M. 2015, China, Russia team up on $200 million robotics deal, Forbes Tech, viewed November 4, 2019, https://www.forbes.com/sites/montymunford/2015/04/23/china-russia-team-up-on-200-million-robotics-deal/#24e3a4745ea3.
Nolfi, S. & Floreano, D. 2001, The biology, intelligence, and technology of self-organizing machines, MIT Press, Cambridge, MA.
Rebecca, T. 2014, Will smart robots revolutionize the supply chain?, Retail Week, London.Starship. 2018, Starship Business, viewed November 2. 2019, https://www.starship.xyz/
business/.Zubair Bokhari, S. M. 2019, Zebra’s Warehouse Vision Report-XDIMENSION, viewed
November 25, 2019, http://www.scdigest.com/ontarget/19-06-25-1.php?cid=15617
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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73
6 Intelligent MIS for High-Quality Marketing Decisions
Anshu Goel and Munish TiwariMangalmay Institute of Management & Technology
6.1 INTRODUCTION
The sprawling global market, opened up to transnational competition, liberated from
state regulation, especially the Soviet block, requires an effective marketing strategy
for all the countries in order to succeed in the modern corporate world (Bessen, 1993).
There is a special reference to India and China which not only recognized the need
for these strategic decisions but also have taken proactive actions to implement them
as both share the condition of a giant domestic market. The efficacious company is
one that can trade its standardized product at a reduced price in the international mar-
ket (Green et al., 1983; Levitt, 1983). And that is possible if the efforts of teams are
coordinated across the departments. Strategic decisions taken in an organization are of
precise importance. Few of them are of foremost importance; related human and mon-
etary resources are more vital for the survival of the organization, although few others
CONTENTS
6.1 Introduction 73 ....................................................................................................
6.2 Gaps of MDSS 74 ................................................................................................
6.3 The Significant Attributes of MDSS 76 ..............................................................
6.4 The Differentiated Characteristics of MDSS 76 .................................................
6.5 The Contextual MDSS Communications 77 .......................................................
6.6 Intelligent MKIS Structural Design 78 ...............................................................
6.7 Input Section 79 ...................................................................................................
6.8 Filter 81 ................................................................................................................
6.9 Documentation 81 ................................................................................................
6.10 MKDSS 81 ..........................................................................................................
6.11 Productivity Method 82 .......................................................................................
6.12 Intellectual Structure 82 ......................................................................................
6.13 Information Support 84 .......................................................................................
6.14 Inferences of Intellectual IMKIS 86 ...................................................................
6.15 Conclusion 86 ......................................................................................................
References 87 ................................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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74 Transforming Management Using Artificial Intelligence Techniques
have a lesser significance, involving trivial resources and effects limited by temporality.
Sorting of the choices is done, which is grounded in the criteria of time required to make
one or the other organizational purposes involved and obscures the necessity of the flow
of timely and unbiased information at diverse centers of sale and its sharing among
decision-makers who may use their know-how in diverse markets (Higgins et al., 1991).
The timely accessibility of information is the key asset for progressive organizations.
Besides this advanced technology and innovation to speed up the data entry, also
the money sacrificed on collecting, processing, and disseminating the huge data is
a challenge nowadays. As consumer tastes and preferences are changing rapidly,
the assessment and fulfillment of these are possible with the use of technological
a ssistance (Eisenhart, 1990; McKenna, 1990). The marketing-related information,
when combined with computer-assisted technology and an aware and well-informed
managerial stratum that believes in cost management as well as cost efficiency,
will yield a system that enables the dynamics of marketing information (Schmidt,
1993). The application of MDSS (marketing decision support system) is critical as a
decision-making tool and becomes an integral part of their planning tool, and imple-
mentation leads to success in the market. The cluttered information assortment and
its systematization of the use of artificial intelligence (AI) can be a boon to the mar-
keting information. Companies are using MDSS to combat the challenges of product
line extension issues, profit margin-related issues, growth rate-related issues, costing
and pricing-related issues, monitoring and controlling of product management issues,
market coverage-related issues, and pioneer entrepreneurial inventiveness-related
issues, as well as to tackle the enormous pressure of competitors. For example,
companies such as Toyota, Starbucks, H&M, The Campbell Soup, Google and Walt
Disney, Microsoft, and PepsiCo were among those which have taken initiatives and
implemented sophisticated knowledge and technological know-how.
Marketing managers use IT for purposes such as automated data exchange, mar-
keting decision support arrangement, and innovative database systems, for exploring
and utilizing the information source (Pathak & Agrawal, 2009). Toyota has a well-
known decision support structure to handle the hitches of manufacturing as well as
marketing as functional in-house-produced CAD/CAM systems; moreover, universal
standardization of their professional application arrangements achieve a robust usage
of data (Carlyle, 1988). IT structure by way of IC network systems persisted and
is also universally standardized but altered to TCP/IP-based systems in Japan and
overseas (75 years of Toyota).
6.2 GAPS OF MDSS
The information available to marketing personnel is not valuable until and unless
it has been processed and the information is available for a shorter period, but if
administered properly, it can be extremely beneficial (Cox & Good, 1967). The ele-
ments of MDSS are database acquisition, data management system, graphical repre-
sentation, statistical and analytical models, and finally retrieval (Barabba, 1983). All
successful companies listed in Fortune 1000 firms are using MDSS for their market-
ing decisions (McLeod & Rogers’s, 1985). The management collects, analyzes, and
evaluates information about the company’s customers, environment, competitors,
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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75High-Quality Marketing Decisions
intermediaries, and sales force. All of the former information is synchronized and analyzed to understand the events: what is happening in the market and what could happen in the future. Even though systems are heavily loaded with information, they do not serve the purpose of management, and sometimes an exaggerated or devas-tated picture portrays the potential consumer.
Mossman also supplied the modular database system to generate a multidimen-sional segment report to facilitate the user-defined structures and operations, which can be further used by specific units of organization, and all the executives can use the same set of information in MDB at different decisional points (Crissy & Mossman, 1977). On retreat literature review, an imperative shortcoming is found that MDSS has inadequate quantitative models (McLeod, 1990). The reason behind as being pro-quantitative is that it can deal with the situations and problems which had complete information only. And it cannot reproduce any solution and alternatives if permutation and combination of this information are applied. A coordinate marketing mix is misleading in that case (Schmidt, 1993). The application of expert system AI to resolve the concurrent issues and present a more translucent and articu-lated picture is a vesting strength to MDSS. Expert systems are the ways of AI that utilizes the human expertise, which has clear guidelines what to be done in precise circumstances based on the defined guidelines or experience and finally reaches on a conclusive decision or appropriate recommendations (Kirsh, 1992).
There is a wide usage of expert systems on the launch of a new product or service, media management, and copy appraisal. Expert systems can also be utilized for both retail and industrial consumers on their promotional tools’ telemarketing or mail system (Schwoerer & Frappa, 1986). The use of the expert system is the option avail-able for the shortcomings of quantification (Kastiel, 1987). The expert system cannot
A study showed that sometimes organizations place more trust in the feedback of their workforce in comparison with the formal network (Evans & Schlacter, 1985). But to understand the acumens of management issues is the prerequisite; filtering will contribute an ineffective use of MDSS as an effective marketing tool. Filtering will lead to an inability
• To match the expectations of prospects• To reduce environmental uncertainty• To provide the solutions with the aid of consolidated information on the
issues on market entry with new products• To modify product and service propositions to convert them into apt solu-
tions to evolving consumer’s needs• To add new product lines, or either to launch new product or venture to keep
satisfied the customers and to retain the long-term relationships with them.
MDSS’ access to the pool of information applies the relevant tools and methods with the relevant software and hardware packages (Little, 1984). The popular models and software are BRANDAID (John D.C. Little), which incorporates the impact of advertising, promo-tion, price, personal selling, and retail distribution on the sales. Besides this, CALLPLAN, DETAILER, GEOLINE, PROMOTER, MEDIAC, ADCAD, and COVERSTORY are the popular software packages applied in the processing of marketing information.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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76 Transforming Management Using Artificial Intelligence Techniques
be applied for semidesigned assignments as in the case of new introduction screen-
ing, owing to the limitations of well-acknowledged specialists (Durkin, 1994). The
well-executed problems within certain boundaries can only be picked and modeled.
The expert system is restricted to a fixed setting or domain area and cannot work
beyond this, and a significant limitation is that it is bounded step-by-step, and noth-
ing could be concluded while deviating the sets of steps (Barr et al., 1981).
6.3 THE SIGNIFICANT ATTRIBUTES OF MDSS
It is well attributed that how much this tool will help in management is still a matter
of debate. And modern business activities need more articulated and integrated mod-
els, and nonquantifiable situations need to be presented well. We endorse a smart and
intelligent MDSS, which will be the extension of the basic models applied; apply the
AI, which will make the marketing manager robust as the direct application of online
data; and can produce immediate reports on the product, which will enhance their
performance by requisite reports related to sales, market coverage, promotional tools
application, their outputs, their know-how distribution, and the maximum possible
permutation and combinations of the marketing, which mix the image or position
of the product among the relative counterparts and are also applied to competitors.
MDSS performance can be improvised in terms of customer profiling, sales budget-
ing, sales anticipation and outcomes, competitor analysis, and stocks’ requirements
for meeting both the real and volatile demand (Higby & Farah, 1991). This will not
only handle the specific situations but also assist in routine decisions and provide
guidance by generating specific reports which are based on knowledge information.
It will resolve the situations and what to do stages more efficiently and accurately and
will empower the expertise with requisite support.
6.4 THE DIFFERENTIATED CHARACTERISTICS OF MDSS
The specialist scheme or the assessment support method can be utilized at any stage.
Marketing circumstances need an incorporated model, which directs to interdependence
in recent industry background which marketing decision support system (MKDSS) is
incapable to make available. The requirement design of “soft” is applied in nonquan-
tifiable circumstance (Durkin, 1994). Environmental structures are not efficient since
the viewpoint is objective for a distinct utilization and their incapability to purpose the
exterior of the vicinity. There is a thought of intellectual MDSS as a method of assisting
marketing judgment building. This is an addition of MKIS-MKDSS thought in which
it utilizes the methods of information illustration as of the artificial intellect meadow.
An MKIS has equal capacities as of MKIS-MKDSS. This supports marketing persons
a contact to operational databases besides extra description for decision building, such
as competition, promotion, product performance, distribution, product mix positioning,
market segment sales, sales, and promotion. The database and reports are based on the
thought of IMKIS as a division of an accessible marketing structure.
The MDSS competencies are for resolving the semistructured difficulties with the
assistance of representations for buyer profile investigation, sales estimating, bud-
geting, order dealing out, pricing choices, viable analysis, sales force component
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77High-Quality Marketing Decisions
investigation, stock control, etc. (Higby & Farah, 1991). A differentiated charac-
teristic of MKIS is information-based, which comprises information about several
aspects of marketing and integrated information that is applied for management
decisions. PepsiCo is endorsing its sports drink (Allsport) in isotonic beverage mar-
ket’s product as using celebrities for countrywide network TV commercials through
NBA playoffs, Simultaneously, PepsiCo also addressed delivery through vending
machines and merchandising channels. The information is programmed in the struc-
ture using an apt illustration scheme. The MDSS essentials such as databases and
reports were taking care of regular info necessities. The MKDSS manages regular
decisions, while the knowledge-based aspect manages the condition requested by
specified expertise. If the vivacious aspects of marketing conditions are taken care
of by the knowledge base, it would serve as a robust decision assistance through
expertise.
6.5 THE CONTEXTUAL MDSS COMMUNICATIONS
Marketing is an ongoing progression in which new decisions and their impact are
monitored unceasingly. Marketing executives take corrective actions for unpredicted
outcomes and also develop strategies when market situations are modified (Levitt,
1983; Kotler, 1988). All this requires access to information about the marketplace and
its features, as of competitors, potential customers, and how they are performing in the
respective segment. This should be presented in the MDSS design, specifically their
database and presentation of reports. Marketing executives also have other sources of
information like merchandise administration groups, which sort the info about their
buyers and merchandise segments. Keeping an eye on this set of information, they
can come up with a new strategy for national and global markets. The executives have
to consider factors such as pricing, placing, promoting, and delivery of merchandise
for subsequent tactical strategies for identifying robust interrelationships. Marketing
executives also have to consider the division costs concerning rational expenses. For
MDSS to be powerful, these relationships should be replicated through its strategy.
PepsiCo portrays a well-known firm in the soft drink (beverages, syrups, or
conc entrates), snacks, foods, and restaurants (distribution, carry-out, and full
ser vice professional areas). It has been effective with its aggressive promotion, cost
management, ads, and distribution management. PepsiCo came to the market in the
1990s and challenged with intense competition for market coverage through new
product introduction, ad battles, and price discounts. Due to a strict competition and
to be proven, PepsiCo – the top customer product company – brought new product
introduction, intensive advertisements, cost cutting, and sales promotion; moreover,
it enhanced its distribution channels. It also utilizes its expertise from its other effec-
tive divisions such as Pepsi Cola and Frito-Lay. Wayne Calloway, CEO, believes in
the rotation of employees from Frito-Lay to others. Although the new person takes a
much longer period to get adjusted with new cultures, they should also need support
as of the previous one.
Figure 6.1 shows the numerous groups with which the marketing executive deals
with. These groups include domestic planning, online databases, public information,
merchandise/cost management, promotion, distribution, and global planning. It was
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78 Transforming Management Using Artificial Intelligence Techniques
reflected in the context of PepsiCo, which is endorsing its All Sport sports drink in
the isotonic drinks market. In these conditions, marketing executives must know dif-
ferent channels of distribution for the timely delivery of the merchandise to its end
users. It shows the altruism effect on the allocation sector. The cost-effectiveness of
promotions has to be assessed based on yardstick like preceding promotion in olden
times. This defines the effect of the price administration group on the decision of a
marketing executive. Although buyer goods cannot be endorsed lest, they are shaped
by the contribution of an interfunctional team of quality monitoring, manufacturing,
R&D, pricing strategists, and trial marketers. Finally, the marketing executive has to
share the suitable public information about novel products to monetary organizations,
e.g., so that the stockholders are well versed about what happened to their invested
money. The use of endorsement in international markets as of tropical nations is
under the long-term strategy of marketing executives.
So few diverse groups affect the market-related decisions such as national and
global planning, online databases, promotion, product/cost management, delivery,
public information, and price administration. So a marketing executive must deliber-
ate the relationships among these factors before arriving on any tactical decisions
(Dickson, 1994). If they are mirrored in the MDSS (in the “library” or “database”
elements), it can help in the efficacious inclusion of information among different sec-
tions of the organization. Organizations like PepsiCo can effortlessly allocate such
information from the tested and proven division of the company to newer divisions.
6.6 INTELLIGENT MKIS STRUCTURAL DESIGN
Figure 6.2 explains the necessary parts of the planned IMKIS as well as the way in
where they interrelate with each other.
FIGURE 6.1 Framework of a marketing information system. (McLeod and George Schell.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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79High-Quality Marketing Decisions
6.7 INPUT SECTION
The initial structure receives the facts beginning from the inner and outer atmosphere.
The arriving facts start from the inner surroundings similar to the position of stock/
store statements, sale information, consignment and trades order, purchase, etc., and
are stocked up in the record. Sometimes, this data is accessible through Internet dur-
ing electronic information exchange. In Figure 6.3, the initial structure from PepsiCo
comprises deal data starting every one of its good parts such as Pizza Hut, Kentucky
Fried Chicken, Taco Bell, Pepsi, and Frito-Lay. In accumulation, deal data begin-
ning its overseas functions is an element of its initial arrangement. The MKDSS uti-
lizes the record to conduct study. Information obtained through outside surroundings,
FIGURE 6.2 The marketing management process and information flow. (Richard H. Brien,
James E. Stafford, 1968.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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80 Transforming Management Using Artificial Intelligence Techniques
such as new research details, industry details, and information regarding rivalry,
government policies, and stock marketplace during Internet records, are displayed
during a filter, and it is sent electronically to the suitable staff.
FIGURE 6.3 IMKIS relationship.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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81High-Quality Marketing Decisions
6.8 FILTER
It’s a method that differentiates appropriate data from inappropriate data (Malone
et al., 1987). It displays and admits appropriate data by overlooking inappropriate
data. The significance of arriving data is recognized as the requirements of the orga-
nization. However, the data about the requirements of the organization depends on
the objective arrangements of the organization such as market share and growing
deals (Amaravati & Kumar, 1994). Depending on objective arrangements, points are
known for different stages of an organization. Receiving data is sorted out and dif-
ferentiated into dissimilar parts to pertinent people in the shape of electronic dis-
patch communication (Malone et al., 1987). For example, in Pepsi Company, data
significant to rivals similar to Coke (soft drink section), Little Caesars and Domino’s
Pizza (Pizza section), Church’s/Popeye’s fried chicken of this part (Chicken section),
and Chi Chi (Mexican section), as well as Borden & P & G (snack-food segment), is
included into the organization. Moreover, others incorporated are data commencing
from the administration system. International organizations (such as WB, GATT)
provide information concerning customer attentions, lifestyles, flavors, amusement
occasions, etc. For example, executives can utilize CISG, “Europe”, and “drink mix”
as points to scrutinize new CISG rules of Pepsi Company’s soft drink section in
Europe.
6.9 DOCUMENTATION
The significance and fame of modules of descriptions require sustaining a lively folder
of mechanism progression description in the documentation. The mechanized library
keeper keeps a file with diverse administrative and fiscal organizations, significant
information, and inside details through different gatherings. Proper categorization
systems are utilized with the aim that they should be able to be received simply.
The documentation may be questioned about data about fresh measuring replica
or methods applicable to a market analyst who may choose and categorize their
re quirements. This may begin fast admission and transfer of data when needed.
Activates are utilized to move mechanically intermittent statements to dissimilar
people inside and outside the group at specific binding periods. In Pepsi Company,
intermittent information starting from snack-food/soft drinks sections and everyday
information since the restaurant trade sections may be mechanically upheld, reorga-
nized, and ahead to people for the management of administration.
6.10 MKDSS
MKDSS is a coordinated set of information, system, utensils, and methods with the aid
of hardware and software for building market-related choices (Lillis & McIvor, 1984;
Rockart & Morton ,1984),. Administrators cooperate through the method in a query
and reply form, and data got through such meetings and through the outside record is
examined using different forms of the replica to arrive at choice suggestions (Proctor,
1992). The scheme may maintain the “what if” form of the study to permit execu-
tives in order to modify replica limits by a test and mistake procedure. The scheme
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82 Transforming Management Using Artificial Intelligence Techniques
may illustrate text data through the records and exhibit numeric outcomes through
expressive and graphical explanations. MKDSS uses goods mix and allocation rep-
lica; product cost optimization and pricing replica; industrial sales force transaction
and revenue gain forecasting replica; marketplace split alter, replications and interac-
tive replica. Market, promotion, customer, and channel analysis models are a few.
6.11 PRODUCTIVITY METHOD
Executives may interrelate with the structure through a workstation monitor, to ana-
lyze information and show reports. These doubts may be conservative record doubts
(like “What are the sales in the Midwest region for the last quarter?”) or doubts to the
information stand (like “What are some of the advertising strategies used by Frito-
Lay in the past?”). Market-related choice needs interrelated meetings, makes it easy
during a workstation with superior visibility potentiality and a quick reply system
(Figure 6.4).
6.12 INTELLECTUAL STRUCTURE
It includes an information foundation and a conclusion machine. The information
foundation includes details and information regarding the company’s goal and
approach. So the information in an information foundation is revealed in the shape
of frames, systems, or semantic associations (Capon-Bench, 1990). The regulation-
dependent plan explains information in the shape of a sequence of “if-then” situation-
reply declarations. The method is rigid due to the reality, and its regulations are
inter-reliant so it is hard to adjust. The outline-based plan utilizes evidence similar to
an arrangement to confine belongings of things and their association with additional
things. Every outline explains information regarding things by “slots” or character-
istics. Openings are packed with standards, including a car edge among additional
objects; opening meant for “type” and “manufacturer”, used for a Ford Thunderbird,
which includes the standards of “Sports Car” and “Ford”, correspondingly. The
edge-dependent system is in shape for affirmative information – information that is
mainly realistic in life. The semantic system is a set of joints that are associated with
relations to narrate matters.
The associations communicate to openings in the outline-dependent system. For
example, the nodes would be “Car”, “Ford”, and “Sport Car” through “Manufacturer”
and “Type” as associations. The difficulty faced by the market analyst is to change
disjointed tactical information into functional, mechanism practicable information. In
this view, the semantic system has a benefit branching because of its connective envi-
ronment. It links items jointly in a lot similar to the method as human beings perform
(Capon-Bench, 1990). This advancement is preferential, while market-related tactics
are explained by associations and interreliance, which is additional than something
else. The conclusion is that machine utilizes the information and methods describing
ending. While information is symbolized through a semantic system, the conclusion
procedure occupies navigating the scheme to determine associations, e.g., to carry on
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83High-Quality Marketing Decisions
through car example, if the client were inquired about “who manufactured a sports
car?”, the initial thing which will be identified is the “Sports Car” nodule and then
observe if it has any “manufacturer” as its characteristic. Here, if we get any trace
it did not, so we require to find associations of “Sports Car” nodule to know if it
is having “owner” nodule and observe that nodule has “manufacturer” as a connec-
tion. It shows “Sports Car” has a holder nodule, “car”, and it has “manufacturer” as a
characteristic.
It can be concluded that developing Ford Manufacturer’s Sports car inference in
a system is difficult, particularly if the connection kinds are not restricted. We show
FIGURE 6.4 Architecture for IMKIS.
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84 Transforming Management Using Artificial Intelligence Techniques
our information foundation with four link types. The link types among two items are
A and B, which are explained as follows:
1. “IS-A” – A is a kind of B (e.g., a sports car is a kind of car);
2. “HAS-A” – A with a characteristic/belongings, B (e.g., a car is having pace
characteristics);
3. “C-T” – A gives to B (e.g., options are added to the car price);
4. “U-T” – A user strategy/method B (e.g., the automatic system of power
transmission in the car).
These limited connection kinds compel an arrangement on the system, which
pro vides presumption. The option of the link types is an important choice on the side
of the analyzer. Dazzling this ability and instinct in formatting mixed information
collected through promotion executives after the interview are shown in Figure 6.5.
6.13 INFORMATION SUPPORT
It is built up by the semantic system scheme limited toward only the four connection
forms. There are two nodule forms, namely, the objective nodule showing
administration’s objectives (such as productivity, the share of the market, and pro-
ductivity) and the nodules on behalf of changeable (such as testing and promotion).
With this system, the information arrangement is developed for PepsiCo, and its
investments depend on descriptions as of business writing. This has shown in the
chapter “Why HR is going for outsourcing”, which points to Frito-Lay like addi-
tional organizations, which utilized subcontract of employees appointing for good
competence and to decrease expenses. We conclude about the information that “cost
reduction” is a purpose, and the technique utilized by Pepsi Company to get through-
out is subcontracting. In the diagram, “reduce costs” is an objective nodule through
a connection to “outsourcing”, which is a single factor contributing to it. Additional
features include “forward integration” and “layoffs”. These features are revealed as
nodules through C-T connection relating to the objective nodule, “reduce costs”.
Benefits of Information structure:
1. It defines the management goals with the help of objective nodule recog-
nized through dark elliptical in the semantic system. This improves effi-
ciency, reduces charges, and increases sales;
2. The system has a “productivity” node and other factors to improve produc-
tivity by tracing its entire “C-T” link. In the case of PepsiCo, the connected
variables of “productivity nodes” are cost reduction, improvement in train-
ing, and use of information technology;
3. The system has an advertising node through “U-T” links. In the case of
PepsiCo, these nodes are celebrity use, advertisements on major events, and
social support causes.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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85High-Quality Marketing Decisions
FIGURE 6.5 Inner structures.
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86 Transforming Management Using Artificial Intelligence Techniques
The knowledge base can be extended to include additional features of the PepsiCo
approach. Partial completion of IMKIS may depend on the internal business infor-
mation. The power of this move depends upon various types of information that is
changed to a functional matter with part numbers of connection forms. The semantic
system is spontaneous and can provide a communiqué instrument involving analysts
and managers. It gives a tactical choice-building procedure by giving information
about goals and strategies. The same information base could be used toward analyz-
ing challenger actions.
6.14 INFERENCES OF INTELLECTUAL IMKIS
IMKIS is better than the conformist MKIS. It presents conventional MKIS cover-
age and documentation services. It may clean data from online records, depends on
keywords, and informs executives regarding fresh expansions automatically. This
augments the importance of inward data and decreases the data surplus to sell-
ing executives. Activates may be planned to do something while some tendency
is known. Likewise, the output falls under some point or the competitors proclaim
fresh goods. Opportunities and threats from the surroundings can be recognized in
that way. The structure utilizes conventional MKDSS potentialities to know past
information through the goods record and to know which good is in which stage of
PLC. The information foundation differentiates IMKIS with a conservative skill. It
gives sales executives to include proficiency and decision into the organization in a
way to facilitate liberally modified/inquired or collectively by additional executives.
Dissimilar to MKDSS, it can give support below the surroundings of fractional data.
This is frequent in the matter of selling as the data regarding markets, rivals, etc. is
not huge. We developed the knowledge structure of Pepsi Co., but there was no com-
plete knowledge. If the marketing system is there, then other components are to be
included in the information support. This needs skilled analysts and managers who
can implement their strategies.
The maintenance and development of the information have to be cautiously han-
dled (Greco & Hogue, 1990). Organization safety is a main concern as the informa-
tion support shows a proposal of the institute’s approach and may be susceptible
to sabotage or theft. Access to an organization should be cautiously examined and
restricted. There are numerous advantages of a properly planned selling scheme. It
could give the organization an advantage over rivals, whereas concurrently there
would be tumbling prices of physical data compilation and data dispensation. It
would be a superior instrument for encouraging and distributing companies educat-
ing and assisting in relocating this data quick to additional vicinities of the company
that needs it.
6.15 CONCLUSION
IMKIS has the capacity to show something which a market is facing. It might assist
in analyzing item characteristics through evaluating channel, consumer information,
and costing alternatives; generating and examining endorsement procedure; getting
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87High-Quality Marketing Decisions
advice on plans and concepts; and affecting selling campaigns, which are always
interested in manufacturing. The information foundation module of the IMKIS
makes selling strategy to be admitted and publicly very simple. Selling execu-
tives must believe the perspective of connecting AI through conservative MKIS to
include an organization that assists their tactical choice creation. When the technol-
ogy improves, the potential of IMKIS will be exhilarating. The recent enhancement
regarding the data in a faster way and its accomplishment shows the prospect for
impersonal selling and straight selling. This helps the marketer to analyze their items
and make them to obtain instant online comments regarding some items endorse-
ment or alter channel efficiency. The selling executive knows how to maximize busi-
ness travel schedules and sales presentations. The practical actuality is capable of
utilizing data for customer negotiations and deal arrangements. The user-friendliness
of handwriting recognition and voice-activated organization can assist selling execu-
tives to interrelate enhanced data with computer arrangements. Due to the growing
use of an electronic system of disbursement, we have to go to a paperless civilization
where actual occasion events and results become an obligation. It shows a better
belief in IMKIS structure with competition-related benefit if it reacts to and exploits
these growth.
REFERENCES
Amaravadi, C.S. and Kumar, N.K. (1994), “Organizational perspectives on EIS design,” working paper, Western Illinois University, Macomb, IL.
Barabba, V.P. (1983), “Steel axes for stone Age men,” Marketing and the new information/Communication techniques, Harvard Business School 75th Anniversary Colloquium, 2–29 July, Harvard Business School, Boston.
Barr, A, Cohen, P.R. and Feigenbaum, E.A. (1981), The Handbook of Artificial Intelligence, Addison-Wesley, Reading, MA.
Bench-Capon, T.J.M (1990), Knowledge Representation: An Approach to Artificial Intelligence, Academic Press Limited, San Diego, CA.
Bessen, J. (1993), “Riding the marketing information wave,” Harvard Business Review, September/October, pp. 150–160.
Brien, R.H. and Stafford, J.E. (1968), “Marketing information systems: A new dimension for marketing research,” Journal of Marketing, Autumn, Vol. 32 No. 3 pp. 19–23.
Carlyle, R.E. (1988), “Managing information systems at multinationals,” Datamation, March, pp. 54–60.
Cox, D.F. and Good, R.E. (1967), “How to build a marketing information system,” Harvard Business Review, Vol. 45 No. 3, pp. 145–154.
Crissy, W.J.E. and Mossman, F.H. (1977), “Matrix models for marketing planning: An update and expansion,” MSU, Business Topics, Autumn, pp. 17–26.
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support systems, Where’s the payoffs?,” Business Marketing, June, pp. 46–51.Evans, K.R. and Schlacter, J.L. (1985), “The role of sales managers and sales people in
marketing information systems,” Journal of Personal Selling and Sales Management, November, pp. 49–58.
Greco, A. and Hogue, J. (1990), “Developing marketing decision support systems in consumer goods firms,” Journal of Consumer Marketing, Winter, pp. 55–64.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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Green, P.E., Goldberg, S.M., Mahajan, V., and Kedia, P.K. (1983), “A decision support system for developing retail promotional strategy,” Journal of Retailing, Autumn, pp. 116–143.
Higby, M. and Farah, B. (1991), “The status of marketing information systems, decision support systems and expert systems in the marketing functions of US firms,” Journal of Information and Management, Vol. 20, pp. 29–35.
Higgins, L.F., Mcintyre, S.C., and Raine, C.G. (1991), “Design of global marketing information systems,” Journal of Business and Industrial Marketing, Summer-Autumn, pp. 49–58.https://www.toyota-global.com/company/history_of_toyota/75years/data/company_information/personnel/information_systems/business_data_processing_systems.html
Kastiel, D.L. (1987), “Computerized consultants,” Business Marketing, March, pp. 52–74.Kirsh, D. (1992), Foundations of Artificial Intelligence, MIT Press, Cambridge, MA.Kotler, P. (1988), Marketing Management: Planning, Analysis and Control, Prentice-Hall,
Englewood Cliffs, NJ.Levitt, T. (1983), The Marketing Imagination, The Free Press, New York.Lillis, C.M. and McIvor, B.J. (1984), “MDSSs at general Electric: Implications for the
1990s from experiences in the 1970s and 1980s,” Marketing and the New Information/Communications technologies, Harvard Business Review, Colloquium, 26–29 July, Boston.
Little, J.D.C (1984), “Decision support for marketing managers,” Journal of Marketing, Summer, pp. 9–27.
Malone, T.W., Grant, K.R., Brobat, S.A., and Cohen, M.D. (1987), “Intelligent information sharing systems,” Communications of the ACM, May, pp. 390–402.
McKenna, R. (1990), “Marketing is everything,” Harvard Business Review, January-February, pp. 65–79.
McLeod, R. (1990), Information Systems, Macmillan publishing Company, New York.McLeods, R. and Rogers, J. (1985), “Marketing information systems – Current status in fortune
1000 companies,” Journal of Management Information Systems, Spring, pp. 57–75.Pathak, S. and Agrawal, R. (2009), “Design of knowledge based analytical model for
organizational excellence.” International Journal of Knowledge-Based Organizations (IJKBO) Vol 9 No 1, pp. 12–25.
Proctor, R.A. (1992), “Marketing decision support systems: A role for neural networking,” Marketing Intelligence & Planning, Vol. 10 No. 1, pp. 21–26.
Rockart, J. and Morton, S.W. (1984), “Implications of changes in information technology for corporate strategy,” Interfaces, January-February, pp. 84–95.
Schmidt, D. (1993), “Automated production planning: A new solution to the old problem of promotion cost-effectiveness,” Journal of Advertising Research, July-August, pp. RCA-8.
Schwoerer, J. and Frappa, J. (1986), “Artificial intelligence and expert systems: An application for marketing and marketing research,” European Research, Vol. 14 No. 4, pp. 510–514.
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7 Contemporary Trends in Education Transformation Using Artificial Intelligence
Simran Kaur, Nidhi Tandon, and Gurpreet Singh MatharouManav Rachna International Institute of Research and Studies
CONTENTS
7.1 Introduction 90 ....................................................................................................
7.2 Features of Artificial Intelligence 90 ...................................................................
7.3 Growth Rate of AI in Education Industry 91 ......................................................
7.4 The Process of Learning and Teaching 92 ..........................................................
7.5 Computer-Aided Instruction (CAI) vs Artificial Intelligence (AI) 93 ................
7.6 Changes in the Education Industry by AI 94 ......................................................
7.6.1 Improving Administrative Task 94 ..........................................................
7.6.2 Smart Content 95 .....................................................................................
7.6.3 Personalized Learning 95 ........................................................................
7.6.4 Global Learning 95 ..................................................................................
7.6.5 New Efficiencies 95 .................................................................................
7.7 Ways in which AI Is Used in the Education Sector 96 ........................................
7.8 Advantages of AI in Education 97 .......................................................................
7.9 Disadvantages of AI in Education 98 ..................................................................
7.10 Impact of AI on Learning and Education 99 .......................................................
7.10.1 Current Developments in Special Needs Education 99 ...........................
7.10.2 Impact on Cognitive Development 100 ...................................................
7.11 Future of AI in Education 102 .............................................................................
7.12 Conclusion 102 ....................................................................................................
Bibliography 102..........................................................................................................
Web Links 103 ..............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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90 Transforming Management Using Artificial Intelligence Techniques
7.1 INTRODUCTION
India has an acute shortage of teachers at elementary, at secondary, and even at the
higher levels of schools and colleges. There is not only a chronic shortage of faculty
but also the problem of finding a qualified people to fill this gap is more difficult. So
it is felt that the educational system needs a revolutionary technological intervention
to make it more inclusive and accessible for all.
Manmade brainpower (AI (artificial intelligence) is the driving innovative power
of the first half of this century and will change for intents and purposes of each
i ndustry. Manmade consciousness (AI) highlights the improvement and advancement
of robotics, logical/rational judgment, and functioning of people. For the same,
br inging AI to the classrooms in remote locations in India will be the appropriate
solution.
AI is a department of science that deals with helping machines in finding answers
to complicated problems in a greater human-like fashion. This generally includes
borrowing traits from human intelligence and applying them as algorithms in a
p c-friendly way. A greater or less flexible or efficient method may be taken relying
on the requirements established, which impacts how synthetic the clever behavior
appears. AI research development, and structural design, in addition to applications
and policies, enables and ensures that these structures broadly benefit people and
society. As time has taken a U-turn, a custom-designed ecosystem will be provided
via AI to be without difficulty useable by means of the wide range of its clients.
Some of the known examples of AI are ‘Cortana’ by Windows, ‘Siri’ by Apple,
‘Alexa’ by Amazon, etc. These are a few examples of voice recognition systems that
can imitate human intelligence and enhance decision-making ability.
AI has an impact on all major areas, i.e., industries from transportation to finance,
and education is one of the major sectors where the prospect of personalized learning
is becoming a reality (Agrawal and Gupta 2017). AI is making its significant impact
on the education industry-changing traditional and conventional teaching methods.
Using AI, a better analysis of every student is possible and analyses of data give a
clear idea about individual student’s progress on every topic and assist the teacher to
take appropriate action.
7.2 FEATURES OF ARTIFICIAL INTELLIGENCE
Computer-based intelligence can take part in connections from people or then
again different machines, deciphering meaning and planning an accurate reaction.
Manmade intelligence can translate provided data, what’s more, make the fitting to
move to accomplish its ordered objectives. Computer-based intelligence can disguise
new data, what’s more, modify its practices as needs should be amplified its viability.
Simulated intelligence can direct the majority of its basic leadership processes
without the requirement for human input.
These features of AI are follows:
a. Receptive;
b. Conclusive;
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-19 20:32:23.
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91Trends in Education
c. Adaptable;
d. Autonomous.
Challenges faced by AI in education policy implications (Education 2030, pp. 7):
i. Guaranteeing comprehensive and evenhanded utilization of AI in training;
ii. Leveraging AI to upgrade training and learning;
iii. Promoting aptitudes improvement for employments and life in the simu-
lated AI period;
iv. Safeguarding straightforward and auditable utilization of training information.
Dynamic environment innovators have started from a long time to adjust to these
challenges and removed from a fantasy pedagogy world in an extended manner, there
are AI problem solvers in the market who provides relaxation to brain and time,
allowing us to pursue accurate, transformational, and actual student expertise.
Computerized reasoning is a blasting innovative area equipped for modifying
each part of our social cooperation. We are in an era where all the concerned persons
in the field of education will be alleviated of various laborious works.
In education, AI has started creating new educating and learning arrangements
that are currently experiencing testing in various infrastructural settings. The latest
investigation shows that there is a positive influence of AI on education.
7.3 GROWTH RATE OF AI IN EDUCATION INDUSTRY
Education is the most powerful weapon which you can use to change the world.
—Nelson Mandela
AI in education market size was around USD 400 million in 2017 and is expected to
grow at a CAGR of more than 45% from 2018 to 2024.
AI in the education market is picking up footing because of the multiplication
of keen devices and the fast digitalization over the globe (Jalota and Agrawal
2019). AI in education utilizes profound learning, and progressed analytics to
monitor the understudy’s learning procedure, e.g., individual learning pace and
marks acquired in tests. This encourages the understudy to learn at their very
own pace through customized learning and observing in each course offered to
them (Figure 7.1). This additionally upgrades the educators’ information level and
experience (Table 7.1).
In light of advancements, the AI in the education market is divided into AI and
deep learning, and Natural Language Processing (NLP). The AI and deep learning
technology are relied upon to have a bigger market size during the figure time frame.
(Agrawal 2020)
The AI and deep learning technology offer a systematic method to analyze the
advancement of understudies from their performance data. This technology is get-
ting significant for understanding educational examples and recommending changes
(changes to classrooms and educating techniques).
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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92 Transforming Management Using Artificial Intelligence Techniques
In light of segments, the AI in the training market is divided into solutions and ser-
vices. The solution segment is additionally divided into software tools and platforms.
Though, the services segment is grouped into professional services and managed
services. Among solutions, the software tools segment is relied upon to have a bigger
market size during the estimated time frame.
Software tools are utilized in different educational applications to examine the
hidden patterns from students’ information for anticipating the result of different
issues.
7.4 THE PROCESS OF LEARNING AND TEACHING
To learn and teach is the main issue of knowledge-oriented society, and its procedure
to solve can be affecting the future of any country. The optimization technique of
teaching and learning must be adopted, based on the dominant culture and values in
society.
Different models have displayed, including Keller learning model (ARCS)
(Figure 7.2). Keller accepts that inspiration is under the impact of individual,
e cological, specialties, and learning materials. Keller – in his persuasive, educational
structuring, forming hypotheses, and motivational systems with educational
p lanning – structures an application result that makes students accomplish more
struggle to achieve educational objectives of EQ emotional model. This model
of emotion or feeling has a lot of impact on learning (Sam 2009). After making
emot ional remembrance as compared with text reports, the fundamental thought pro-
cess comes to show that the enthusiastic model of humans consists of a positive range
of feelings (happiness, joy, expectation and compassion) and negative range (sadness,
anger, dread frustration, and forcefulness); feeling in the technique of training must
FIGURE 7.1 AI in education market growth. (Global Industry Report 2024.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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93Trends in Education
be in a positive range and ideally should be dynamic, and a few of learning model
should be dealt with care to document objectives.
7.5 COMPUTER-AIDED INSTRUCTION (CAI) VS ARTIFICIAL INTELLIGENCE (AI)
PCs, especially microcomputers, are presented in broad use inside the framework of
the education program. Their use is also diversified in various areas such as giving
instructions, analytical roles, a resource for academics, and administration of data.
The most significant use of computers in education, which has been observed from
the past years, is an instructional role.
TABLE 7.1AI in Education Market Growth
Artificial Intelligence (AI) in Education Market Report Coverage
Report Coverage DetailsBase year: 2017 Market size in 2017: 400 Million (USD)
Historical data
for:
2013–2017 Forecast period: 2018–2024
Forecast period
2018 to 2024
CAGR:
45% 2024 Value projection: 6 Billion (USD)
Pages: 270 Tables, charts & figures: 302
Geographies
covered (16):
The United States, Canada, the United Kingdom, Germany,
France, Italy, Spain, Australia, China, India, Japan, South Korea,
Brazil, Mexico, GCC, South Africa
Segments
covered:
Model, deployment, technology, application, end use, and region
Companies
covered (24):
Amazon Web Services, Blackboard Inc., Blippar, Century Tech Limited,
Cerevrum Inc., CheckiO, Pearson PLC, Volley.com, TrueShelf, Inc., Sofia
Labs LLC, Querium Corporation, Knewton Inc., Cognii Inc., Elemental
Path, Google Inc., Microsoft Corporation, Nuance Communication Inc.,
IBM Corporation, Jenzabar Inc., Content Technologies Inc., Yuguan
Information Technology LLC (Liulishuo), Pixatel System Inc., PLEIQ,
Quantum Adaptive Learning LLC.
Growth drivers: Rising venture capital investment in AI and EdTech
Exponentially developing digital data
The growing integration of ITS in the learning process
Increasing per Trillion (USD)ership with education content providers
Increasing adoption of cloud-based services
Pitfalls and
challenges:
Data safety and security issues
Limitation of ITS
Lack of skilled professionals
Source: Global Industry Report 2024.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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94 Transforming Management Using Artificial Intelligence Techniques
CAI program has been rapidly used in the market, but still it has certain limita-
tions, which are as follows:
i. Incapable to make discussion with the students in their regular language;
ii. Ineffectiveness to interpret the courses being taught and hence no expected
responses to be achieved;
iii. A failure to choose what ought to be instructed straightaway;
iv. Weakness to forecast, analyze, and understand the learners’
misinterpretations;
v. The shortcoming to make changes in the present or upcoming teaching
policy.
Various such concerns have been resolved through AI, such as language, interpretation
of the courses, planning, and modifications in the courses and teaching policy. With
the progress in AI, it has been showered with the power to remove all the capabilities,
weaknesses, failures, and shortcomings in the CAI programs.
7.6 CHANGES IN THE EDUCATION INDUSTRY BY AI
7.6.1 IMPROVING ADMINISTRATIVE TASK
Teachers make investments a brilliant deal of energy in reviewing tests, com-
paring schoolwork, and giving treasured responses to their students. Innovation
in technology can be utilized to evaluate the assignments where various assess-
ments are included. This implies instructors would have beyond regular time with
FIGURE 7.2 Keller’s motivator didactic model (ARCS).
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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95Trends in Education
their students instead of spending extended durations of time in reviewing their
assignments.
We assume a greater amount of this from AI. In reality, program providers are
coming with higher methods for comparing composed solutions and articles. The
different area that is increasing an outstanding deal from AI is the faculty admission
board. Manmade brainpower is taking into account mechanization of association and
preparation of table work.
7.6.2 SMART CONTENT
As the generation is smart so the content to be dealt with also requires being smart.
Smart content of teaching has already reached each classroom of the school and
the universities, respectively. Customized textbooks are made for certain subjects
through AI systems. Course readings are being digitized, and new learning inter-
faces are being made to help students, trainees, learners, and evaluators of every
single scholastic evaluation and ages.
7.6.3 PERSONALIZED LEARNING
With the support of AI notes, study materials, circulars, and notices are reaching
each and every student. The students who were struggling are now capable to cope
up with their academics. The current system of education is designed in such a way
to suit the maximum number of students. AI enables the teachers to improve their
work efficiency, and greater emphasis can be given by the academicians on their task
which they want to pursue, i.e., to improve the students’ potential.
AI is a powerful source to collect instant feedback from the students which will
help the teachers to make improvements and update their teaching pedagogy.
7.6.4 GLOBAL LEARNING
With the help of AI, boundaries for education have been removed at some points at
the international level. Innovations bring drastic advances in the world by means of
assisting the learning of any route from everywhere in the globe at any time. With
more era upgradations, there will be a more huge scope of guides available on the
Internet and with the help of AI; college students will benefit from any place; i.e.,
they may be inside the global.
7.6.5 NEW EFFICIENCIES
AI improves IT processes such as decreasing traffic jams, improving the security of
students, assigning of seats during programmers, and avoiding wastages. But there
are certain expenses which are to be spent while starting of AI in a system such
as infrastructural requirements, installation charges, the requirement of trainers –
each and every organization that is involved in the education sector, automobile
sector, and food sector wants to install AI systems in order to increase the overall
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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96 Transforming Management Using Artificial Intelligence Techniques
efficiency, which will speed up their routine as well as other tasks and will have a
direct impact on their profit and sustainability, which is an essential feature required
in a competitive world. As the education system is undergoing drastic changes, smart
solution through AI itself encompasses customized ecosystem although it involves
from a simple to complex expert system modeling.
7.7 WAYS IN WHICH AI IS USED IN THE EDUCATION SECTOR
i. Smart content: Today traditional textbooks are converted into smart content,
which is easily made available to every student in the class, and through this
the content is also made interesting.
ii. Intelligent tutoring systems: It provides knowledgeable resources to the stu-
dents accompanied by new learning ways required by the students as per
their choices.
iii. Virtual facilitators and learning environment: Virtual human aids and
facilitation are given by AI for use in an assortment of instructive and help-
ful conditions.
iv. Scheduling appointment with prospective students: Student problems need
to be resolved within a specified time as the administrative task is engaged
in the assigned tasks, and this function can be performed by the hatbox
24 × 7 even the problems of same nature can be classified, which will help
to solve the problems on the scaling of classifications and priority sector.
An AI that makes outgoing calls in an understandable human voice is being
created by Google named as Google Duplex.
v. Payment of fees: In this busy world, time is precious for all and time spent
in the deposition of fees by the students, their parents, and their guardians is
too much. Standing in long queues for the deposition of fees and waiting for
the turn to come is a very tedious process for all the students and their con-
cerned ones. Concerning this, AI is a blessing to one and all as in payment
processing AI can be applied in two stages: one for modular and other for
payment arching system. In the modular stage, AI deals with fraud analysis,
payment updating, and payment validation, whereas in the payment arching
system, AI system advises the best payment gateway for customers keeping
in view various elements such as processing time, payment charges, and
payment gateways.
vi. Online courses: For the upliftment of the students, faculties, trainers, and
so on, the government of India has also started online courses that can be
completed from any corner of the world. It provides convenient examination
modes, live classes, and 24 × 7 academic supports. Separate courses are
being run for students and faculties under the category named as MOOCs
and ARPIT courses. Some of the most commonly run AI and machine-made
programs in India are Texas. There are also PG programs run by various
universities with the help of AI, like AI engineers.
vii. Attendance monitoring: AI also aids in attendance monitoring, which
is a very crucial part of students in the schools as well as colleges.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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97Trends in Education
Through time-to-time messages can also be sent to students if their atten-
dance is below a certain specified level as directed by the authorities speci-
fied by the organization. It also helps in analyzing student attendance, which
can be further intimated to the students, parents, and all other responsible
ones. It also gives the alert to the students whose attendance is less.
viii. Grading: Availability of mark structure helps the AI software to automati-
cally put the grades accordingly. It can give a grade to the essay and article,
and can also make improvement in grade if required. This technology is on
its way to helping the teachers to save time spent on giving grading to the
students’ essay, assignments, and so on, and the time saved can be used for
the teacher–student interaction with each other, thus providing a way out for
ironing the students.
ix. Grade cam reads students’ numeric handwriting: Grade cam reads the stu-
dents’ numeric handwriting on the answer sheet and helps assign scores
to the students. Grade cam can read and provide a score on behalf of the
teacher almost without any error.
x. Works as an information supplier: AI is the buzzword of the day in the
sourcing of information. AI is the best platform through which we can share
and provide updated information such as notes and circulars from time to
time to the concerned parties.
7.8 ADVANTAGES OF AI IN EDUCATION
i. Personalized and customized learning: AI provides personalized learning,
thus helping the student in his/her learning journey, and on the other hand,
it assists the teachers to collect insights about every student, and it teaches
to develop an individualized approach for better teaching. AI systems
can create highly customized textbooks from traditional syllabuses, and
personalized learning interfaces can be created to help students.
ii. Minimize the error: With AI, the probabilities of error are minimal, viz.,
almost nil, thus achieving better accuracy.
iii. Save time and resources: With AI, it is possible to complete work faster
with minimal human resources, and also, there is no need for breaks, etc,
thus saving the essentials of human resources as the job is almost infinite,
as the machines will be able to do everything, and essentially as they don’t
have any boundaries.
iv. Automate basic and routine activities: AI can automate much time- taking
activities for any educational institutions, e.g., grading homework and
tests for large lecture courses. AI systems can be programmed to provide
expertise, platform for students to ask questions and find information or
could replace the teacher’s presence to solve very basic courses/concepts.
v. Can be adapted to student needs: AI-based systems respond as per the
needs of the student, putting greater emphasis on certain topics that
s tudents haven’t mastered, and helping students to work at their own
pace.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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98 Transforming Management Using Artificial Intelligence Techniques
Helpful in feedback: AI can provide feedback based on the monitoring
of students’ progress and send alerts to teachers when there is an issue with
student’s performance. AI systems allow students to get support on their
needs and to teachers to find areas of improvement. ‘AI can point out areas
where courses want to improve. Teachers won’t constantly be aware of gaps
in their lectures and academic materials that may leave college students
pressured about sure concepts. Different college students have different
gaining knowledge of styles, abilities, hobbies, and needs. One trainer in
a study room of 30 students will rarely be capable of cater to every one of
those needs. Homework and classes will be customized totally based on a
scholar profile; hobbies can be cultivated and enhanced by exposing college
students to different publications and content’.
vi. Can change the role of teachers as facilitators: The role and importance
of teachers in education cannot be replaced, but by using AI, the expertise,
knowledge, and experience of the teachers can be used for more fruitful
activities, focusing on students who require their personal attention rather
than engaging them in basic activities, which can be easily replaced by AI
systems.
7.9 DISADVANTAGES OF AI IN EDUCATION
i. Cannot replace the teacher: Any system cannot replace the role and
i mportance of the teacher’s knowledge, expertise, experience, and passion
for delivering the best for their students. The system cannot take the place
of a teacher and also cannot develop the personal bonding between students
and teachers. Robots work by the algorithm that is not influenced by the
emotions and zeal the teacher has for his profession.
ii. Technology addiction: When technology takes place of human being and
the student becomes dependent on systems, it will not only create a negative
impact on the learning behaviors but also the students will be dependent on
the system rather than trying on their own. Their imagination power would
also be affected as for every small problem, they will refer to the system and
gradually will be addicted and become technology addicted.
iii. High cost: In the Indian scenario, where already there are several issues
with availability and accessibility of quality education remote places where
no proper infrastructure is available, most of the budget is used for creation
and maintenance of basic infra and facilities, bringing AI-based system will
be a more costly affair, and many students from the poor financial back-
ground cannot afford.
iv. Unemployment: Already India has been facing the unemployment problem,
and replacing the teachers with AI systems will create more problems.
v. Quality of education: Lack of personal engagement of teachers and replacing
teachers with machines may affect the quality of education among the
s tudents and the lack of personal interaction between teachers and students.
As the teacher offers alternative ways to resolve problems, however, the
machine can offer a standard solution with no alternative variants.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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99Trends in Education
vii. Widens gaps between haves and have not: Using AI systems/learning tools
requires high-end machines and gadgets rich can be afforded by the limited
segment of students and thus will increase the gaps between rich and poor
students.
viii. No proper evidence: Impact of AI in completely changing the traditional
education system needs much more study to arrive on any ideal model for
Indian scenario. There are still many questions that are unanswered with
respect to the use of virtual assistants, affordability of students/parents in
investing and adopting AI systems, capabilities of teachers to supervise the
students learning from machines, etc.
7.10 IMPACT OF AI ON LEARNING AND EDUCATION
From the early 1980s until recently, a lot of differences have taken place in the
teaching methodology. Educational uses of AI have fundamentally centered on the
information-based approach. The most noticeable area of research has been an intel-
ligent tutoring system. This system pertains to information-based network hierarchy.
This network-based hierarchy explains that the educational applications of AI have
focused primarily on the knowledge-based approach. This professional system or
instructive model deals with the friendly acquaintance of learning material to the
student through a versatile and interactive user interface.
Since student performance and adaptability can also be observed and kept in
the record in detail in its information technology ecosystem, intelligent tutoring
environments have also served an important platform for providing the data for
learning research. The problem in developing intelligent tutoring system (ITS) for
broad learning domains has moved the attention to more specific problems such
as usage of AI and machine learning to develop instructor interfaces for student
and learning tracking, and learning analysis difficulty of developing it for broad
areas of learning has also allowed us to focus on the narrower problem of using
AI and automatic learning to generate teacher interfaces for student and learn-
ing to monitor and learning diagnostics. Hence, we can say that the impact of AI
on education is going to be widen day-by-day. AI is going to become more suit-
able through instructor interfaces. AI in education is a sharp U-turn on traditional
teaching methodologies, which are going to speedily overcome the gaps in the
education industry.
7.10.1 CURRENT DEVELOPMENTS IN SPECIAL NEEDS EDUCATION
AI-based procedures have proven potential, e.g., within the early detection of
dyslexia. A well-published instance is the Swedish company ‘Lexplore’, which has
developed a machine that quick searches for college kids at threat and detects dyslexia
by following the eye moves of readers. The machine makes use of data primarily
based pattern recognition, and the society is now growing within the United States
and the United Kingdom, supplying analysis at the college and college district level.
Systems based in AI have additionally been efficaciously advanced for the prognosis
of autism spectrum disorders and attention deficit hyperactivity disorder (ADHD).
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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100 Transforming Management Using Artificial Intelligence Techniques
In particular, child–robotic interaction appears to permit for new sorts of diagnostics
and adapted educational applications.
As scholars are trying to outperform an essential role in many training structures,
many projects are attempting to explore the usage of AI for automatic test era and
evaluation. Much of these works are aimed at automating summative evaluation,
with the promise of reducing teacher workload. A feasible unintended outcome of
this work is that high-stakes tests will be increasingly more displaced with the aid
of common low-stakes formative assessment, as the attempt and fee required for
evaluation decrease.
Current AI systems are excellent at combining evidence from loads of complex
records sources and the use of it for the real-time sample recognition. For exam-
ple, pupil assignments may be established and diagnosed incredibly effortlessly by
means of an AI machine that contains records on man’s or woman’s scholar history
and peer responses.
Thus, collected formative opinions could, to a big extent, make high-stakes
t esting redundant. AI is also beginning to be used to diagnose students’ attention,
emotions, and communication dynamics in computer-assisted studying ecosystems,
consisting of course development and management, to generate optimal businesses
for collaborative learning responsibilities and recognize styles that predict losing out.
To try this effectively, big facts sets are wanted for system formation.
As cited above, this is a primary technical bottleneck. Student behavior must also
be actively monitored to offer comments on learning. This creates technical desires
to discreetly display students, e.g., the usage of video processing and faraway eye
tracking, with related ethical and regulatory challenges. Ethically less elaborate are
structures that use less granular records to provide recommendations.
7.10.2 IMPACT ON COGNITIVE DEVELOPMENT
At the basic level, the most important question that arises in mind is the impact of
AI on the development of human cognition and the human brain. The coevolution of
technology and the human spirit go side by side. Recent research on neuroplasticity
has put forth that tools and technology not only assign shape on how we think, but
can also shape the brain itself. This raises the question of how the utilization of AI
advancements in learning changes the structure of the human brain.
In particular, recent research shows that there are vital levels in brain improve-
ment. Cognitive technology can, therefore, have quite fundamental consequences if
they are used during these essential periods. At present, we do no longer understand
whether this is the case in general, AI may be used basically in distinct ways, which
could have different implications for the development of human cognitive talents
in kids and adults. Nevertheless, like two sides of a coin, AI also can have high-
quality and negative effects on cognitive development. First, AI offers the framework
for present capabilities. There turned into a time when skills had been understood
as a combination of domain-unique know-how and behavioral repertoires, AI can
decrease the requirement for human knowledge, and decreasing the performance of
them which impacts stress on the significance of behavioral repertoires.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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101Trends in Education
Subsequently, people don’t need to learn area-specific knowledge which
was required earlier for skillful conduct. Hence, the requirement of domain-
specific knowledge turns out to be less significant in comparison with generic
co mpetencies. The requirement of generic competencies is moderately increasing
day-by-day.
Computer-based intelligence (AI) can accelerate intellectual advancement and
create cognitive abilities that would not be viable without the support of technology.
Computer-based intelligence has made viable all the impossible work, and even the
linger on work has now become a time-bound activity.
AI may also decrease the significance of some human cognitive capabilities, or
cause them to out of date. For instance, as AI can exchange pace to textual content
and another way around, dyslexia may additionally emerge as socially less vast
than it’s been previously. For example, for dyslexia and dyscalculia, AI may also
have clear blessings for people; however, the standard impact isn’t always smooth to
ca lculate. For example, computers help humans in calculations; however, if humans
come to be completely reliant on computational machines, then it can, however,
come to be a very complex task for them to develop such skills, which enables
them to use machines encompassing the solution to more superior mathematical
problems.
Now come on to the smart classes, a new trend in imparting education where a
teacher is replaced by a video. This video is after all prepared by a teacher or a pro-
fessional who gets money for that. This video is replacing thousands of teachers who
are imparting knowledge in classrooms by explaining everything on the blackboard,
fulfilling the queries of the students on the spot – this cannot be done by a smart
class.
So an opinion which is formed is smart classes that are helping students to
become really intelligent but superficially or artificially intelligent. It means the
smart classes are also adversely affecting the cognitive development of the student.
If smart classes can do, what is the logic of opening new schools, colleges, and
institutions? There was a time when students consulted many books, journals, and
their seniors and prepared notes and while answering questions in the examination
would mention the names of the books and the journals consulted. This would fetch
good marks. The student would never forget the notes which had been prepared by
his hard work. This would be enhancing student’s knowledge who could face any
competitive examinations, interview, group discussions, extempore, speech, quiz,
etc.
Now the times have changed. With inventions of many types which science and
technology have given the new generation, the habit of printed books and journals
has been replaced by computers in interest and online gadgets where everything
is available in plenty. There is no need to consult many books and journals; today,
readymade study material is made available to you. Various information is presented
on a single platform. Whether readymade information is making the students intel-
ligent or superficially intelligent? This shows that the readymade information made
available to the student is affecting somehow negatively on cognitive development.
To some extent, it can be said that this is making everyone mediocre.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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102 Transforming Management Using Artificial Intelligence Techniques
7.11 FUTURE OF AI IN EDUCATION
AI is having great significance in today’s world. It is necessary to understand the
impact of AI on future learning in comparison with its impact in the present time.
A comparative analysis of the impact of AI on the present and future learning and
education may provide us with significant details to overcome the weaknesses of AI
in the education sector.
AI providers will provide products that respond to immediate problems. For an
AI start-up in the education sector, it is difficult to offer products and services that
require a change in current educational practices.
Therefore, without clear dreams and goals that place rising technical possibilities
within the wider context of the transformation of education and the future of learning,
educational AI is most possible to bring basic answers to pertaining problems.
AI can, therefore, mechanize and reinvent outdated teaching practices and cause
them to increasingly hard to change. It may consequently be important to develop
suitable visions and guidelines while concurrently growing forward-searching
f ashions for schooling and coaching. It is crucial to create sensible experiments in a
genuine context with teachers and training experts. Given that AI is now high at the
political agenda, it’s far too clean to generate excessive-degree visions of the future
that claim that AI is the next technical revolution.
AI is now generally referred to as ‘new electricity’. It is consequently critical that
teachers, who often war with the concrete needs of everyday coaching exercise and
new initiatives, are not taken aback via this new technology.
7.12 CONCLUSION
There is no doubt about the use of emerging technologies and the advancement of AI
applications in education for different stakeholders such as mentors, trainers, teach-
ers, and educational institutions, which is transforming the education industry in the
country. In comparison with the global scenario, the education sector in India is a
late adopter of AI and machine learning, but the changes will continue.
There are many cases where educational programs powered by AI are helping
s tudents in enhancing their learning skills. It is expected that AI in education has
a vast scope in India, and this will also help in achieving the goal of ‘Education
for All’.
BIBLIOGRAPHY
Jalota, C., and R. Agrawal. “Analysis of Educational Data Mining using Classification.” 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019.
Agrawal, R., & Gupta, N. (2017). Educational data mining review: Teaching enhancement. in S. Tamane, V.K. Solanki, N. Dey (eds.) Privacy and Security Policies in Big Data. Hershey, PA: IGI Global, 149–165.
Agrawal, R. (2020). Technologies for handling big data. in F.P. Garcia Marquez (ed.), Handbook of Research on Big Data Clustering and Machine Learning. Hershey, PA: IGI Global, 34–49.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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103Trends in Education
WEB LINKS
https://discovery.ucl.ac.uk/id/eprint/1474570/1/LesContes-KPP-FINAL.pdfhttps://elearningindustry.com/ai-is-changing-the-education-industry-5-wayshttps://en.unesco.org/news/challenges-and-opportunities-artificial-intelligence-educationhttps://gradecam.com/ 2017/08/artificial-intelligence-grade-papers-faster/http://ijaeit.com/Content/Paper/2019211108100001120192111081000001.pdfhttps://www.indiatoday.in/education-today/featurephilia/storyhttps://www.marketsandmarkets.com/Market-Reports/ai-in-education-markethttps://www.sciencedirect.com/science/article/pii/0898122185900549https://telrp.springeropen.com/articles/10.1186/s41039-017-0062-89abouthttps://www.virtusa.com/perspective/application-of-artificial-intelligence-in-payment-
processing/
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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8 Artificial Intelligence and Personalized Banking
Sonam Rani, Mukul Gupta, and Deepa GuptaGL Bajaj Institute of Management & Technology
CONTENTS
8.1 Introduction of Artificial Intelligence 106...........................................................
8.1.1 At the Beginning of the Industrial Revolution 106 .................................
8.1.2 The Advent of AI Banking in India 107 ..................................................
8.2 Personalized Banking 108 ...................................................................................
8.2.1 Not Just Customer Support 109 ...............................................................
8.2.2 Not without Challenges 109 ....................................................................
8.3 Role of Artificial Intelligence in Banking 110 ....................................................
8.4 Improving the Customer Experience with AI 111 ..............................................
8.5 AI Today: Where It Works and What For 112 ....................................................
8.5.1 AI and Credit Decisions 113 ...................................................................
8.5.2 AI and Risk Management 113 .................................................................
8.5.3 AI and Fraud Preventions 113 .................................................................
8.5.4 AI and Trading 114 ..................................................................................
8.5.5 AI and Personalized Banking 114 ...........................................................
8.5.6 AI and Process Automation 114 ..............................................................
8.6 How Artificial Intelligence Changes the Banking System 114 ...........................
8.6.1 Artificial Intelligence – A Boon to the Banking Industry 114 ................
8.6.2 Artificial Intelligence – All You Need to Know 115...............................
8.7 The Role of Banking Industry 115 ......................................................................
8.8 Artificial Intelligence in Banking Sector 115 .....................................................
8.8.1 Benefits of Artificial Intelligence in Banking 115 ..................................
8.9 The Future of Artificial Intelligence in Banking 116..........................................
8.10 Application of Artificial Intelligence in Banking 117 .........................................
8.10.1 Personalized Financial Guidance 117 .....................................................
8.10.2 Digital Wallets 117 ..................................................................................
8.10.3 Interactive Voice Response System (IVRS) 117 .....................................
8.11 How AI Enhance Customer Service 118 .............................................................
8.11.1 AI-Enhanced In-person Interaction 118 ..................................................
8.12 Benefits of AI for Banking Sector 119 ................................................................
8.13 The Rise of AI in Banking 119 ...........................................................................
8.14 Integration of AI in Mobile Apps for Banks 120 ................................................
8.15 How AI Enhances Banking Services 120 ...........................................................
8.16 Benefits of AI for Banking Sector 120 ................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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106 Transforming Management Using Artificial Intelligence Techniques
8.1 INTRODUCTION OF ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) is used in our daily activities of life, it’s everywhere,
and it’s become an important part of our life. It gives power to so many
app lications, including adjusting facial expression to translate a sentence in differ-
ent languages, e.g., Siri and Alexa. Moreover, there are many applications in the
market, according to the need of customers and companies. AI has the power to
increase the productivity and efficiency of industries. AI creates innovation across
the sectors of industry.
Nowadays, AI has proved that it plays significant roles than the Internet. Many
people believe that AI has the potential to be more reliable and significant than the
Internet. AI helps in supporting an extensive data and showing an extensive improve-
ment in engineering skills, blenches with deep learning, and gives an immense
impact across various dimensions of human life.
We all are surrounded by AI, and AI does our daily chores and makes our life
more easy and comfortable, but my question is, “Are we happy with this concept that
work has done by machine and all the tasks are performed by a machine?” The future
acceptance of AI depends on the answer to this question.
8.1.1 AT THE BEGINNING OF THE INDUSTRIAL REVOLUTION
Last time there was a serious discussion about machines that make humans redun-
dant, which was at the beginning of the industrial revolution. Newly invented
machines and industrial engineering principles put forward by F.W. Taylor treated
humans as a replaceable part of an assembly line. No one cared for the men who lost
their jobs to machines, nor the men who worked on those machines. Workers in the
world’s early factories faced long hours of work under extremely unhygienic condi-
tions and mostly lived in slums. This soon resulted in significant resistance to the
introduction of machines and several labor riots.
8.16.1 Reduce Workload 121 ..............................................................................
8.16.2 Accumulate and Analyze Useful Data 121 .............................................
8.16.3 Drive Banking Business 121 ...................................................................
8.16.4 Handle Risk Management 122 ................................................................
8.16.5 Prevent Frauds 122 ..................................................................................
8.16.6 Hedge Fund Management 122.................................................................
8.16.7 Concluding Lines 123 ..............................................................................
8.17 What to Expect in the Future from AI in the Financial Industry 123 .................
8.17.1 Conclusion 123 ........................................................................................
8.18 Hybrid Model of Banking 124 .............................................................................
8.19 The Fallout 124 ....................................................................................................
8.20 FMCG Model of Banking 124............................................................................
8.21 Powering Business Growth 125 ...........................................................................
8.22 Improving Digital Experiences 128 .....................................................................
Weblinks 128.................................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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107Personalized Banking
The government soon intervened to provide the basic rights and protection for
workers. Statutory regulations forced factory owners to set up formal mechanisms
in order to look into workers’ wages and welfare. Several new studies like Elton
Mayo’s Hawthorne Studies debunked Taylor’s Scientific Management approach
toward raising productivity and established that the major drivers of productivity
and motivation were nonmonetary factors. A host of new theories and management
practices emerged that started treating workers as a resource, as well as an asset. This
human-centric approach played a significant role in making the industrial revolution
a success.
AI is at the foundation of the digital transformation process. It takes more than
just technology to work. It requires new talent, new thinking, and the willingness to
reimagine banking.
AI is fast evolving as the go-to technology for companies across the world to
personalize the experience for individuals. The technology itself is getting better
and smarter day-by-day, thus allowing more and newer industries to adopt the AI for
various applications. The banking sector is becoming one of the first adopters of AI.
And just like other segments, banks are exploring and implementing the technology
in various ways.
The rudimentary applications of AI include bringing smarter chatbot for customer
service and personalizing services for individuals, and even placing an AI robot for
self-service at banks. Beyond these basic applications, banks can implement the tech-
nology for bringing in more efficiency to their back-office, and even reduce fraud and
security risks.
Unsurprisingly, research firms are bullish on the potential of AI in banking.
8.1.2 THE ADVENT OF AI BANKING IN INDIA
As per the latest report of Accenture banking technology, more than 80% of bank-
ers have felt that AI and humans go hand-in-hand at the workplace in the coming
years. “93% of bankers in India said they increasingly use data to drive critical and
automated decision-making. More partner-supplied customer data means a higher
degree of responsibility for banks. Yet, 77% of Indian bankers agree that most firms
are not prepared to confront impending waves of corrupted insights from falsified
data”, said the report.
“AI is not new to India. Research institutions and universities have been working
with various AI technologies for decades, and especially in the area of social trans-
formation. With enabling technologies becoming a lot more accessible and inexpen-
sive, AI is now becoming mainstream, with large enterprises and start-ups looking at
different opportunities. Our research shows that the adoption of AI has the potential
to add nearly $1 trillion to the Indian economy in 2035. AI adoption is still in its
nascent stages, and a lot more needs to be done to realize its full potential”, said
officer of Accenture.
By introducing AI in banking at a different level, banks can work more effectively
and more positively, and AI has also improved the experience and loyalty of the cus-
tomers. The customer is not getting more personalized and customized services and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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108 Transforming Management Using Artificial Intelligence Techniques
products that help the banking industry to achieve profit targets as well. Many other industries are planning to induce AI in their businesses, while the financial industries are now planning to make a big investment in technology upgradation, as per the reports – approximately 40% of the industries are ready to invest and around 75% are planning to invest in the future.
8.2 PERSONALIZED BANKING
A better way to shine and rule the customer’s heart is to apply AI in order to analyze the behavior of the customer with zero error (Figure 8.1).
FIGURE 8.1 Process to record and analyze the human emotion.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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109Personalized Banking
In the banking sector, AI brings transformation in banking services or we can say
that in the front desk jobs. With the help of chatbots, the front desk jobs are easier
and relaxing because the maximum number of customers get their answer by using
smart chatbots. Chatbots are devices that have all customized questions with answers
and give information to the customer. With the help of these smart chatbots, a cus-
tomer can get the answer to the questions related to payment information and recent
account activity.
There are a number of applications available in the market by different banks,
which helps their customer to select the best financial plan or to book a ticket of the
plan. It is a piece of cake because of smart technology to give personalized services.
This intelligent system tracks the income, pattern of investment, and behavior for the
product of a customer, and gives a solution for them.
There are many bog banks in India; they launch an application that gives a
reminder to customers to pay their dues such as electricity bills and shopping bills,
and so it gives them a message when the transaction has been done.
8.2.1 NOT JUST CUSTOMER SUPPORT
Many Indian banks are now started to use smart technology in the banking system in
order to make their customer delivery more accurate and perfect so that the customer
gets better service and they will get more customers. In this smart path, Canara bank
launched robots at some of its branch offices for customer handling, and following
Canara bank, the private banks such as ICICI and HDFC also had introduced chat-
bots for customer queries. Also, State Bank of India– the largest bank and the oldest
bank of India– gives the idea to implement block chain in the banking system in
order to make banking more secure and safe.
AI is not limited to the banking sector, and it is also used by payment companies
to give customized services and offers to their customers so that they can make cus-
tomers comfortable and also get their loyalty.
8.2.2 NOT WITHOUT CHALLENGES
A wide implementation of high-end technology like AI in India is not going to be
without challenges. From the lack of a credible and quality data to India’s diverse
language set, experts believe that several challenges exist for the Indian banking
sector using AI.
According to Accenture’s Rishi Aurora, “A key challenge is the availability of
the right data. Data is the lifeblood of AI, and any vulnerability arising from unveri-
fied information is a serious concern for businesses. Imagine, for example, the risks
that could arise from KYC compliance AI systems if the data sources are incorrect.
Or consider the efficacy of a fraud detection AI system without the right kind of
data. Structured mechanisms for collecting, validating, standardizing, correlating,
archiving and distributing AI relevant data is crucial”.
Abhay Pendse echoes the sentiment stating, “India has 150+ languages with the siz-
able spoken population. Applications that use speech-to-text or text-to-speech systems
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110 Transforming Management Using Artificial Intelligence Techniques
rely on natural language processing (NLP) libraries and techniques. Banks can use
the existing technologies to start with to support some major Indian languages, but to
effectively reach out to a wider population in India, much more progress is required
on NLP front”.
“Data access and data privacy is a central aspect of any AI work banks do. These
aspects will be of paramount importance with the introduction of regulations in
Europe such as GDPR (General Data Protection Regulation). GDPR is currently
applicable to European citizens, but India and other countries have their data privacy
regulations. Banks in India will have to build AI systems with GDPR and similar
privacy regulations in mind”, he said.
Experts have also stressed the need for more skilled engineers to drive the segment.
“The biggest challenge is the scarcity of trained human resources; the existing
workforce is not familiar with the latest tools and applications. Secondly, AI
t echnology is a big threat to redundant employees in the banking sector. The
mass adoption of AI may cause a grave unemployment problem in the sector”,
said Rachit Chawla, CEO of Finway Capital, a Delhi-based nonbanking financial
company.
“One of the important challenges that are faced by industry and not just banks in
India is the unavailability of people with right data science skills. With only a small
number of good data scientists available to do AI work, the industry needs to work
with universities in India to develop skilled data scientists as well as develop in-house
training programs to train employees on data science skills. Also, identification of
right use cases for AI implementation with the help of domain experts and data scien-
tists can help banks in the successful implementation of AI technologies for banking
functions”, Abhay stressed.
8.3 ROLE OF ARTIFICIAL INTELLIGENCE IN BANKING
Business functions have been redefined again because of the involvement of digital
things in the business model. Every industry has many options to change this world
with the help of technology. The banking industry has been witnessed by many
changes. Nowadays, customers love to use technology to do daily chores of their
life–either it is submitted the electricity bill or on the AC by using mobile. We can
say that techno-customization is the new term to use for attracting the customers for
the product (Figures 8.2 and 8.3).
There is much industry that joins hand to use technology and implement the
technology in their business function, and there is much smart application for the
customers to attract and give them the best service that enhances their satisfaction
level too. The banking sector shakes hand with the IT sector to give different
types of facilities to their customers at their door. And the technology makes
banking comfortable and increases the level of the banking business. This is
because we are using AI in banking, and AI changes the life and model of the
banking industry.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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111Personalized Banking
8.4 IMPROVING THE CUSTOMER EXPERIENCE WITH AI
AI gives many advantages to the customers and makes their life easier and hassle-free.
Here, an article published in Forbes by Danial Newman gave five major positive
impacts of AI in human life, being a customer. These areas are as follows:
• Accredited self-service;
• Enhanced customization;
• Provided all-time service availability (24 hrs);
• Life became more hassle-free and lively;
• 24-hr care service.
AI gives new insight to customer service and gives a different level of comfort to
their customers by adding different types of value.
In the banking industry, we have so many other applications such as chatbots
and voice recognition. Chatbots are used in banking to give instant solutions to a
customer query. Chatbots have specified questions in their system and are also used
to answer these questions.
There are number of applications that help to give more personalized services to
the customers and increase the number of loyal customers. The smart app is enabled
to track the income and expenses pattern of individuals and give them a personalized
offer.
FIGURE 8.2 AI application in financial services.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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112 Transforming Management Using Artificial Intelligence Techniques
8.5 AI TODAY: WHERE IT WORKS AND WHAT FOR
For instance, in the tourist sector, to prevent fraudulent access in accounts, we used
AI in the banking sector to maximize sales and minimize the cost. Also, AI helps to
give personalized offers in order to match the requirements of customers related to
dates, expenses, and routes.
In the transportation industry, AI is successfully used in the development of smart
parking and smart control features, which makes driving more adventurous and full
of thrill. These features are already in use in auto-driver cars and vehicles, thus
attracting the customers.
AI is not only limited to one specific area, but also increases the potential of other
areas like education. It acts as a spark for those who are left from education, because
of AI, they can get an education. Online, video classes, and courses are available now
FIGURE 8.3 How AI helps to deliver the customized product to customer.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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113Personalized Banking
that are helpful for those students who eager to learn something new, and there are so
many self-thought web programmers that help you to get knowledge.
AI does a wonderful job in the medical field also. No one thinks that there is a day
when robots would use surgery and diagnose the people. But here this job is done by
robots better than a human being. They can do tests and surgery, and read reports,
with the best care and less chances of error. This is not very expensive so it can be
available at an affordable cost. Robots are used by doctors for assisting them during
any surgery and monitoring purposes.
The increased use of AI in the finance sector shows how faulty it has been chang-
ing the traditional business to smart business mode. So there are some examples of
AI in the finance industry.
8.5.1 AI AND CREDIT DECISIONS
AI sets a new bar in the financial industry by making a comparison on various invest-
ment schemes and also by giving correct and updated information about the potential
buyer, which can be more fruitful and better for decision-making purposes with very
less cost. This helps to give data about the borrower in a very clear manner and better
than the traditional system and helps to differentiate between the high-risk applicants
and good credit score.
Evidence and fact base is another feature of AI that is employed in machine.
Because it cannot be biased like a human being.
Online banks and loan-providing applications used AI to evaluate the various data
related to persons, also analyze creditability, and give a customized solution.
8.5.2 AI AND RISK MANAGEMENT
It is not possible to underestimate the role of AI in business and helps to analyze
the risk and manage the data in a structured form so that any decision can be made
so easily and that it cannot be done by a human without any flaws and in a short
period. So, this is the power of AI that enables the systems to do all data calculations
perfectly.
AI is the most powerful tool in finance sector when we talk about to study the real-
time situation in the market to analyze the future and to predict the market behavior
because it is based on so many variables and changes in all these variables are very
obvious, and this can be done so easily with the help of AI.
8.5.3 AI AND FRAUD PREVENTIONS
From many years, AI did a tremendous job in detecting the frauds and making the
transactions safer, and this is a success of AI in banking and this does not stop here.
AI has a bright future in all areas with a very high success rate.
AI gives a solution to online transactions and gives a high sign of relief to
e-co mmerce business customers by giving them the security to save their online
transactions and prevent hackers. AI is enabled with these features so that it can
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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114 Transforming Management Using Artificial Intelligence Techniques
analyze the pattern behavior of the customer, analyze or detect any unauthorized
access on the account, and order the security alarm and features to active and send
the message to the customer to alert. Banks also use AI to crack and trace the money
laundering activities in the banking industry, and this investment cost related to
investigation reduces approximately 20%.
8.5.4 AI AND TRADING
Data-related investment is known as high-frequency trading. This type of new trad-
ing makes crazy people around the world and stock market too. For this trading
purpose, people rely on AI, which offers multiple benefits.
This smart trading system makes a system to observe and collect the data in two
forms, namely, structured and unstructured. Later on, this system processes this data
with time for customers, which makes faster decision-making and faster transaction.
AI collects data perfectly, analyzes the data, and draws the result accurately.
8.5.5 AI AND PERSONALIZED BANKING
AI gives new insight to customer’s service and gives a different level of comfort
to their customers by adding different types of value. In the banking industry, we
have so many other applications such as chatbots and voice recognition. Chatbots
are used in banking to give instant solutions to a customer query. Chatbots have
specified questions in their system and are also used to answer those questions.
There are number of applications that help to give more personalized services to
the customer and increase the number of the loyalty customer. This smart app is
enabled to track the income and expenses pattern of individuals and give them a
personalized offer.
8.5.6 AI AND PROCESS AUTOMATION
AI helps in making many smart workers in different sectors, and the process starts
automatically, thus helping to increase the productivity of the company too.
AI system helps to collect the data and also analyze the data using so many appli-
cations and good features, and perform this task in a few seconds without any flaws.
This helps to increase the productivity.
8.6 HOW ARTIFICIAL INTELLIGENCE CHANGES THE BANKING SYSTEM
8.6.1 ARTIFICIAL INTELLIGENCE – A BOON TO THE BANKING INDUSTRY
In the era of technology, AI is a growing concept and a new buzzword. Worldwide,
AI is the go-to technology for companies. Now, it is the most discussing topic by
media and people; mostly, media talk shows are about AI and its role and impacts,
so it is something that we should understand and know about it. In this chapter, we
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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115Personalized Banking
will discuss AI and banking, and how AI is used in the banking sector, and the vari-
ous uses and scope of AI in the banking sector. To begin with, let us understand the
meaning of AI.
8.6.2 ARTIFICIAL INTELLIGENCE – ALL YOU NEED TO KNOW
As the name speaks itself, AI has skills to imitate something natural. This skill of
imitating is done with the help of machines and computers. So, the machine does
work as a human mind by making a decision and thinking for itself is called artificial
intelligence. Nowadays, machines use the small computer inside and the machine is
designed in such a way that they can perform many tasks as a human does. There
are many activities such as learning, voice identification, problem-solving, and the
ability to move an object. Therefore, the main focus of AI is to build machines and
applications that can be work like a human. We all are using AI in our daily life in
many ways from morning tonight.
8.7 THE ROLE OF BANKING INDUSTRY
For the development of the financial system, banks play a very important role in
society. Bank acts as a blood for the financial and economic world as it deals in the
transaction for cash, credit, and other financial transactions. Banks always play a
role as a catalyst to motivate the customers for saving and get better interest in the
future. This motivation assists banks to give financial assistance to large companies.
As we know, banks play a significant role in grooming the economy. Each transac-
tion is done through the bank so banks must follow a proper documentation. For this
purpose, banks are using computer systems that store huge data of various customers
and transactions in their database. Banks also provide various services, and many
operations of banks are done through ATM, emails, e-banking, telephonic banking,
and banking. The entire banking system operation is done through computers related
to track any detail of customers and exchange the information. This all is done with
the help of computers and networks, which is possible only because banks use AI.
8.8 ARTIFICIAL INTELLIGENCE IN BANKING SECTOR
AI is the most essential in the banking industry. When interaction happens between
machine learning and humans in the banking industry and the way decision outcome
convinces and encourages customers is all because of AI. The main purpose of using
AI in the banking industry is to ensure that the customers are satisfied and happy
with the services provided by banks, and to understand the demand and needs of
customers.
8.8.1 BENEFITS OF ARTIFICIAL INTELLIGENCE IN BANKING
In the present time, AI plays a crucial role in the banking industry and gives new
wings to banking life are as:
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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116 Transforming Management Using Artificial Intelligence Techniques
• Customer satisfaction:
Banks’ main focus is to make their customers delight and to satisfy their
customer’s expectations. To render the service as per the customer’s expec-
tations, this is the main objective and key factor for banks and companies to
satisfy their customers. Nowadays, to lure the customer, banks started giv-
ing personalized services and products to their customers. AI helps banks
to increase revenue and build a good strong relationship. The introduction
of AI in the banking industry not only helps to gain customer’s satisfaction
but also to maintain a good settle bank.
• Chatbots:
The bot is the short form of robot– a word “add chat” with a bot becomes
a program that automates a chat box that follows a predetermined path.
In banking, chatbots are running using AI. This service is very fruitful in
the banking sector and makes banking service hassle-free because bank
employees are very much engaged in the daily job, and it’s very difficult for
them to present physically for every customer for one-to-one interaction.
Generally, banks’ working time is till late evening, and banks are mostly
closed on weekends and people also get time after late evening and on
weekends. So at this point, chatbots play a very important role in provid-
ing service to customers 24/7. Chatbots are a very effective and efficient
customer service provider. It assists customers to get online balance and
transaction details.
• Detecting Fraud:
Fraud in the banking sector is one of the terrible things among the people
and the banking industry. It’s very difficult for an individual to cope and
recover from the financial loss whenever any unauthorized access occurs.
However, whenever any unauthorized transaction and fraud occur with an
individual, he/she can immediately inform the bank and take necessary
steps to inform the bank on time and then the bank helps individuals to get a
refund so that bank can take necessary action to handle this problem. These
instant steps taken by the bank attract the customers. AI helps the bank to
detect the fraud by analyzing large data, tracking the regular pattern, and
finding out any irregular pattern.
8.9 THE FUTURE OF ARTIFICIAL INTELLIGENCE IN BANKING
Today, we understood the meaning of AI and the advantages for banking of AI, and
we will be seen the more extensive role of AI in the banking industry in the coming
years. According to the development in technology in the AI by now, we are having
a clear understanding about the meaning of AI and implication of AI in our daily
life; thus it makes our life hassle-free. If we analyze the recent developments in
t echnology, we have found that AI is getting smarter every day, and on this basis, we
can say that we will see a strong AI in the future, which helps customers in many
ways.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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117Personalized Banking
Digitization:
To transform data into digits, a process called digitization plays a very crucial
role. With the help of this technology, we easily transform the digit data into the
digital form. The banking industry has so many advantages of using digitization,
which are as follows:
• Helps in effective time utilization for both people and bank;
• Helpful in customer’s delight;
• Helps in the minimization of human errors;
• Helpful in building the customer’s loyalty;
• Helpful in cash flow, i.e., inflow and outflow of cash;
• Helpful in the transaction of money from any place at any time.
8.10 APPLICATION OF ARTIFICIAL INTELLIGENCE IN BANKING
The major applications of AI in banking are suggesting, planning, and giving a finan-
cial advice to the customers, and all are possible by installing AI applications in
banks. This application helps to get faster information about various loan rates and
market progress.
8.10.1 PERSONALIZED FINANCIAL GUIDANCE
For decision-making, a customer or individual needs information related to the current
market situation, and this is a very complex process, but with the help of AI, the customer
makes the faster decision and also gets the suggestion to invest in stocks and bonds.
8.10.2 DIGITAL WALLETS
Innovation in the financial world is the digital wallet. These wallets help individu-
als to buy any product online through computers and mobiles with the help of the
Internet, and this innovation gives a wing to the digital world.
8.10.3 INTERACTIVE VOICE RESPONSE SYSTEM (IVRS)
This is an amazing development in the banking system to answer customer’s ques-
tions. This is an automated computerized voice application that helps the customers
to find the answers to some specific questions. This distinct feature of banking gives
a new insight to customer’s satisfaction and the banking industry too.
Last but not least, AI becomes famous day-by-day, and the banking industry uses
AI in its operation to serve its customer utmost. So we can say that days are not far
when the banking industry explores more AI in its operation and assists its customers
in a better way. Thus, AI has a bright future in the banking industry, and it gives free-
dom to the customer from long queues at the bank. Customers can transfer money
from anywhere to any place by one click. Therefore, the main purpose of AI in bank-
ing is to enhance customer services and make more personalized banking.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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118 Transforming Management Using Artificial Intelligence Techniques
8.11 HOW AI ENHANCE CUSTOMER SERVICE
8.11.1 AI-ENHANCED IN-PERSON INTERACTION
Today we live in the digital world; everyone is fond of digital life and loves to use
such digital things in their life that make life easier and hassle-free. The same bank-
ing also not untouched with the effect of digitalization. Today, we all are very busy in
our life and do not have time to go to banks and do transactions. So here, digitaliza-
tion makes banking easier for customers (Figure 8.4).
However, the customer has this option to do all business transactions by making
simple clicks and using a smart application on their mobile phones, but there are also
some customers who want to go bank physically, do a transaction at the desk, and
thus prefer the traditional banks.
We talk about the role of AI in our personal lives. When it comes to the role of
AI in banking, we have seen so many impacts of AI: it replaces the job of humans in
banking, e.g., chatbots. Chatbots give solutions to their queries and replace customer
care.
The United States of America plan to launch a bank that is fully based on AI,
which means that making payment to give solutions to customer’s problems as well
as everything can be done by machines. This experiment has begun to check the
reliability of AI.
There is one more bank named Nordea in the United States of America, which
introduce a smart system to analyze the customer’s text messages and send these
messages to the concerned department for a solution. This software is supersonic
since they can process thousands of messages in a few seconds and deliver the rapid
results to the customer, thus increasing customer’s satisfaction.
FIGURE 8.4 Presence of AI at different functional levels in banking.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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119Personalized Banking
Certainly, this AI helps to minimize or control the hacking and cybercrime by
analyzing the pattern behavior of the customer and sending them a reminder and
quick message to take the correct action to stop that unwanted access in their account.
8.12 BENEFITS OF AI FOR BANKING SECTOR
This chapter will discuss various solutions where AI can do magic in the banking
world. While each solution is currently in market by at least one large bank, this is a
far cry from broadly deployed. Mercator surveyed large banks and found 93 different
AI solutions deployed in 13 different departments. This chapter discusses less than
10 (bold added):
Fraud detection: Irregularity that is observed in pattern behavior of consum-
ers can be a useful method for the detection and curing of the fraud and
antimoney laundering.
Personal assistance and helpdesk: Nowadays, smart chatbots are used by
banks to increase the efficiency and cost of customer meetings and query
handling.
Avoid risk: Many customized and personal products are offered to customers
in order to avoid the risk and analyze their behavior patterns for the future.
Safety: To prevent unauthorized access, the unethical analysis of data can be
easily tracked down by the help of new security features.
Computerized and machine in back-office processing: Collect the data by
using the latest technology like optical character recognition, which helps to
understand the text data and helps in back-office processing times.
Wealth management for masses: Customized portfolios are handled by bot
consultant for customers by considering lifestyle, risk management, and
return on investment (ROI).
ATMs: Facial recognition used AI skills like a wide understanding that can be
used at ATMs to find out errors and protect the errors.
“Machines are getting smarter globally. Thanks to the thriving AI concept, compa-
nies can make their devices more powerful and ‘intelligent’ to serve their customers
in a better way. Both B2B and B2C businesses have started adopting this revolution-
ary technology as per their scale and size.
However, the penetration of AI in the banking sector is somewhat limited to date.
The distinct datasets and the risk of confidential data are primarily responsible for
the sluggishness of AI integration in the banking system. But then, as the online
banking and mobile banking become increasingly popular as a tool for 24/7 transac-
tion, we can expect that AI will soon take over”.
8.13 THE RISE OF AI IN BANKING
A strong and fast processing needs AI, and AI is a fact of mobile technology, data
availability, and proliferation of open-source software that offers AI to explain a
large scope in the banking industry. However, AI is the blood of banking nowadays.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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120 Transforming Management Using Artificial Intelligence Techniques
AI has been part of the banking system for a long time, but its presence has been
came into notice recently.
Digital personal assistants and chatbots bring revolution in the banking sector
in terms of customer service, i.e., assisting people in performing daily tasks and
communication related to business services. In terms of banking sector, technology
combined with AI is used for improving the services of banking.
8.14 INTEGRATION OF AI IN MOBILE APPS FOR BANKS
Mainly, bank enclose AI and similar technologies at the international level. As per
the research by NBRI, for predictive analysis and voice recognition, many financial
institutes utilize AI – approximately 32% of financial institutes find out that AI is
helpful in this research. We can say that banks and financial institutions use AI to
improve customer satisfaction by making banking more personalized. Young gen-
eration mainly depends on mobile and online banking, or else, we can say that they
prefer more cashless transactions– in that case, the role of AI is to enhance and give
a boost to the banking sector to lure them. There are smart apps that can be used
to trace the consumer behavior and guide them by giving personalized advice and
understanding of saving and expenses.
8.15 HOW AI ENHANCES BANKING SERVICES
The finance and banking sectors have been growing day and night and touch the
new height in today’s economic world. Lakhs of transaction in few minutes are done
per day no matter where are you sitting and what is your place and time around the
world. There are so many new applications that came into picture, which contribute
to the banking system.
Let’s begin with customer assistance. An AI-enabled system boosts customer
service personnel and makes their job more hassle-free. Once the data is collected
from customers’ mobile devices, the AI-enabled mobile banking application ana-
lyzes the information/data via machine language to deflect the customers to the root
of information.
Moreover, to present the show services, offers, and insights in line with the cus-
tomer’s behavior, we used AI and it is very effortless for banking to combine AI with
banking operation and produce AI-enabled features.
With regard to personalized designing, AI can do amazing job by its features. It is
like a piece of cake to engage customers in financial forecasting with AI techniques.
For instance, if a person likes to purchase a home, the banking app suggests the
customer on the current expenditure and income, what will be the budget, and other
information.
8.16 BENEFITS OF AI FOR BANKING SECTOR
AI has a massive future for the banking sector. It brings automation and simplifies
the process. A few noteworthy benefits of AI for the banks are shown in Figure 8.5.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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121Personalized Banking
8.16.1 REDUCE WORKLOAD
For explaining this part, we use the example of chatbots. Chatbots work by responding
to customer queries with the help of AI. They have some specified questions that
customer usually asks and looks for the answers, and at last, they help them to reach
or login bank web page. Many small things can be done by chatbots such as transfer
of money, and opening and closing account.
Chatbots give a better solution to customers rather than a phone call. They
give important links and guidelines to complete the entire process. They connect
immediately and minimize the work pressure of the executives. Customer executives
can work in a limited amount of time; i.e., they can attend only limited person in a
day, but chatbots have no such type of limitations.
8.16.2 ACCUMULATE AND ANALYZE USEFUL DATA
This amazing technology concept is based on data collection and analysis; any
AI-enabled system can do amazing work with the dataset. A personalized banking
mobile app intensifies AI-enabled features that can pile up important data of
customers to enhance the learning process and improve the customer experience.
After pileup and analysis of data, the experience can be more personalized.
Banks can analyze and predict the behaviors of customers by analyzing their
expenditure patterns and making customized investment proposals for users. In the
future, banks can send messages about the new offers and expenses.
8.16.3 DRIVE BANKING BUSINESS
With the help of AI, we can do better wealth and portfolio management. It is a dream
that comes true for those who hate to visit banks personally, and it makes banking
for a customer like a piece of cake. It boosts mobile banking facilities by handling
FIGURE 8.5 Use of AI in banking.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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122 Transforming Management Using Artificial Intelligence Techniques
the basic banking facilities. It is an extra benefit for the customers to get a safe and
healthy transaction by doing a simple click. It helps to know the customer if any
unauthorized access takes place on his account.
Moreover, this app not only provides safety for credit and debit cards by intro-
ducing a card management system but also gives freedom to the customer from an
extensive authentication process if the customer lost their card. This app improves
the banking service and gives new dimensions too.
8.16.4 HANDLE RISK MANAGEMENT
The risk assessment process while giving loans is very complex and critical. It
requires both accuracy and confidentiality. AI can handle and simplify this process
by analyzing the relevant data of the prospective borrower. AI can combine and ana-
lyze the data related to the latest transactions, market trends, and the most recent
financial activities to identify the potential risks in giving the loan.
Banks can also get the idea of the prospect’s behavior with an AI-based risk
assessment process. AI can minimize the probability of error in identifying even the
slightest probability of fraud. The predictive analytics can manage the entire process
smoothly.
8.16.5 PREVENT FRAUDS
Banks should be bankable for providing secure and swift transactions. AI is designed
to detect the fraud in the transactions based on a predefined set of rules. Also, the
mobile app can find out any suspicious activity in the customer’s account based on
the behavior analysis. For example, any online transaction of a huge amount from
the customer’s account that has a history of small transactions can be figured out
instantly.
AI also plays a vital role in protecting personal data. As we witness a rapid rise in
the instances of cybercrimes in the recent years, AI-based fraud detection can lend
a helping hand in preventing such attempts. So, for the banking and finance sectors,
AI has a tremendous scope in the domain of cyber security. The mobile app devel-
opment services can address the issue of fraud and data breach while developing an
AI-powered mobile app for the banks.
8.16.6 HEDGE FUND MANAGEMENT
Worldwide, artificial-based model is used for hedge funds. From anywhere and any
parts of the world, AI has this power to fetch the real-time data from any financial
market. It has the power to trace the pattern of mood and sentiments of various finan-
cial markets and produce a more accurate result for forecasting. This helps the user
in making decisions quickly using the sophisticated algorithms.
The movement in hedge fund trading is traced using AI in the banking industry.
This suggestion helps the banking sector to overcome the risk in the market.
In short, we can say that AI gives security and safe banking to the coming
generation. Mobile transactions become faster and safer because of AI. The bank
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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123Personalized Banking
can handle large number of customers with a limited number of employees because
of AI.
8.16.7 CONCLUDING LINES
The banking sector has so many advantages by introducing AI in its operations. AI
brings an extensive improvement in the banking sector; it is not about android app
development or is app development. Banks nowadays focus on giving personalized
banking services to their customers by analyzing the behavior of customers through
the app.
Solution analysts are acclaimed IT solutions providers who give personalized
business product by combining contemporary technologies such as AR, VR, AI and
block chain.
8.17 WHAT TO EXPECT IN THE FUTURE FROM AI IN THE FINANCIAL INDUSTRY
Nowadays, AI is a burning topic, but the dynamics of the financial industry have
been changed by AI.
The adoption of the latest technologies in banking sectors such as block chains and
cryptocurrency increases the security and safety feature of banking. Technologies
also make an economic transaction because of the lack of channels. We can say that
AI changes the dynamics of business in the financial sector.
Here, all applications and media assistants continue to make itself perfect, thanks
to intellection computing. These bring a new theory for the management of personal
finance to make it more hassle-free, and all credit goes to applications used by the
machines to perform the various tasks from issuing the bill to the payment of the bill.
The sophisticated self-help VR systems fulfill the customer expectation and give
better customer service.
New technology brings a revolution in the way of interpreting customer’s data
and reporting more thorough alertness in checks in very less time as compared to
the humans.
8.17.1 CONCLUSION
It is not hidden that AI plays a very important role and gives numerous advantages
to the financial industry in multiple ways, which cannot be overlooked. As per the
survey done by Forbes, it has been observed that more than 65% of senior man-
agers expect a positive change in financial management by using AI in financial
services.
Someone said in 2018 that only a few companies used and introduced AI in their
processes, and most are still starving for its implementation and some have fear of
expenses and time.
But now, we can see the technology growth and people do understand that there
is no cost fear in the long run.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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124 Transforming Management Using Artificial Intelligence Techniques
8.18 HYBRID MODEL OF BANKING
In addition to the introduction of technology in various areas and the operation of
banking, the latest model that is emerging is called a hybrid model of banking. It
is a combination of two parts: one deals with traditional banking and another one
has the latest technology and application-based structure. Let’s take an example: in
traditional banking, customer who transfers the money through the branch level is
possible, whereas in hybrid banking, it deals with centralized processing centers.
These centers require a mass of employees for performing various operations like
data entry, which takes a lot of time and energy.
Banking involves many components for completing a transaction, which are as
follows: (a) customer acquisition, (b) assessment, (c) filing the paper (documents), (d)
recording, (e) approvals, and (f) delivery.
In the traditional banking, humans are involved in every stage to give the result
manually, i.e., either for recording the information or delivering the product to the
customer.
8.19 THE FALLOUT
This technology changes the banking up to some extent. This technology reduces
the role of the humans in a bank; they have been replaced by the machines slowly.
In the coming time, the banking sums up into two departments, namely, sale and
data processes. If we talk about the other departments of banks such as recruitment,
inspection, risk management, and others, supporting banking activities are also
undervalued in terms of weight. Because of these changes, the roles and responsibili-
ties of a recruitment cell are also changed. There is a large requirement for the sale
of staff in banking. Due to increasing competition in the banking industry, there is
also increasing pressure on banking for profitability and are also various challenges
to recover the nonperforming asset and in parallel decrease the cost of operation. We
hire a cheaper workforce to balance the cost of operation, but this leads to another
problem that it demands the change in the profile of a candidate who is interested to
join banking. This is the reason why this technology becomes the lowest choice at a
recognized management institute.
Due to the increasing advancements in technology, there is another area or scope
of work is opening (i.e., outsourcing) because the technology divides the banking
into two departments that can be managed as a separate entity.
Technically, the outsourcers work as an independent unit, but they are helping
hand of the organization. This will help banking to save various costs.
8.20 FMCG MODEL OF BANKING
Eventually, banking has been a shift from the traditional banking – where all
sections and areas of banking have equal importance – to the banking where a
technology-based platform is employed to sell its product. And all other functions of
banking are outsourced and done by third parties, such as recruitment of manpower,
audit, and recovery of bad debt.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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125Personalized Banking
Banking nowadays fits into a small mobile phone. There is no point to astonish those mobile companies that are starting to become bankers. (Customers also change their bank so often without any second thought in mind as they switch their mobile operators.)
Because of the imploding model of banking and due to less human involvement, safekeeping in banking has declined, which leads to more fraud and manipulations.
Gone are the days when banks focused on making a trustworthy relationship with the customer and maintaining loyalty. But now banks only focus on achieving the predetermined targets. Now the banking institution smartly replaces the FMCG model, which effects on sales transaction and which takes seconds to execute.
Fast-moving consumer goods are double Dutch for items such as tooth-paste and cookies that are bought by a customer in large volume and frequency. A fast- moving consumer goods industry does not require any energetic commit-ment with its customers on an everlasting basis. It does not require to develop a contractual relationship that goes beyond the short time duration because it analyzes its consideration in a few minutes.
But in the banking industry, it is about either keeping a deposit or taking borrowing with the bank; the consideration is realized by the customer or bank in a long period. It is either repayment of the loan by a debtor or repayment of interest and a deposit made by the customer.
Here, the transaction time by the customer in banking is much longer as compared to the time in the consumer goods company. Therefore, this two-way relationship between the customer and the bank has to be nurtured for a longer period.
This job cannot be performed by the machines alone. For the perfection, the machine needs human interference for doing so many tasks like sharing the informa-tion and observation, taking corrective measures, and making the decisions.
Therefore, we should not over dependent on technology and do not underestimate the role of humans.
If we are increasing the role of technology at various levels in banking just to save the cost of manpower, this will hit the banking basics. You cannot create a bank only through technology, but surely you can develop people who use technology to create a bank.
O.P. Srivastava has spent 35 years in public and private sector banks. He was responsible for setting up the technology-based infrastructure for one of the leading private sector banks. After retiring, he has been making films and received the National Award 2015 for the best biopic for his first film Life in Metaphors.
8.21 POWERING BUSINESS GROWTH
The world’s most successful financial services organizations are already consid-ering AI and machine learning to help them address the following (Figure 8.6):
• Compliance, antimoney laundering, and fraud detection;• Payments processing;• Personalization of customer experiences;
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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126 Transforming Management Using Artificial Intelligence Techniques
• Digital interference affects the industries and changes the business dynam-
ics. Nowadays, the goal of each industry is to implement the technology and
create value in these tech saviors. The banking sector is also becoming part
of unusual changes;
• Technology-centric consumers experience smart technologies in their daily
life and expect the bank to give flawless experiences. To give this flawless
experience to its customer, the banks should also expand its services such
as banking, real-time settlement, and instant money transfer. Considering
all these expansions in the banking industry, a customer can do banking by
simply using figures anytime and anywhere on their mobile phone;
• With regard to banking and other sectors such as telecom and retail, IT has
expanded the exchange of data over the virtual networks that are easy to
target and attacked by cybercriminals. These types of accidents not only
affect the growth of banks but also breach the trust relationship between
banks and customers;
• Due to an increase in online fraud activities and security challenges, bank-
ing sector has strict rules and regulations made by the government; even
these types of regulations help to keep eyes on each financial transaction
and hold back all bank’s capabilities and to keep up with digital transforma-
tion. Banks have faced a major challenge to invest in technology, and they
have to comply with the international framework related to capital adequacy
ratio. Therefore, the bank becomes weaker as Fintech players do not require
maintaining the capital adequacy ratio. According to the report, about half
of the customers move to these players;
FIGURE 8.6 Model to control frauds.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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127Personalized Banking
• AI and banking;
• Channelizing the technology with AI comes with the advantage of the digi-
tal world to banks, and supports them to achieve the target and avoid the
competition created by the Fintech player. However, around 32% of service
providers are using AI for purposes such as sound recognitions and future
analysis of market trends;
• We can say that AI takes banking to the next level, or it is the future of
banking in the coming years as it gives strength to analyze the data to fight
the fraud transaction and maintain compliance. AI complies with the antim-
oney and unauthorized activities in seconds rather than hours. With the help
of AI, banks can make and record a huge amount of data. Many other fea-
tures of AI also help to increase the customer base of the banking industry,
such as chatbots and online payments. Because of this, AI increases the
profit of the banking industry;
• Bank competitiveness can be increased by AI, and this can be proved by the
following characteristics:
• Improved customer experience: Based on the pattern analysis of the behav-
ior of the customer, AI creates a strong structure to understand the customer
behavior;
• Banking industry uses AI to make its system more competitive, and it can
be done by Ai area as:
• Improved customer experience: Based on past data, the behavior patterns
of customers can be better understood by AI. This technology helps bank
give a more personalized product to its customer and enhance the customer
experience to different levels;
• Forecasting the future results and trends: Because this feature and skill
is used to analyze the data and forecast the future trend, AI does help to
formulate future policies and forecast the possible results and trends. By
doing this, banks can take precautionary steps to avoid future risks such
as frauds and antimoney laundering, and also guide its customers to take
necessary steps if required. The Hawala people or the persons involved
in money laundering can transfer the money from place to another, which
makes the illegal money to legal money. But with the help of this latest
machine learning and intelligence, AI saves the many banks by detecting
the hidden actions;
• Intelligence process automation: This characteristic is helpful in the auto-
mation of a variety of technology-intensive, expensive, and like-to-make-
error banking services. This secures ROI, minimizes costs, and assures
correct and fast processing of services at each step. Intellectual process
automation automates a group of actions that better their previous repetition
via a constant machine learning;
• Realistic interactive interfaces: Chatbot is an application that understands
the emotions and feelings of the text and responds as the same. These smart
machines did not help banks to save time and expenses but led to saving
millions of dollars as cumulative cost saving;
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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128 Transforming Management Using Artificial Intelligence Techniques
• Influential decision-making: The real-time data can be better understood
and analyzed by the intelligence system as reacted like a human expert.
Knowledge database keeps by cognitive systems, and banks used such type
of data for strategic decision-making;
• Robotic automation of process: With the help of robotic process automa-
tion, AI can review and transform the processes. By doing this, we can shift
the human resource to more value-added works, and the work efficiency can
be enhanced while assigning the repetitive work to automation;
• AI-enabled future;
• AI brings a lot of things in the banking sector; thus, it is not only enhances
the knowledge of the workforce but also takes the steps to minimize the risk
related to cybercrime and minimize the competition from Fintech. AI is
the thing that combines the bank operation and processes and auto-updates
with time without any human interference. With the help of AI, banks can
now give more personalized solutions, and they also strengthen the human
and machine efficiency to operate cost-effectively. All these are not a future
dream for banks, but most of the leaders in the banking sector perform this
entire task and get the benefits;
• Because of the revolution in the banking industry, people rarely have face-
to-face communication. This is the reason for the challenges faced by peo-
ple to attract, convince, and retain them for the improved services. Banks
are invested a large amount of money into digitalization, and they introduce
so many online services like chatbots. There will be a more expected role
of AI in banking; because of cloud computing and machine algorithm, it
creates a perfect condition for the expansion of the business.
8.22 IMPROVING DIGITAL EXPERIENCES
Based on the past behavior of customer, AI can do many things to make easy
decision-making for the customers and also give customized solutions and sugges-
tions. Banks use AI and take the help of AI to analyze the behavior of the customers
and communicate the same things to banks so that bank can design product and
services accordingly and enhance the customer satisfaction; for instance, a customer
uses their credit card to make payments of the ticket, so this information with the
help of AI is transferred to the bank and the bank will offer customized service to
the customer.
WEBLINKS
https://www.finextra.com/blogposting/14617/how-artificial-intelligence-can-deliver-a-personalised-banking-experience
https://www.livemint.com/ https://www.wipro.com/en-IN/business-process/why-banks-need-artificial-intelligence/ https://www.opentext.com/info/ai-financial-services/ai-analytics-for-payments https://www.paymentsjournal.com/the-18-top-use-cases-of-artificial-intelligence-in-banks/
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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129
9 AI in FashionPresent and Future Applications
Shalini Aggarwal, Priyanka Bhardwaj, and Jalaj AroraChandigarh University
9.1 INTRODUCTION
Artificial intelligence (AI) – an algorithm-based computer program that runs on pro-
cessing power – is very creative. It is, in fact, the advancement of computer systems
that can emulate human intelligence to perform certain tasks that were hitherto lim-
ited to humans. It has a vast and diverse intelligence that is capable of making more
accurate and faster decisions.
Until now, the fashion industry production process was more important than the
product quality. But with the growth of technology, through ever excelling Internet
power, the scenario has changed. In this fiercely connected ambient world, the
CONTENTS
9.1 Introduction 129 ..................................................................................................
9.2 Benefits of AI in Fashion 131 ..............................................................................
9.3 Big Data and Fashion Quality Control 134 .........................................................
9.4 Major Fashion Retailers Using AI 134 ................................................................
9.4.1 Alibaba 135 ..............................................................................................
9.4.2 Amazon 135 .............................................................................................
9.4.3 H&M 135 .................................................................................................
9.4.4 Tommy Hilfiger 136.................................................................................
9.4.5 ASOS 136 ................................................................................................
9.4.6 Dior 136 ...................................................................................................
9.4.7 Nike on Demand 137 ...............................................................................
9.4.8 Grabit Inc 137. .........................................................................................
9.4.9 VF Corporation 138 .................................................................................
9.4.10 Macy’s 138 ...............................................................................................
9.4.11 Nordstrom 138 .........................................................................................
9.4.12 Final Thoughts 139 ..................................................................................
9.5 Demerits of Using AI in the Fashion Industry 139 .............................................
References 140 ..............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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130 Transforming Management Using Artificial Intelligence Techniques
prominent fashion brands have become alert to the need for adopting the latest fash-
ion designs all over the world and also to ensure product quality to stay in business
were to explore tomorrow’s trends. One has not only to stay in business but also to
compete with international standards. Since these tasks are handled by labor class, it
is not possible to adhere to the desired benchmarks at all levels. We can understand
that manual jobs can’t ensure high-speed production with accuracy level because
human efficiency is based on many factors. With this tendency to feel monotonous,
it leads to human errors more and more negatively impacting the apparel industry to
fail in achieving goals.
The textile industries aim at grabbing the market fast by launching their designer
dresses quickly from the ramp into the market (Schmelzer, 2019). For this, they need
a highly efficient designing and production process to come out fast with new cloth-
ing patterns in line with public taste. To enable this to happen, the supply chain
management has to ensure with suppliers to work at shorter deadlines. It is the need
of the market that companies are to stand up to consumer’s satisfaction by provid-
ing the best fashion garments at competitive rates. The biggest challenge lies at this
point because the product cost is comprised of various factors such as the time put in
behind designing plus the cost of production, logistic expenses up to the retail market
but remaining as near as possible to consumer’s expectations.
The unmatched expanse of the fashion world has made this field of business very
ambitious and enterprising because the designers are aware of the prevailing styles,
which is a fast-changing curve. It is so because they are expected to set trends for
widely recognized people who are acknowledged for their distinctive excellence in
specific fields. With this kind of product and business knowledge and access to the
largest data sets in the fashion world, the managerial team thinks that such an inno-
vative and creative industry can’t be digitized. However, they all must understand
that with the advent of AI in all walks of life, the same can bring amazing results
when conjoined with human skills. Hence, AI can provide a competitive edge to the
retailer market.
Since AI has an impact on the changing business processes, the fashion industry
cannot afford to remain aloof of the same. Realizing the added advantages of AI,
many big and medium-sized organizations, which include well set as well as start-up
businesses, have taken recourse to this unavoidable change.
Time has come for the apparel industry to wake up to the needs of time and adopt
this so-called digital transformation because it is an essential ingredient to be in the
race always.
With this objective in mind, organizations such as Fashion Tech Accelerator
from Silicon Valley and the New York Fashion Tech Lab have taken the lead to
develop an energetic ecosystem with the help of accelerators and incubators, and
contribute to the transformation of fashion technology. Even the top fashion brands
such as Nike, Adidas, Burberry, and Levi’s have realized and accepted the fact that
amalgamation of innovation and AI can turn the fashion industry more dynamic,
influential, easy to deal with, and crystal clear whereby there would be no wasteful
drainage of inputs. They believe that AI has a definite role to play to uplift wardrobe
consultants.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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131AI in Fashion
On the same lines, another leading brand Gucci, an Italian company, that aims
to shift the supply chain of the company closer to home has the plan to set up a
35,000 ft2. Gucci Art Lab to produce leather wears/accessories and footwear. The
main aim is to tighten supervision and control on manufacturing, inspection, and
quality of the products.
Hook helps customers shop like a digital designer. It is executed by improved
exceptional search capabilities that provide the customer with information for a spec-
ified product in an individualized outlook of the product in comparison with similar
products of other manufacturers for quality, costs, and offers.
9.2 BENEFITS OF AI IN FASHION
Undoubtedly, AI has drastically impacted the styles of business in several ways. The
fashion sector has shifted from predictive industry analytics to computer vision to
establish the credibility of brand attributes. The following examples depict how the
power of AI helps the fashion world:
• Trendspotting:
The AI tool makes previous fashion market analysis easier when it is
connected with information on grade and volume to reflect consumer choice
and expectancy, and measures tires of competitors who presents market
trends. The data processing is quite fast and enables the AI system to display
an accurate picture of trends, styles, and designs instantly. Garment tycoons
have been able to enhance their businesses by monitoring these analytical
trends, thanks to AI.
• Machine-assisted designs:
Some big business houses have been able to progress very fast by apply-
ing AI clubbed with new technology to their benefits. For example, a group
of experts working with Amazon, the largest online store, developed a
program that can create a new fashion design different from all fashion
images posted on the store wall. Another side of the story is that the busi-
ness behemoth – Amazon – has developed a second program to analyze and
manipulate the fed images to derive the fact of whether or not the apparel
looks trendy. Looking at the success of Amazon, many other business
houses have followed a suit to adopt and apply AI to their business meth-
ods to boost the project development. IBM has joined hands with Tommy
Hilfiger and The Fashion Institute of Technology (FIT) to empower design-
ers to enhance the lifecycle of product development with the help of AI.
• Enhanced customer service:
Further, some AI apps are a step ahead to help you to search a suitable
dress design for you by clicking your picture, and the app would suggest
the latest trendy apparel best suited to your personality based on your body
shape, looks, and choices. The personal experience with customers has
been furthered with the help of AI by responding to the customer queries
on styles, trends, designs, prices, and discounts. AI is capable of playing a
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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132 Transforming Management Using Artificial Intelligence Techniques
dual role, guiding the designers to create the best clothes, and guiding the
customer to what will suit him out of the available designs. In other words,
we can’t underplay the role of technology in today’s fashion business world.
• Customization:
Since e-commerce has become a trend of the day, the companies prefer
to collect more and more data about their customer choices and have devel-
oped a data storage system to keep a log of each individual. They closely
watch their browsing habits, which in turn help them to send suggestive
messages on colors, designs, prices, and offers to tap business.
• Improved customer service and communication:
In the list of changing trends in terms of businesses, chatbot software
is increasingly replacing the past time system of phone calls and e-mail
messages. This enhanced conversation and interaction with customers have
proved more effective than the earlier methods. There is a multitude of
solutions from IBM Watson to HubSpot and chatbot that help track leads,
answer questions, and even give recommendations for buying goods.
• Powering productivity and creativity:
It has been observed over the time that there has been a commendable
improvement in the supply chain management of the fashion industry as a
result of AI implementation and other forms of smart technology, which
not only enhanced the pace of work but also provided cost savings while
pro viding a more supple working system. This, in turn, resulted in improved
forecasting, capacity planning and marketing, process management, and
d istribution. All the above are passed on to the customer because AI ensures
the better product availability and fast replenishment. But this is not the end
because AI has unending potential and the fashion business is expected to
be more efficient day-by-day. Not limited to the sales and supply process,
the big players of the industry are on the way to assign the creative process,
i.e., improved skills and designs of the garments to this technology. It will
c ertainly lead to a more efficient product development process with lesser
mistakes. AI tools use information from the big data about the browsing his-
tory of fashion consumers in detail to evolve designs that could enchant them.
• Better buying and planning:
We must understand that it is not easy to draw a balance between a vari-
ety of material available and even a large variety of customer choices, the
fashion buyers and planners face this as the biggest challenge because order-
ing for a wrong material may lead to a situation of stagnation by a slow or
low sale of products not fancied by the consumers. This becomes a hazard
in the planning and procurement process. However, since the AI-enabled
business can make use of advanced predictive analytics, they understand
the customer behavior better and hence can plan things with an enhanced
accuracy level in terms of quantity and quality.
• Automating operations:AI is a versatile technology that is capable of handling all tasks which
include data entry and related calculations, which means saving time and
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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133AI in Fashion
manpower with the added advantage of accuracy. The vital time thus saved
is utilized more effectively in more accurate strategic planning and cost
calculations to enhance profit margins. It can be concluded that AI is a boon
to the fashion industry, which is the diciest business.
• Managing inventory:
The retailers have always been caught in a triangle of over-ordering,
unsold items, and missing out income because trading is a tricky business
where unexpected happens many times if the equations are not set logically.
There have always been sales forecasting methods that were unscientific.
Even if the traders had some statistical data, its usage had some limitations.
For example, if you had to use data sets for sales, reaching a 10,000 set
would mean that it is not an easy task. However, currently, even the most
complex forecasting is made easier with the help of the AI. The machine
learning helps us to reduce the prediction errors up to 50% with no embargo
on data sets.
• Improved product discovery:
To make shoppers’ life easy, machine learning is further supported by
computer vision technology, which helps you to get quick feedback on the
product uploaded by you as well as its availability on various outlets. Google
Lens is another supporting tool to usher you to locate similar designs as you
snap the image from your smartphone. At the same time, it is now also pos-
sible to view similar images through Pinterest using AI software.
• Visual searching:
AI floats to help consumers visualize their feelings. Whether it’s giving
a picture or placing an object in the shadow, it’s going to go like a hunt for a
reverse image. For instance, if you have a geometric patterned dress in your
mind that was worn a day or two ago by your friend, AI will help you find
it. You can pick your friend’s photo, upload it to a fashion website, and you
will get the equivalent or comparable stuff from the AI algorithms. With
this creativity on their existing pages, companies such as Nordstrom and
ASOS are now trying different things.
• Reducing the cost of manufacturing:
Because of e-commerce and innovation, the costs of starting a fashion
company have decreased significantly. Etsy’s launch made it easy for anyone
to start an online store and create a new one. Today, lower manufacturing
costs make it possible for start-ups or new brands to produce low-cost prod-
uct runs and grow digital visitors from that point onward. In years prior,
fashion business would have to create tons of products with the ultimate
goal of creating them at a reasonable cost.
• m-Commerce and E-commerce:
Through social networking apps, messaging tools, and digital wallets,
the phone has influenced our daily lives as well as pushing its way into
the corporate world at the moment. via mobile business; this network era
has offered a sleeker style of shopping. Equipped with smartphones, we
would now not be able to shop solely on the Internet, but get easy payment
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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134 Transforming Management Using Artificial Intelligence Techniques
service with digital wallet options such as Android Pay and Apple. It is
even researched that, as opposed to going to a physical store, many people
enjoy shopping online. This fresher shopping strategy allows a one-click
order – an easy experience. Take, e.g., the shopping function of Instagram
as users are currently able to enjoy a consistent mobile-friendly shopping
history by clicking on the product they like and being redirected to the item
site – significantly reducing search time. Brands can use their convenient
cell phones to give their customers a simple way to find products and shop.
• Costing of products:Big data tools allow retailers to maintain a competitive price plan that
combines machine learning and AI. Retailers integrate this AI into the
knowledge of a company, so there is a principle-based mechanism that
keeps costs updated, depending on the ongoing external elements such as
sales, inventory, and out-of-stock circumstances of contenders. This inte-
gration helps retailers to reduce their prices and, for instance, to guarantee
that they generally offer 10% less costly SKU than Amazon.
9.3 BIG DATA AND FASHION QUALITY CONTROL
Recognition of patterns (not to be mistaken for the recognition of images) is a branch
of AI. Combined with big data, companies use the pattern recognition to defend their
brands’ reputations. Not only through quality control, but also through battling to
stop counterfeiters from distributing imitation fashionwear, product’s credibility is
maintained. All the fashion product data is therefore called as “fashion data”. These
data can be used to analyze patterns, the consumer behavior, forecast, etc. The fash-
ion industry produces and creates different data sources. All these data come in dif-
ferent forms such as letters and images. The information is rapidly growing and
evolving as it is the age of fast fashion.
A significant work in the world of big data has been done over the past decade. A
big data concept involves analyzing large amounts of data to extract valuable infor-
mation. Big data plays a growing role in trend analysis, and analyzing consumer
behavior, desires, and emotions in the fashion world.
The industry has experienced a shift from mass production to mass customization,
which is essentially the mass production performance customization. Many innova-
tions allow the industry to create new ways to meet the customer’s ever-growing and
ever-changing needs. Nonetheless, there are many difficulties in adapting the pro-
duction process as the difficulty increases with the rate of customization (Mageean,
2019).
9.4 MAJOR FASHION RETAILERS USING AI
Although AI is not very new, what is due is the full realization of this tool because its
application to fashion and other businesses is just the first step, a start-up effort. The
scope of the AI is unlimited and needs to be explored fast.
You may soon find an AI-powered textile market being introduced by the
e-co mmerce leader, Amazon, which is capable of reproducing popular styles with
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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135AI in Fashion
the help of an efficient algorithm. This would give a new dimension to the fashion
because this tool can develop new design garments based on the yesteryear tastes of
the customers, which would be an exciting experience for the users.
9.4.1 ALIBABA
“Modern retail” is the new revolutionary shopping concept initiated by Alibaba
Group, a Chinese company, who are in multinational retail and technology business
since 2018. They claim their “Fashion AI” store will transform the customer mind-
sets and provide them a fresh shopping experience, which they would love.
This highly technical store has very special features that are unique to this store.
The products are fixed with radio frequency identification (RFID) technology, and
the Bluetooth chips are loaded with specialized information, which helps the custom-
ers to know about other products of their size and color. Choices are available in the
vogue that provides an in-depth knowledge, which is useful in their decision-making.
Similarly, all floors and try rooms of the store are fixed with smart mirrors. There
is an integration between the smart tags and the smart mirrors. These mirrors have
the technology to display costume designs in the mirror whereby the customer nei-
ther has to carry any dresses to the trial room nor has to search for the same in-store
shelves because of the RFID technology along with gyro-sensors displaying dress
designs, colors, and sizes to him/her in the try room automatically. The touch screen
technology also helps the store staff to know if a customer is in the change room as
well as what other products he is carrying with him. The mirror then will suggest
which matching product he can buy from here to complete his shopping.
Not ending here, the omnichannel further integrates the artificially intelligent
fashion with Mobile Taboo app, and the customer gets the advantage of viewing a
virtual wardrobe created by AI for all garments that he has tried which provide an
excellent shopping experience because he can finally decide to buy the most suitable
product in consultation with his family/friends.
9.4.2 AMAZON
Following a suit of the rising trend of AI in fashion, Amazon too has formulated an
algorithm that is capable of creating new design clothes based on copy–paste meth-
odology from different styles.
Amazon also has “Alexa Fashion” assistant called “Echo look”, which is designed
to track your closet and then flash suggestions on new inclusions in your apparel col-
lection. This software will further help you to customize your machine intelligence
concerning styles.
9.4.3 H&M
While the retail catastrophe has engulfed numerous organizations, H&M is an
outstanding example depending solely on big data boom by analyzing receipt and
loyalty card data, which information is used by them in customizing and preparing
the product according to the needs of different stores as revealed by the loyalty data.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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136 Transforming Management Using Artificial Intelligence Techniques
In other words, they are the only company that tailors made merchandise according
to the individual store requirements.
Hence, they are the pioneers in offering personalized suggestions in fashion and
planning to extend the same technology to building material stores through RFID
implementation.
Further, they have joined hands with Google in developing a new technology
named “Coded Couture”, which enables art and code to work together with the help
of an app installed on the mobile. This app will, in turn, analyze your lifestyle over
the past 7 days and create an exclusive dress design to suit your personality.
9.4.4 TOMMY HILFIGER
FIT, Tommy Hilfiger, and IBM have collectively worked on a unique project named
“Reimaging Retail” campaign, which would boost retailers by providing them with
AI-based designs and skills so that they could surpass their contemporaries.
The FIT students are enabled to design individualized outfits with the help of
valuable data and other facilities such as computer vision, natural language clubbed
with AI testing systems devised by IBM to which these students have been given a
right to access. This methodology has resulted in a reduced lead time while providing
an upper edge in creativity at a faster rate. The designers are exposed to numerous
images and videos, which, when aided by AI, enhance their analytical ability and can
produce the most wanted products through a deeper insight provided by an all-round
technology.
9.4.5 ASOS
ASOS is the pioneer in introducing “Fit Assistant” software in their retail store with
an AI integration that provides an excellent shopping experience to its customers. Fit
Assistant helps them to look at the correct size of the apparel. Not ending here, it also
works through a fabric information database, as well as scans through the purchasing
history of the client and then changes the size of the outfit accordingly.
On the other side, a different kind of AI platform is used by Shane and Falguni
Peacock, a designer establishment engaged in couture through which they can watch
and study more than six lakh photographs of global fashion shows and innovate new
design based on this analytical study.
9.4.6 DIOR
In March 2017, Paris-based fashion brand has come out with a Facebook Messenger
app called “Dior Insider”, which works as a beauty assistant. This AI-integrated chat-
bot is capable of interacting with the customers, whose names are also known to it.
When the conversation opens with the name, the customer feels delighted and is
attentive to the question–answer session which follows. A series of questions have
been framed, which help the app to establish their interest in skincare and makeup
products the company deals in.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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137AI in Fashion
According to a 2016 report by the PMX advertising agency on luxury brand trends,
it has been established that Facebook is the leading social platform that caters to
6.3% of web traffic for luxury brands and fashion sales, which is an awesome figure.
9.4.7 NIKE ON DEMAND
In 2017, the advertising agency R/GA designed a campaign “Nike on Demand” on
the behest of Nike in Germany that aimed at transforming the company image from
“cool product brand” to “quality partner”. It ultimately helped the athletes to achieve
their performance objectives by assuring quality products. This campaign worked
using IoT data which would initiate AI assistant to suggest a consistency in exercise
behavior. It was a data analytics and machine learning-integrated exercise which
continued for 6 weeks.
The advertising company collected data for the last decade from Nike Running
Club and Nike Training relating to pacers and leveraged the same for analysis to
i dentify the problem areas in order to find solutions. The analysis leads to the
conclusion that the track runners were not able to strictly follow their practice sessions,
and hence, it emerged as the major bottleneck in achieving their objectives. Therefore,
via WhatsApp talk, the Nike on Demand program was designed to deliver “private
one-on-one coaching” paired with coach guidance tailored to the needs of each client.
A total of 22,000 messages shared with 240 Nike On Demand users resulted from the
six-week campaign:
83% would recommend Nike On Demand to a friend. 81% would use the service
again. 70% expect this type of service from Nike
Based on the findings, wake-up messages were encoded with inspiring words to help
them shed off their sleep, go the tracks, be committed to goals, and visit gymnasium.
The messages that were sent were personalized depending upon athletes’ behav-
ioral study derived through data analytics and the same were reviewed twice a day to
ascertain their effectiveness and finally earmark messages for the next transmission.
Encouraged by the positive outcome of this campaign, Nike decided to extend the
same to other markets in order to enhance its business globally. However, presently,
it is all a guesswork because the company is waiting to assess the return on invest-
ment in order to finally decide how much more they should invest in the AI to achieve
market objectives (Kumba, 2019).
9.4.8 GRABIT INC.
Nike further shook hands with Grabit Inc., a Californian company, in 2013 to adopt
their automated robotic manufacturing process support blended with machine learn-
ing. However, the investment amount for the same has been kept under the carpet by
Nike.
The factors related to machine learning included in this model are not vividly
elucidated, but unlike most customer-facing retail robotic cases, Grabit is more of a
mobile, back-end tool. In view of this feature, Grabit is a unique business model for
Nike. Most manufacturers have to spend much of their time (60%–80%) in handling,
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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138 Transforming Management Using Artificial Intelligence Techniques
which is a labor-intensive job and hence quite expensive. Grabit offers software that
is special in a way that it is a blend of electrical adhesion and machine learning that
makes it flexible in automation. The manufacturers claim that the process is so effi-
cient that it can handle with precision most fragile products like eggs and soft fabric
with ease.
The devices provided by Grabit include smart conveyors, grippers and fixtures,
and case handling grippers, and it means you can handle all types of product packs
with these automated systems. For these lucrative devices, the company raised an
investment of 21 million dollars by September 2016 in A and B funding. The major
investing client was Samsung Electronics and a few more (Joshi, 2019).
A 6% revenue growth was recorded in their declaration of 2016 over 2015, which
totaled 32.4 billion dollars. Nike’s 2016 10-K report did not publish clear R&D
estimates.
9.4.9 VF CORPORATION
The end of 2015 witnessed the launch of the Fluid Expert Personal Shopper (XPS), a
product of The North Face, a VF Corporation Company. It was another AI assistant
aimed at improving customers’ shopping experiences. Unfortunately, their initial press
release in this context is now not available on records. This Fluid XPS was unique in
the sense that the customer didn’t have to scan page after page as we normally do
for online shopping. The new app provided an intensive dialogue facility with the
customer, and he could discover the product(s) of his choice using the IBM Watson’s
software, which was designed to understand and analyze the natural language used in
day-to-day life. In other words, XPS played the role of an in-store assistant who would
help the customer by providing personalized answers to their discovery questions and
finally provide the required product or suggestions for an alternative.
The VF Corporation has some 30 brands in their portfolio, and the North Face is
one of these that deal with Outdoor and Action Sports, which had been the top earner
of the corporation touching a sale target of $7.53 billion as enumerated in their 2016
annual report.
9.4.10 MACY’S
Macy’s initiated its “Macy’s Call Programme” in July 2016 at 10 pilot locations in
the country. It was another AI-powered in-store shopping assistant through mobile
devices. This program uses IBM Watson software and Satisfi, which would provide
an enhanced and more satisfying shopping experience to the customers.
Macy’s On Call can decipher customers’ natural language questions and in return
provide answers to queries about the products, equipment, or locations of a store.
9.4.11 NORDSTROM
The first chatbot was launched by Nordstrom in 2016 aimed at uplifting the holiday
season shopping experience of the customers who worked on Facebook Messenger
as well as Kik (Newgenapps.com, 2019).
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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139AI in Fashion
The Nordstrom collaborated with a New York-based mobile messaging service
offers tools for the brands to create their chatbots, which in turn would be responsive
to Nordstrom items matching to their choices. This will happen through a series of
multiple questions to deicide the shopping preference of the customer. A facility to
interact with human customer services is also available on this platform whereby the
customer gets back gift ideas from the members.
Nordstrom reported a revenue of $14.5 billion for the 2016 fiscal year, including
$2.5 billion in online sales, in its 2016 annual report.
9.4.12 FINAL THOUGHTS
As technology continues to improve flexibility alongside the best execution of line
and customer support while reducing costs, the fashion industry and AI tend to be
an ideal fit.
Both activities in the fashion business will change as a few businesses are auto-
mated using AI and robotic engineering, from development to production to business
analysis. The big test is the way to make sure that these changes can benefit everyone
in the fashion industry.
In this world of rapid technological disruptions, it seems meaningful to adopt
AI and machine learning process, which is expected to take the fashion industry by
the tide. The coming times are going to bring an exciting experience to retailers,
m anufacturers, designers, and managers alike by revolutionizing the business world.
Chain of demand is one such example of a company to implement AI-driven
machine learning because the proper use of big data in the fashion retailers can
forecast more accurately the market trends for the best and worst selling items. This
accuracy would further lead to enhanced profits through an increase in productivity
and a reduction in product wastage (Dataflair, 2019).
9.5 DEMERITS OF USING AI IN THE FASHION INDUSTRY (FIGURE 9.1)
• Incur high cost:It has been experienced by the retailers that returns of costumes by the
customers result in heavy losses, which are estimated at $642.6 million
every year. This happens due to the wrong choice of products because the
garment when received physically does not match the customer perception
for color, texture, or fabric. It is, therefore, suggested using AI to make cus-
tomers more aware of the product they are buying and thus reduce the rate of
returns. It will also strengthen their confidence level and reduce frustration.
• Making humans lazy:
AI, in the long run, may prove to be a killer of innovation because too
much dependence on machine tends to make humans lazy and casual in
approach (Kumar, 2019).
• No emotions:Machines can neither replace man nor can these be accepted as mas-
ters of the human mind. A machine can be helpful in many ways but it
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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140 Transforming Management Using Artificial Intelligence Techniques
can’t understand human feelings and thus establish a bond with customers.
Yes, machines can be accepted as tools to be a part of the team in the retail
business.
• Unemployment:This is the riskiest and can have severe effects. With capital intensive
technologies, human-intensive requirements have decreased in some indus-
tries. If in the future, human beings don’t add to their skills, then in no time,
we can see that they will be replaced with machines. The major issue of the
GDP being stagnant or not growing at the expected rate is unemployment.
People don’t possess the required skills that are in demand. There are a huge
demand and supply gap because of this (Kumar, 2019).
REFERENCES
Dataflair. (2019). Pros and cons of artificial intelligence – A threat or a blessing? Available at https://data-flair.training/blogs/artificial-intelligence-advantages-disadvantages/ [Accessed December 18, 2019].
Joshi, N. (2019). Artificial intelligence in the apparel industry | Internet of Things |. [online] Allerin.com. Available at https://www.allerin.com/blog/artificial-intelligence-in-the-apparel-industry [Accessed December, 18 2019].
Kumar, S. (2019). Advantages and disadvantages of artificial intelligence. Available at https://towardsdatascience.com/advantages-and-disadvantages-of-artificial-intelligence-182a5ef6588c [Accessed December, 18 2019].
Kumba, S. (2019). AI in fashion – Present and future applications. Available at https://emerj.com/ai-sector-overviews/ai-in-fashion-applications/ [Accessed December, 18 2019].
FIGURE 9.1 Pros and cons of AI. (created by author.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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141AI in Fashion
Mageean, L. (2019). The future of fashion: How AI is changing the fashion retail industry. [online] WhichPLM. Available at https://www.whichplm.com/the-future-of-fashion- ai-changing-fashion-retail-industry/ [Accessed December, 18 2019].
Newgenapps.com. (2019). AI and its impact on the fashion industry. [online] Available at https://www.newgenapps.com/blog/ai-its-impact-on-fashion-industry [Accessed December, 18 2019].
Schmelzer, R. (2019). The fashion industry is getting more intelligent with AI. [online] Forbes.com. Available at: https://www.forbes.com/sites/cognitiveworld/2019/07/16/the- fashion-industry-is-getting-more-intelligent-with-ai/ [Accessed December, 18 2019].
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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143
10 Digitalization, Innovation, and Artificial IntelligenceA Road Map to the Future of Industry 4.0
Vijay Prakash GuptaInstitute of Technology & Science
CONTENTS
10.1 Introduction .................................................................................................. 14410.1.1 The Journey toward Milestones of Industry 4.0 ............................... 144
10.2 What Is Industry 4.0?.................................................................................... 14510.2.1 Need of Industry 4.0 (I-4.0) .............................................................. 14510.2.2 Features of Industry 4.0 .................................................................... 145
10.2.2.1 Flexibility in the Production Process ................................. 14510.2.2.2 Flexible Production Environments .................................... 14610.2.2.3 Optimized Logistics Solutions ........................................... 14610.2.2.4 Saving of Resources ........................................................... 14610.2.2.5 Clear Customer Orientation ............................................... 14610.2.2.6 Effective Use of All Data ................................................... 146
10.3 Components of Digital Technologies in Industry 4.0 ................................... 14610.3.1 Benefits of Digitalization, Innovation, and
Artificial Intelligence ................................................................. 14810.3.2 Digitization, Innovation, and Artificial Intelligence: As the
Basis for Success for Industry 4.0 ......................................................14910.3.3 Blueprint for Success of Industry 4.0 ............................................... 149
10.3.3.1 Map Out Your Industry 4.0 Strategy ................................. 15010.3.3.2 Create Initial Pilot Projects ................................................ 15010.3.3.3 Define the Capabilities You Need ...................................... 15110.3.3.4 Become a Data Virtuoso .................................................... 15210.3.3.5 Transform into a Digital Enterprise ................................... 15210.3.3.6 Actively Plan an Ecosystem Approach .............................. 152
10.4 Impact of Digitalization, Innovation, and Artificial Intelligence on Industry Growth ........................................................................................... 153
10.5 Issues and Challenges in Industry 4.0 .......................................................... 15410.6 Summary and Outlook ................................................................................. 155
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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144 Transforming Management Using Artificial Intelligence Techniques
10.1 INTRODUCTION
The term “Industry 4.0” refers to the combination of several major innovations and digital technology takes place. Industry 4.0 includes advanced robotics, smart sensors, and AI, IoT, IIoT, big data, cloud computing, and other new production models. The combination or set of these technologies integrates the physical and virtual worlds. This change enables an advanced and sophisticated way of organizing the manufacturing process by increasing the efficiency and speed of the production process to large-scale machine production. Now, in the Fourth Industrial Revolution, the use of the innovation and digitalization process has been introduced in the indus-try, especially in the manufacturing sector.
10.1.1 the Journey toWArd mIleStoneS of InduStry 4.0 (fIgure 10.1)
The modern industry that we are experiencing today has been shaped by major tech-nological revolutions in the manufacturing process. The First Industrial Revolution was stated from Britain, and the processes of the steam engine and mechanical meth-ods of production were started. After that, the second phase came into existence where the beginning of industrial production takes place with the help of electricity and the birth of the factory and mass production method. In the late 1960s after the third world war, the use of automated machinery, computer-aided manufacturing, and the use of IT in industrial processes opened the door to a new age of the opti-mized and mechanized production process.
Today, we are experiencing a Fourth Industrial Revolution in which we are doing production in smarter ways by using the innovation and digitalization process, which makes Industry 4.0 a reality.
Industry 4.0 is ultimately beneficial to producers because it reduces the manufac-turing costs involved, increases the production rate, speeds up the production, and
10.7 Conclusion .................................................................................................... 156Bibliography .......................................................................................................... 156Weblinks................................................................................................................. 156
FIGURE 10.1 The journey toward milestone of Industry 4.0. (www.gelifesciences.com/.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-20 19:18:36.
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provides better and more efficient productivity, 360° quality control, and greater product and component traceability. The main technologies that will allow Industry 4.0 to deliver these benefits are the IIoT, automation, and digitalization systems. IIoT is the integration of computation, Internet, and physical processes. In other words, the convergence of business systems with the physical plant controls systems and machines. Adopting the principles of Industry 4.0 and smart processing of produc-tion requires high levels of automation and network infrastructure, so the road to digitalization and innovation can require a huge amount of investment.
Therefore, the governments and industries in the economy have recognized the potential of Industry 4.0 to change the competitive landscape. New organizations have been set up to direct and support the development of Industry 4.0, and substan-tial private and public sector investments are being made.
10.2 WHAT IS INDUSTRY 4.0?
Industry 4.0 is the age of innovation in the manufacturing process with the help of various tools and techniques such as IoT, blockchain, automation, digitalization, and artificial intelligence. This is a type of foundation of modern industrial setup enabled with innovative tools and technologies that allow the vision of the smart factory to become reality.
10.2.1 need of InduStry 4.0 (I-4.0)
The I-4.0 is needed to transform the traditional production process, i.e., from hand-driven machines to automated smart machinery enabled with IoT and AI, i.e., self-regulated and automated machines to improve their overall performance, efficiency, and output.
Industry 4.0 aims at sustainable development of a smart and innovative manu-facturing process in industrial units. Monitoring the data in real time, outlining the production status, and getting regularly updated about the production processes and the status of total output are the main needs of Industry 4.0.
10.2.2 feAtureS of InduStry 4.0
The digitalization, innovation, and AI play a very vital role in the transformation of traditional industry into the Industry 4.0 segment.
Industry 4.0 and able to analyze how the new features are different and how it will be beneficial for producers to gain the upcoming opportunities.
10.2.2.1 Flexibility in the Production ProcessWith the help of digital and innovative tools, the production process can be more flexible and smooth. As a production process, the production of a product requires various techniques to produce the finished product. Digitalization and optimized networking techniques allow flexibility and better coordination between all the pro-duction activities and thus significantly increase the productivity.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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146 Transforming Management Using Artificial Intelligence Techniques
10.2.2.2 Flexible Production EnvironmentsThe digitalization and innovations in the industry help to adopt the changes with the flexibility in production activities as per the market requirement and also maintain a better production output dynamic or flexible environment.
10.2.2.3 Optimized Logistics SolutionsIn the field of logistics, with the help of digitalization and innovations, commodity chains can be calculated and optimized by algorithms. Digitally, the enabled logis-tics system helps in optimizing the operating costs while improving the efficiency of operations.
10.2.2.4 Saving of ResourcesWith the help of new methods of production techniques, manufacturers can able to save the wastage of the natural resources and other raw materials, the innovative manufacturing process is designed and developed in such a way that they can do the optimum utilization of resources throughout their entire production cycle as well as it is also helpful in increasing productivity by reducing energy consumption during the production cycle.
10.2.2.5 Clear Customer OrientationNowadays consumers become more used to digital services, including e-commerce expect to receive the price value and quality product and service from industries. With the help of digitalization and innovation in industries, producers can produce quality products as per the expectations of the customers.
10.2.2.6 Effective Use of All DataThe digitalization and innovation in Industry 4.0 will generate various and a huge amount of data from various sources, and these data are collected and evaluated by the producers, with the help of necessary analytics and AI with data algorithms to predict equipment failures and streamline production processes.
10.3 COMPONENTS OF DIGITAL TECHNOLOGIES IN INDUSTRY 4.0 (FIGURE 10.2)
The components of the digital technologies ecosystem are outlined as follows:
• Mobile Devices: Mobile or communication devices are considered as the heart of Industry 4.0. Mobile devices enabled with the Internet help to record, transmit, and process large amounts of information from one department to another department and the right people in the right format at the right time. Thus, the processing of data helps to drive the operation in a manufacturing plant.
• Internet of Things (IoT): The Internet of Things is a type of network system that processes and stores the data directly via the Internet. This is
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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already implemented in various industries nowadays, and it also continues to grow significantly in the context of Industry 4.0.
• Industrial Internet of Things: The Industrial Internet of Things (IIoT), when applied to the manufacturing industry, is called smart manufactur-ing. Thus, IIoT will revolutionize and boost the manufacturing process by collecting, monitoring, analyzing, and delivering greater amounts of data both faster and more efficiently, and then, the availability of important data on time can help manufacturing firms make faster and better-informed decisions.
• Location Detection Technologies: Location detection technologies are considered as one of the important components of digital technologies in Industry 4.0. This technology is connected with smart sensors to their oper-ations where they place them in very harsh locations to collect needed data.
• Advanced Human–Machine Interfaces: A human–machine interface is also known as man–machine interface (MMI), or human–computer inter-face. It is a component of various hardware and software tools and devices that can handle man–machine or man–computer interactions.
• Authentication and Fraud Detection: Authentication and fraud detection are used in the production process to secure all digital interactions and to detect the unauthenticated data that are not useful and harmful to the pro-duction unit.
FIGURE 10.2 The Industry 4.0 framework and contributing digital technologies. (https://blog.atheerair.com/2017/08/10/building-your-digital-industrial-enterprise-part-1.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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148 Transforming Management Using Artificial Intelligence Techniques
• 3D Printing: 3D printing is considered as a big component and innovative production methodology of Industry 4.0. It helps to produce customized products with improved flexibility and responsiveness to changing condi-tions and customer needs. It allows a shift from mass production to full customization, from centralized to distributed production. Its capability to manufacture a part as a whole also makes it more cost-efficient than tradi-tional manufacturing technologies,
• Smart Sensors: A smart sensor is the combination of a sensor, a micropro-cessor, and communication technology used to convert inputs into readable data and transmit them onward to another production unit. Smart sensors have also played a vital role in the process of production or manufacturing. Today, the mixing of local computing power and the Internet of Things (IoT) has transformed ordinary sensors into smart sensors, enabling them to carry out complex tasks. Greater efficiency in manufacturing can be achieved by integrating smart sensors.
• Big Data Analytics and Advanced Algorithm: Industry 4.0 applies machine learning and AI algorithms to analyze the big data and do the interpretation of the analyzed data and gives information to adjust pro-cesses automatically as needed.
• Multilevel Customer Interaction and Customer Profiling: Customer pro-filing is the process of organizing multiple customers into different specific groups as per their similar profile, preferences, need and demands, which help the producers to target products and services that have to be delivered to the customers and help large enterprises to gain and keep a holistic view of their relationships with customers.
• Augmented Reality (AR) and Wearable Technology: Augmented reality (AR) is very much beneficial to workers who are doing repair and operation tasks and working in manufacturing industries, and it enhances workers’ experience, keeps the workers safe, and also closes the knowledge gap.
• Cloud Computing: Cloud computing means to compute the data stored over the Internet or “the cloud” or in the server. The stored databases used in manufacturing industries for the prompt response, faster innovation, flex-ible resources, and economies of scale.
10.3.1 benefItS of dIgItAlIzAtIon, InnovAtIon, And ArtIfIcIAl IntellIgence
1. Increased productivity: With the help of digitalization, innovation,&and artificial intelligence, the manufacturers can able to produce goods in very little time to complete a difficult task of production inefficient ways without any wastage and it helps to increase productivity.
2. Cost efficiency: By digitalization, innovation, and artificial intelligence in the production process, the manufacturers can improve the quality and reduce the cost of the products and can make efficient production. Overall, the use of the digitalization technique in the manufacturing process reduces
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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subcosts such as equipment management, labor management, marketing, maintenance, and space costs.
3. Being easy to access and always accessible: It is the most important benefit of digitalization that the data can be easily accessed through the cloud or system using any device that has Internet, anywhere, anytime.
4. Enhanced security: Digitalization and AI help to maintain the security of the data and also give early warning signals if they find any problems in the input and production process, and they also maintain the confidentiality of the production unit.
5. Enhanced information preservation: Information stored in physical form can be lost, but the data stored in digital form can be preserved for a long period. The digital data can be store degradable information.
6. Saving of Space: The cost of Real Estate space is expensive and acquires most space while using multipurpose digital machines in the production process, it eliminating more space, reduce cost.
7. Being competitive: Digitalization and innovation in the production pro-cess are much beneficial to small producers and organizations, and artificial intelligence and digitalization have been the mantra of the new age produc-tion process.
8. Being environment-friendly: The digitalization, innovation, and artificial intelligence process reduces pollution, and it is an environment-friendly ini-tiative. It removes the needs of creating unnecessary actions, increasing the eco-friendly quotient of the company.
10.3.2 dIgItIzAtIon, InnovAtIon, And ArtIfIcIAl IntellIgence: AS the bASIS for SucceSS for InduStry 4.0
In a current dynamic business environment, the processes and methods of produc-tion also change and the mass production process becomes more customized with the help of digitalization, innovation, and artificial intelligence. The digitalization, inno-vation, and artificial intelligence technologies help industries for modernization and rapid advancements and also help in increasing the productivity, which is considered as a key to success in future production.
10.3.3 bluePrInt for SucceSS of InduStry 4.0 (fIgure 10.3)
The main key concept of success of industries is the adaptation of innovative tools, techniques, and artificial intelligence in industrial production, i.e., transformation from traditional approach to the modern approach of production techniques, i.e., Industry 4.0.
So, this transformation process is very beneficial to gain a first-mover advantage over the competitors. The concerned person related to the production unit needs to be more committed toward the significant implementation of investments. So, to increase their business and become successful in their particular filed, producers should use the technology as per their business needs and logic.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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150 Transforming Management Using Artificial Intelligence Techniques
Based on a certain study, the author has tried to frame six practical steps that a company needs to implement to sustain in this competitive digital business environment.
10.3.3.1 Map Out Your Industry 4.0 Strategy (Figure 10.4)To make Industry 4.0 advanced and competitive as compared to other producers, then you have to make your strategy as per the requirement of Industry 4.0 as per the company’s profile and product toward becoming a fully digital business unit, so it is very much important to clearly define the mission and vision of the com-pany. Therefore, it is important to analyze own digital mellowness for the upcoming years because some industrial units have already adopted digitalization and artificial intelligence in their business and production operations. By this way, your road map decides and considers the future changes in production techniques for the transfor-mation of the manufacturing operations.
10.3.3.2 Create Initial Pilot Projects (Figure 10.5)Industry 4.0 requires a huge investment for a better outcome. For this, first of all, companies should create initial projects to overcome initial challenges, because com-panies making a huge investment are unable to measure the economic benefit of digitalization. An initial or pilot project to proceed with your business organization
FIGURE 10.3 Blueprint for digital success. (www.pwc.com.)
FIGURE 10.4 Map out your Industry 4.0 strategy. (https://www.pwc.com/m1/en/publications/.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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151A Road map to the Future of Industry 4.0
and it can also help to solve various technical issues which may business expected to come in the final business run. With confirmation and support from the success of the pilot project, you can also gain better experiences, which will help in your long-run business activities and other larger project. So, it is an important task to pick the right projects targeted with a confined scope highlighting the concept of Industry 4.0.
10.3.3.3 Define the Capabilities You Need (Figure 10.6)After the success of the pilot project, you should find out the capabilities needed by your organization. An agile IT system, and technological infrastructure, can improve the operation of your business activities. So, for the successful approach, companies should boost innovative digital business models integrated with smart sensors and artificial intelligence.
FIGURE 10.5 Create initial pilot projects. (https://www.pwc.com/m1/en/publications/industry-40-survey/blueprint-digital-success.html.)
FIGURE 10.6 Define the capabilities you need. (https://www.pwc.com/m1/en/publications/industry-40-survey/blueprint-digital-success.html.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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152 Transforming Management Using Artificial Intelligence Techniques
To implement a new capability in the industry, industrialists need to focus on four strategic key points, i.e., people, process, product, and technology:
Focusing on product and process: Business will be able to define the size, shape, color, design, etc., of the product for the money value to both the producers and con-sumers, as well as the process of manufacturing with low-cost advantages.
Focusing on people and technology: Focusing on people and technology, the industry will be able to select competent staff and workers and develop a strategy for attracting people with the right digital skills and particular technology for complet-ing a particular task.
10.3.3.4 Become a Data Virtuoso (Figure 10.7)Collecting, analyzing, and shorting the quality data, and using them for the right task, is one of the important components for the success of Industry 4.0. Effectively analyzing the processed data will be very much constructive to make Industry 4.0 perfect.
Defining and developing an effective strategy for the analysis of data will require more focus on the following:
• Deep access to the existing database, which is typically different from the database of other vendors.
• Predictive analytics to predict the future.• The use of predictive analytics of data for business-driven development and
decision-making.
So, there must be a focus on improving master data management – by making their reservoir data pool by analytical methods and algorithms to make the industry more productive.
10.3.3.5 Transform into a Digital EnterpriseStrong data analytics and technical skill capabilities are needed to transform the industry into Industry 4.0. Many business industries have adopted creative digital strategy design and rapid prototyping capabilities. It is proved in the current business scenario that a digitalized and innovative environment can only happen with com-mitted leadership. So, to transform their existing industry into Industry 4.0, some business houses have appointed a committee of digital officers or another executive to lead the effort. A digital officer and council can support cross-functional teams in proactively managing a digital pipeline.
10.3.3.6 Actively Plan an Ecosystem ApproachFor affirmative ecosystem can integrate the production unit in both horizontal and vertical integration form. First of all, it is very much important for movers to achieve breakthrough performance by understanding the needs of the business and custom-ers, and the use of the active ecosystem can create and deliver value to the customer.
Fundamentally, an actively planned ecosystem approach is about developing value products and services for their valued customer.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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153A Road map to the Future of Industry 4.0
10.4 IMPACT OF DIGITALIZATION, INNOVATION, AND ARTIFICIAL INTELLIGENCE ON INDUSTRY GROWTH (FIGURE 10.8)
Digitalization, innovation, and artificial intelligence act as drivers of industrial and economic growth. At the production and operational levels, a large majority use smart sensors and artificial intelligence, and the use of more advanced technologies can lead to industrial growth.
Digitalization, innovation, and artificial intelligence in the production processes have a very positive impact on both the growth of the industry and the economy. These elements act as a catalyst for the production and manufacturing industries and have the ability to do their best in a complex environment also. Apart from this, industrial development will be able to generate more income and employment for the common people, which helps in economic growth and prosperity by reducing unem-ployment and also raises the standard of living of the individuals. Thus, understand-ing the strategic potential of innovation and digitalization and the use of artificial intelligence are decisive factors in establishing a sustainable business model.
FIGURE 10.7 Become a data virtuoso. (https://www.pwc.com/m1/en/publications/ industry-40-survey/blueprint-digital-success.html.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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154 Transforming Management Using Artificial Intelligence Techniques
10.5 ISSUES AND CHALLENGES IN INDUSTRY 4.0 (FIGURE 10.9)
Innovation, digitalization, and the use of artificial intelligence have made the indus-try sophisticated and efficient, but apart from this, there are also some concerns and confrontations that occur during the implementation of digitalization and artificial intelligence to make Industry 4.0 much better than the existing one.
The new innovative industry requires more autonomy, but today, the business industry is having a lack of these autonomies and capabilities.
Digital and innovative technologies require a high-bandwidth network because they have to do the heavy work and transfer a high volume of data as various nodes. But, the main concern is that the availability of high bandwidth and superspeed networks is one of the major challenges to the current industry.
Other concerns are also to ensure high quality and integrity of the data that are recorded during the production cycle, and sometimes under some unfavorable cir-cumstances, it is not much possible to incorporate diverse data repositories with dif-ferent nodes.
With the increased digitalization and use of Internet connectivity in Industry 4.0, the privacy and security of significant data are also needed to protect from the cyber-security threats that are increasing radically.
FIGURE 10.8 Transform into a digital enterprise. (https://www.pwc.com/m1/en/publica-tions/industry-40-survey/blueprint-digital-success.html.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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The setup of Industry 4.0 requires a huge cost and investment, due to which most industrialists do not take initiatives to set up Industry 4.0. The requirement of a huge amount of investment for implementing Industry 4.0 seems not feasible for MSMEs also.
10.6 SUMMARY AND OUTLOOK
The current digitalization, innovation, and artificial intelligence age brought so many new prospects and opportunities for manufacturing industries that help to achieve the target of transforming the business model into Industry 4.0. Now, in the current dynamic business environment, rapid digital processes will lead to an increase in the profit ratio of industries. Digitalization and innovation are considered as one of the important drivers of growth and expansion of the business. In the past few years, it has seemed that digitalization and artificial intelligence have experienced revolutionary technological progress. Digitalization, innovation, and the use of arti-ficial intelligence in industries drive economic growth, productivity, and sustainable development. Communication, interactions, processes, and information flow have also been optimized, and hence, the cost of production is decreased, which results in financial benefits to the producers.
FIGURE 10.9 Actively plan an ecosystem approach. (https://www.pwc.com/m1/en/publica-tions/industry-40-survey/blueprint-digital-success.html.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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This leads to the conclusion that digitalization, innovation, and artificial intel-ligence in terms of industrial growth and development for strategic action are most important for industrial and economic growth, respectively.
10.7 CONCLUSION
Digital transformation, innovation, and artificial intelligence can be seen as an emerging issue in the current business environment. It is argued that integration and upgradation of technologies, such as big data, AI, IoT, IIoT, 3D, smart sensors, cloud computing, and associated analytics, cybersecurity, advanced robotics systems, social media, and automation.
The main purpose of innovation and artificial intelligence is to redesign the business organization and transform the industry into Industry 4.0. Through the introduction of innovative and digital technologies for achieving benefits such as improved productivity minimize cost reductions and innovation. Innovation, digital transformation, and artificial intelligence are affecting not only the manufacturing industry but also other sectors of the economy, such as automobile, health, and education.
BIBLIOGRAPHY
Agrawal, R. “Technologies for handling big data.” Handbook of Research on Big Data Clustering and Machine Learning, edited by F. P. Garcia Marquez, IGI Global, Hershey, PA, 2020, 34–49.
Bleicher, J., & Stanley, H. (2016). Digitization as a catalyst for business model innovation a three-step approach to facilitating economic success. Journal of Business Management, 12, 62–71.
Jalota, C., & Agrawal, R. “Analysis of educational data mining using classification.” 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019.
Pathak, S., & Agrawal, R. (2019) “Design of knowledge-based analytical model for organizational excellence.” International Journal of Knowledge-Based Organizations (IJKBO), 9(1): 12–25.
Pessl, E., Sorko, S. R., & Mayer, B. (2017). Roadmap industry 4.0—Implementation guide-lines for enterprises. International Journal of Science, Technology, and Society, 5(6), 193–202.
Remes, J., Mischke, J., & Krishnan, M. (2018). Solving the productivity puzzle: The role of demand and the promise of digitization. International Productivity Monitor, 35, 28–51.
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0—A glimpse. Procedia Manufacturing, 20, 233–238.
WEBLINKS
https://www.etmm-online.com/industry-40-clearly-explained-definition-benefits-and-topics-a-835529/
https://www.myforesight.my/ 2018/02/27/characteristics-of-industry-4-0/https://www.pwc.com/m1/en/publications/industry-40-survey/blueprint-digital-success.html
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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11 A Review of Innovation Diffusion Modelling Literature
Gaurav Nagpal and Udayan ChandaBirla Institute of Technology and Science
11.1 INTRODUCTION
We are living in a world that is undergoing a very rapid digitalization, not only for
the physical goods but also for the services. These innovative products have a unique
characteristic; i.e., they are first adopted by the innovators and last, adopted by the
laggards. The product life cycle is very short on account of fast-changing technology,
consumer behavior, and the changing dynamics of the overall supply and demand
ecosystem. There are multiple generations of the same technology in the consumer
markets that cannibalize the sales of one another. Also, with the increasing digita-
lization, the adoption rate of these products is very fast, which further leads to the
shortening of the product life cycle for a given market potential. There has been a lot
of research to model the diffusion of such products, which has also been extensively
handy to the practitioners in forecasting the demand and making sound operational
as well as strategic decisions related to sales and operations. Also, there is plenty of
research available on inventory modelling and optimization, but the research on the
inventory modelling of innovative products is very scarce. A few studies that are
available on the inventory optimization of technology products are also on single-
period inventory modelling, which is no more a phenomenon these days. So, there is
a need to develop robust multi-period inventory optimization models.
Having discussed the motivation and the need to develop diffusion models for
the innovation-based digital products under the trade credit mechanism, the purpose
CONTENTS
11.1 Introduction 157 ..................................................................................................
11.2 Methodology 158 .................................................................................................
11.3 Literature Review 158 .........................................................................................
11.4 Achievements of the Existing Research 162 .......................................................
11.5 Research Gaps and Future Directions 162 ..........................................................
References 164..............................................................................................................
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158 Transforming Management Using Artificial Intelligence Techniques
of this research paper is to bring forward the summary of the existing literature
that can be used by inventory managers, practitioners, policymakers, and also future
researchers to be further improvised upon.
11.2 METHODOLOGY
The extensive search of the literature on diffusion modelling was carried out on pop-
ular research databases: Springer, Wiley, Scopus, ScienceDirect, JSTOR, EBSCO,
and Web of Science. Although this review does not claim to be an exhaustive one,
the attempt has been made to cover all the possible aspects of diffusion modelling
that have been worked upon. After the first search of the papers using the keywords,
the papers found were screened to filter out the irrelevant papers. For example, the
word diffusion modelling also yielded many papers on chemistry for the diffusion of
chemicals in the solvents, which were not relevant to our theme. After the irrelevant
papers were screened out, the citation chaining was done on the shortlisted papers
to search for more papers relevant to our theme. Also, the snowballing technique
was used to refresh the list of the keywords obtained from the newly discovered
papers. We finally ended up with 89 research papers on the modelling of innovation
diffusions.
11.3 LITERATURE REVIEW
Mansfield (1961) introduced the concept of technological change and the role of imi-
tation in the same. Rogers (1962) came up with the theory of innovation diffusions,
which was the first insightful and revolutionary work on the innovation diffusions.
The most popular work in the modelling aspect of innovation diffusions was done by
Bass (1969) when he came up with the hazard rate function and the adoption rates for
the demand governed by innovation diffusion. This has been widely accepted due to
the simplicity of the approach and the applicability to real-life scenarios.
As early as in the 1970s, Fisher and Pry (1971), Blackman (1975), and Bretschneider
and Mahajan (1980) modelled the technological substitution. One of the earliest
works on dynamic pricing for new product launches was done by Robinson and
Lakhani (1975).
Bass (1980) suggested that the diffusion rates are also dependent upon the price
elasticity of demand and the learning curve of the potential adopters. Van den Bulte
and Lilien (1997) showed that all the three methods of parameter estimation—
ordinary least squares estimation, maximum likelihood estimation, and non-linear
least squares estimation—are subject to bias. Dolan and Jeuland (1981) also used the
experience curve of the consumers to formulate dynamic demand models. Jeuland
and Dolan (1982) emphasized how dynamic pricing can play an important role in
new product planning. Schmittlein and Mahajan (1982) used the maximum likeli-
hood method to estimate the diffusion models. Horsky and Simon (1983) studied
the influence of advertising on the diffusion of innovations. Kalish (1985) added the
effect of price and advertising along with uncertainty. Winer (1985) came up with the
price vector model of demand for consumer durables. Mahajan et al. (1986) assessed
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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159Innovation Diffusion Modelling Literature
various estimation procedures for new product diffusion. Srinivasan and Mason
(1986) minimized the sum of squares of deviations from non-linear relationships
to estimate the new product acceptance. Kamakura and Balasubramanian (1987)
built the factor of repeat purchase, price index, and population change dynamics.
Kamakura and Balasubramanian (1988) used nested models to test the role of price
and influence in innovation diffusion. Meade and Islam (1998) worked on combin-
ing models for innovation diffusion, realizing the fact that one pure model cannot
forecast technological growth. Dockner and Jorgensen (1988) came up with optimal
advertising policies for product diffusion. A few of the useful works in the 1980s
can be attributed to Easingwood (Easingwood et al., 1981, 1983; Easingwood, 1987,
1988).
Horsky (1990) advocated the consumer income, product price, and information
as the three key determinants of technological adoption and suggested that the over-
all market potential is influenced by these three factors. Jain and Rao (1990) studied
the effect of price elasticity of demand on consumer durables. Mahajan et al. (1990)
also performed an insightful review of diffusion modelling literature that existed at
that time. Lilien and Yoon (1990), and Mahajan and Muller (1996) worked upon the
timing of the innovations. Bayus (1992) and Bayus (1994) stated that the product
life cycles are getting compressed with the ever-evolving consumer preferences and
with the advent of newer digital technologies. Bass et al. (1994) came up with an
a daptation of his original diffusion model to incorporate the effect of the decision
variables such as price and advertising spends. Bridges et al. (1995) modelled the
market share as a function of customer expectations. Speece and Maclachlan (1995)
applied the innovation diffusion model to multiple generations of milk container
technology. Putsis (1996) studied the influence of purchasing frequency on the tem-
poral aggregation of innovation products’ demand. Dekimpe et al. (1998) modelled
the timing of adoption across the nations under globalization. Radas and Shugan
(1998) formulated the model for optimal timing and seasonal marketing of new
product launches. Krishnan et al. (1999) came out with an optimal pricing strategy
for new products.
Putsis and Srinivasan (2000) developed the forecasting techniques for macro-level
diffusion models. Srivastava et al. (2001) came up with a model that included initial
and repeat purchases for multiple generations of innovative products and allowed for
leapfrogging. Bass et al. (2000) and Danaher et al. (2001) modelled the technological
substitution as a function of the overall marketing mix. Wejnert (2002) gave the con-
ceptual framework for integrating the diffusion models that have been worked upon
till then. An important work that studied diffusion from the perspective of consumer
psychology is that of Brown and Heathcote (2008). Grasman et al. (2009) analyzed
the measures of central tendency and dispersion for the response times to estimate
the parameters in diffusion models.
Stefan et al. (2010) suggested that the newer products launched witness a
higher growth as compared to the earlier ones. Vulcano et al. (2011) said that
the primary demand of substitutable products can be estimated from the point
of sales data. Vaagen et al. (2011) modelled consumer-directed substitution. Kuo
and Huang (2012) worked on the optimal pricing for multi-generational products.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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160 Transforming Management Using Artificial Intelligence Techniques
Dutilh et al. (2013) modelled the diffusion as a function of the biological age of
the adopter. Germar et al. (2014) suggested that social influence and perceptions
have an important role to play in diffusions and modelled the same. Lerche and
Voss (2016) suggested that diffusion models should be made more parsimoni-
ous. Sachdeva et al. (2016) developed a three-dimensional model of innovation
diffusion and suggested the three key drivers of innovation as the goodwill of
the product, the selling price, and the marketing efforts. Steeneck et al. (2016)
presented a procedure for estimating the demand for substitutable products when
the inventory record is unreliable and only validated infrequently and irregularly.
Vejlgaard (2018) took the example of the television industry and advocated that
culture plays an influential role in the diffusion of innovation, hence concluding
that modelling of the same cannot be pure science. Kumar (2019) stated that social
capital benefits with the innovations. Hambrick (2019) studied the innovation dif-
fusion in the light of the new product launches at GoPro, an American technology
firm engaged in the manufacturing of action cameras and development of mobile
apps and video editing software.
When it comes to the literature on the inventory modelling of substitutable
products, there has been a significant amount of work. Hadley and Whitin (1963)
and Johnson and Montgomery (1974) discussed traditional inventory models with
independent demand under constrained resources. As early as in the 1970s, the
constrained inventory optimization for multiple items under continuous review
was done (Schrady and Choe, 1971). Goyal (1973) determined the economic pack-
aging frequency for jointly replenished items. Kao (1979) developed a dynamic
inventory lot-size model for multiple products with individual and joint set-up
costs. Graves (1979) solved the lot scheduling problem for multiple items on a
machine under deterministic demand. Rosenblatt (1981) compared the solutions
of two approaches: Lagrangian method and fixed cycle approach, for solving the
inventory system for multiple items under constrained budgets. Ben-Daya and
Rouf (1993) solved the single-period inventory model for multiple items under
the constraints of budget and space. The effects of transportation and container
have also been considered in the joint replenishment problem for multiple items
(Ben-Khedher & Yano, 1994). Hahm and Yano (1995) worked on the supply of
multiple components for automotive assembly. Hausman et al. (1998) worked on
the estimation of demand fulfillment probabilities of correlated items with the
independent ordering policies. Hammer (2001) worked on the supply of multiple
refrigerated goods for supermarkets. The scenario of discrete demands under con-
tinuous production for multiple items was considered by Rempala (2003). Abad
and Jaggi (2003) developed a framework for setting the price and optimal credit
period when the demand for the end product is price elastic. There has been plenty
of research on the joint replenishment of multiple items. Chen and Chen (2005a,
2005b) studied the influence of the nature of channel coordination (centralized
or decentralized) on the replenishment of multiple items in a two-echelon supply
chain and concluded that centralized decision making can optimize the costs bet-
ter. Bhattacharya (2005) developed a two-item inventory model for deteriorating
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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161Innovation Diffusion Modelling Literature
items under a stock-dependent demand rate. Panda et al. (2008) used penalties for
deviations from constraints in the goal programming for joint replenishment for
multiple items. Porras and Dekker (2006) developed an efficient method for finding
out the minimum order quantities for the joint replenishment problems. Nilsson et
al. (2007) solved the joint replenishment problem using the spreadsheet technique.
Uthayakumar and Priyan (2013) integrated continuous review with production and
distribution for multiple items in a supply chain. Lee and Lee (2014) developed an
optimal lot-sizing solution for multiple items whose demand is correlated with a
bivariate Gaussian probability distribution. Sadjadi et al. (2015) used a geometric
programming approach and cubic production cost function for joint pricing and
production optimization for multiple items.
Figure 11.1 taken from Norton and Bass (1987) shows how the successive genera-
tions in the case of technology products enjoy a higher market potential as well as a
much faster diffusion rate as compared to the earlier generations.
Figure 11.2 shows that the initial sales of the product are attributed to the innova-
tors, while the latter portion of sales is attributed to the laggards, with the cumulative
adoption following the S-shaped curve.
Table 11.1 shows the cross-tabulation of a few very popular works on diffusion
modelling and classification of the same into broad categories.
FIGURE 11.1 The diffusion of successive innovations according to Norton Bass model
(1987).
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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162 Transforming Management Using Artificial Intelligence Techniques
11.4 ACHIEVEMENTS OF THE EXISTING RESEARCH
The extant research on the diffusion of innovations has been very helpful to the
academic researchers, practitioners, and policymakers. The beauty of the existing
research is that it can be applied to the physical goods’ innovations as well as service
innovations. The existing research has also been able to quantify the diffusion rate
of innovations by quantifying the sociocultural phenomenon that drives the spread
of innovations in the market.
The literature on diffusion modelling has been fairly exhaustive and compre-
hensive. There also exists plenty of work on advertising influence, optimal pricing,
dynamic pricing, and optimal launch timing of new products.
Many of the existing models have also been validated with the real-life data of
the innovation launches and have been proved to be a reliable predictor of the diffu-
sion rates. They have been of great utility to the policymakers while coming up with
the social welfare models that disrupt the behavior of the common public for social
gains. Also, they have helped the businesses to accurately and precisely predict the
sales patterns of the new launches and plan accordingly.
11.5 RESEARCH GAPS AND FUTURE DIRECTIONS
A few directions in which it can be further extended are linking the consumer psy-
chology and peer-group influence with the new product acceptance. The demo-
graphic characteristics of the adopters (in terms of age, social status, income group,
FIGURE 11.2 The product life cycle of innovations with the cumulative adoption given by
the S-shaped curve.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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163Innovation Diffusion Modelling Literature
cultural values, education level, etc.) can also be related to the probability of dif-
fusion. Although creating diffusion models on such a macro-level will make them
more cumbersome and difficult to forecast by data requirements, such models can be
expected to give more accurate forecasts.
Also, we often observe that innovation and imitation influence varies with time
over the product life cycle, while the existing diffusion models have considered only
constant coefficients for innovation and imitation.
Another extension can be working out the optimal product design to achieve the
desired diffusion rate. In the case of technology products, hardware and software
are bundled together. The work on the diffusion models of such product bundles is
currently missing.
Also, the existing models assume that there are a sufficient number of adopters
available at the introduction stage of the product for the take-off, while that may not
be the case always. Therefore, diffusion models that explain the take-off phenom-
enon as well need to be explored.
TABLE 11.1History of Research on Modelling of Innovation Diffusion
Multiple Generations
Repeat Purchase
Aggregate/Micro-LevelAuthor(s) and Year Leapfrogging
Bass (1969) No No Aggregate No
Fisher and Pry (1971) Yes No Aggregate No
Blackman (1975) Yes No Aggregate No
Bretschneider and Mahajan (1980) Yes No Aggregate No
Kamakura and Balasubramanian
(1987)
Yes No Aggregate No
Norton and Bass (1987) Yes No Aggregate No
Kalish (1985) Yes No Aggregate No
Wilson and Norton (1989) Yes No Aggregate No
Lilien and Yoon (1990) Yes No Aggregate No
Bayus (1992) Yes No Aggregate No
Mahajan and Muller (1996) Yes No Aggregate No
Lilien et al. (1981) Yes Yes Aggregate No
Rao and Yamada (1988) Yes Yes Aggregate No
Hahn et al. (1994) Yes Yes Aggregate No
Srivastava et al. (2001) Yes Yes Individual Yes
Young (2009) No No Individual No
Bridges et al. (1995) No No Individual No
Winer (1985) No No Individual No
Wejnert (2002) No No Individual No
Jiang and Jain (2011) Yes Yes Individual Yes
Sachdeva et al. (2016) No No Individual No
Benhabib et al. (2019) No No Individual No
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164 Transforming Management Using Artificial Intelligence Techniques
The existing models have not worked on the integration of time and space
d imensions that can help formulate the distribution strategies across the geographies.
Future research can also take place in the areas of model estimation and the use of
time-varying parameters in lieu of the current practice of using constant parameters.
Not only should the parameters vary with time, but they also vary in a dynamic
manner, which can be best captured by building stochastic demand models as
suggested by Putsis (1998).
There also lies a scope of adding the influence of marketing variables and
initiatives in diffusion models, and the linkage of the coefficients of innovation and
imitation with the marketing variables.
Some of the models developed have not been validated empirically. The parameter
estimation procedure for such models can be carried out as a future action. The
es timation of models can be made much easier either by putting restrictions on the
values of parameters to bring down the complexity of computation, or by building in
the effect of seasonality in the sales of innovative products.
It would be interesting to study the impact of the business cycles and the
m acroeconomic indicators on the diffusion of innovations.
A few other possible extensions of this research can be considering the influence
of supply restrictions and market interventions such as patent violations, competition
entry, and the intensity of market rivalry, on the diffusion of innovation products.
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167Innovation Diffusion Modelling Literature
Mahajan, V. and Muller, E. (1996). Timing, diffusion and substitution of successive generations of technological innovations: The IBM mainframe case. Technological Forecasting and Social Change, 51(2), 109–132.
Mahajan, V., Muller, E., and Bass, F. M. (1990). New product diffusion models in marketing: A review and directions for research. Journal of Marketing, 54, 1–26.
Mansfield, E. (1961). Technical change and the rate of imitation. Econometrica, 29(4), 741–766.
Meade N. and Islam, T. (1998), Technological forecasting: Model selection, model stability, and combining models. Management Science, 44, 1115–1130.
Norton, J.A. and Bass, F.M. (1987). A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Science, 33(9), 1069–1207.
Panda, D., Kar, S., and Maiti, M. (2008), Multi-item EOQ model with hybrid cost parameters under fuzzy/fuzzy-stochastic resource constraints: A geometric programming approach, Computers & Mathematics with Applications, 56(11), 2970–2985.
Porras, E. and Dekker, R. (2006). An efficient optimal solution method for the joint replenishment problem with minimum order quantities. European Journal of Operational Research, 174, 1595–1615.
Putsis, W.P. (1996). Temporal aggregation in diffusion models of first-time purchase: Does choice of frequency matter? Technological Forecasting and Social Change, 51, 265–279.
Putsis, W.P. (1998). Parameter variation and new product diffusion. Journal of Forecasting, 17, 231–257.
Putsis, W.P. and Srinivasan, V. (2000). Estimation techniques for macro diffusion models. Mahajan, V., Muller, E., and Wind, Y. (eds.) New-Product Diffusion Models, Kluwer Academic Publishers, London, pp. 263–291.
Radas, S. and Shugan, S. (1998). Seasonal marketing and timing new product introduction. Journal of Marketing Research, 35(3), 296–315.
Rao, A.G. and Yamada, M. (1988). Forecasting with a repeat purchase diffusion model. Management Science, 34(6), 734–752
Rempala, R. (2003). Joint replenishment multiproduct inventory problem with continuous production and discrete demands. International Journal of Production Economics, 81–82, 495–511.
Robinson, B. and Lakhani, C. (1975). Dynamic price models for new product planning. Management Science, 10, 1113–1122.
Rogers, E.M. (1962). Rogers’ Innovation Diffusion Theory. 5th Edition. IGI Global, Hershey, PA.
Rosenblatt, M.J. (1981). Multi-item inventory system with budgetary constraint: A comparison between the Lagrangian and fixed cycle approach. International Journal of Production Research, 19, 331–339. doi:10.1080/00207548108956661.
Sachdeva, N., Kapur, P.K., and Singh, O. (2016). An innovation diffusion model for consumer durables with three parameters. Journal of Management Analytics, 3(3), 240–265
Sadjadi, S.J., Hesarsorkh, A.H., Mohammadi, M., and Naeini, A.B. (2015). Joint pricing and production management: A geometric programming approach with consideration of cubic production cost function. Journal of Industrial Engineering International, 11(2), 209–223.
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Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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Srinivasan, V. and Mason, C.H. (1986). Nonlinear least squares estimation of new product diffusion models. Marketing Science, 5(2), 169–78.
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Steeneck, D. et al. (2016). Estimating demand for substitutable products when Inventory records are unreliable, http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1050640&dswid=9364
Stefan, S., Eitan, M., and Renana, P. (2010). Does new product growth accelerate across technology generations? Marketing Letters, 21(2), 103–120.
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Vaagen, H., Wallace, S., & Kaut, M. (2011). Modelling consumer-directed substitution. International Journal of Production Economics, 134(2), 388–397.
Van den Bulte, C. and Lilien, G.L. (1997), Bias and systematic change in the parameter estimates of macro-level diffusion models. Marketing Science, 16, 338–353.
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Vulcano, G., Ryzin, V.G., and Ratliff, R. (2011). Estimating primary demand for substitutable products from sales transaction data. Operations Research, 60(2), 313–334.
Wejnert, B. (2002). Integrating models of diffusion of innovations: A conceptual framework. Annual Review of Sociology, 28, 297–326.
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Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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169
12 Artificial Intelligence (AI) in ClassroomsThe Need of the Hour
Neha Puri and Geeta MishraAmity University
12.1 INTRODUCTION
Over the past few years, the education landscape has changed dramatically:
Demographic changes impacting household make-up, multiple learning opportuni-
ties accessible to children, wealth disparities, the worldwide economy demanding
new skills from students, and perpetual technological developments are some of the
factors affecting studies. Artificial intelligence can affect course planning, and indi-
vidualization of training and assessment, giving some insightful perspectives into
the future (the end of tests, your lifelong learning companion) without becoming
a target of tech speculation. Artificial intelligence is a computer, robot, or product
approach for thinking about how highly educated people think. Artificial intelligence
has recently opened a lot of possibilities and insights globally and across markets
including the higher education market.
CONTENTS
12.1 Introduction 169 ................................................................................................
12.2 Definition of Artificial Intelligence 172 ............................................................
12.3 Characteristics of Artificial Intelligence 172 ....................................................
12.4 Components of Artificial Intelligence 174 ........................................................
12.5 Benefits of AI Utilization 175 ...........................................................................
12.6 Requirements of AI 176 ....................................................................................
12.7 Opportunities of AI in Education 177 ...............................................................
12.8 Requirements of Shifts in Education 178 ..........................................................
12.9 Restructuring Examination 179 ........................................................................
12.10 Threats due to AI 179 ........................................................................................
12.11 Implications for Teaching & Teachers 180 ........................................................
12.12 What Artificial Intelligence Is Planning to Do in the Field of Education? 181 ......
References 182..............................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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170 Transforming Management Using Artificial Intelligence Techniques
For the last few decades, the rise of technology within the education sector has
been phenomenal. This is certainly the case as we realize that technology-based
education has become prevalent in every virtual classroom environment. For exam-
ple, we are surrounded within today’s classroom by devices such as smart boards,
AVs, computers, laptops, tablets, and phones. AI education researchers have been
studying how the two interact for decades. While it is tempting to think that AI’s
primary vision of education is to minimize marking load—a possibility made real by
automatic essay scoring—the reach of implementations goes beyond that.
Artificial intelligence not only has the potential to improve student learning per-
formance, but also decreases and automates the cost of access to education. The
objective of AI is to progress in human knowledge-related functions such as reason-
ing, thinking, and question. Using brain–machine interface tools that are capable of
measuring when an individual is fully focused on information and learning activi-
ties (Chen, 2015; González et al., 2015), supercomputers such as IBM’s Watson can
provide an autonomous educator’s presence. The study explores the phenomenon of
the advent of artificial intelligence technology and higher education. It discusses the
educational effects of emerging technologies on how students learn and how univer-
sities educate and develop (Popenici and Kerr, 2017). Artificial intelligence (AI) has
become a part of our daily lives whether or not we know it, and whether or not we
acknowledge it. Over the years, it has grown to that level in a slow but steady way.
The things we like to buy, such as clothes and shoes, on the Internet and the shows
we watch on TV are all affected by AI to a different extent. An Artificial intel-
ligence teaching assistant will work 24 hours a day. In future, artificial intelligence
would create an adequate program and this would provide the instructor with the
time needed to support the students and their learning. However, with artificial intel-
ligence, there exists the possibility of the replacement of teachers. The replacement
of teachers is indeed top provoking for the work in serious consideration in place of
the artificial intelligence system.
The world will change with artificial intelligence (AI), and higher education is no
exception. AI is going to change the way we work, how we learn, and how we live.
This research analyzes how artificial intelligence impacts higher education. This
qualitative research would explore the developments in higher education caused by
artificial intelligence (Keng and Yizhi, 2018). This article focuses on the improve-
ments AI can make to tackle long-term educational priorities. It addresses five priori-
ties that would support:
• instructors for each learner;
• 21st century social skills;
• learning experience data;
• universal access to global classrooms; and
• lifelong and life-long learning (Woolf et al., 2013).
They leave behind individual information footprints in this age of big data, culminat-
ing in an abundance of data, allowing human and societal behaviors to be objectively
quantified and, therefore, easily tracked, modeled and, to some extent, predicted.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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171AI in Classrooms: Need of the Hour
This phenomenon surrounding information footprints is called “datafication”
(Mayer-Schönberger and Cukier, 2014), and it also affects the information footprints.
Identifying AI’s educational policy ramifications addresses four main challenges:
1. Ensuring equal and fair use of AI in education.
2. Enhancing AI for education and learning.
3. Enhancing work and life skills growth in the AI age.
4. Ensuring open and auditable use of educational data.
Artificial intelligence is a cluster of technologies that allow machines to act with
higher levels of intelligence and emulate sensory, comprehensive, and acting human
capabilities. Thus, computer vision and audio processing by acquiring and pro-
cessing images, sound, and speech can actively perceive the world around them.
Artificial intelligence will continue to play an increasingly significant role in the
area of education. It’s imperative that artificial intelligence began participating in
education sectors still can’t put back educators, but due to changed learning courses
and including artificial intelligence support the instructor by abolishing lengthy
paperwork, artificial intelligence’s that can be a disruption and a valuable change in
education (Sisodiya, 2019). This article summarizes the current ideas from artificial
intelligence (AI) to technology applications, i.e., the description to predict the numer-
ous future applications of artificial intelligence to education and technology in gen-
eral as well as identifying the challenges that the wide-ranging applications of these
innovations in the classroom must address (David McArthur, 2005).
Evolutionary learning is praised as a possible game-changer in higher educa-
tion, a panacea with which universities solve the iron triangle riddle: material,
expense, and access. Although the research is minimal, this study and a few
other studies similar to this show that today’s adaptive learning systems have a
n egligible impact on learning outcomes (Pérez, 2015). Even though the word is
not widely used, AI has already become a part of our everyday lives. There are
so many signs of AI around us: the eye sensor for face recognition, the quest, and
advice for the Amazon or Flipkart, personal phone assistants, and Google’s self-
driving cars. In this study, the authors assess the students’ level of satisfaction with
AI-aided schooling. Students are highly satisfied with AI-aided research and favor
an AI-based learning environment. According to this survey, the performance of
the students is also increased in the education system using artificial intelligence
technologies (Jetawat, 2017).
Using artificial intelligence can undermine the traditional one-size-fits-all con-
ventional teaching system and damage it. Equations of machine learning have also
begun to help teachers fill the gaps while indicating which disciplines the students
fail most. The examples in which artificial intelligence is used in research are as fol-
lows (Lynch, 2017):
• Thinkster Math: Deemed by The New York Times “the math app with
an extraordinary human touch,” Thinkster Math is a tutoring software that
incorporates traditional math curriculum with a customized teaching style.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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172 Transforming Management Using Artificial Intelligence Techniques
• Brainly: This is a Web site for the social broadcasting of classroom ques-
tions. Brainly allows users to ask questions about homework using machine
learning algorithms to weed out spam and get instant, checked answers
from fellow students. The app also helps students work together to get
proper answers by themselves. If you have a student in charge of directing
the school, he or she can answer the questions.
• Content Technologies, Inc.: Typical textbooks are only suitable for stu-
dents looking for cardboard cut-out templates, talking, solving problems,
and processing information the same way. Content Technologies, Inc. (CTI)
is an AI company that uses deep learning to create personalized textbooks
that fit the needs of individual courses and learners. Teachers load syllabi
into Content Technologies, Inc.’s engine.
• Mika: Carnegie Learning’s Mika provides AI-based tutoring services to
children who are too busy for after-school tutors and too vulnerable to sole
concentration in a sea of other pupils associated with Thinkster Math. And
if you believe that one-to-one coaching is only for elementary school stu-
dents with a long division, Mika is experienced in tutoring in higher educa-
tion to fill gaps in college school.
• Netex Education: This encourages teachers to develop curriculum across a
variety of formats and digital devices. Even for the most technically illiter-
ate students, the site helps incorporate interactive elements such as audio,
video, and self-assessment into their educational lesson plans, all within
a customized digital cloud platform. Utilizing Netex, teachers can create
multiple student resources that can be exchanged on any digital platform
while providing opportunities for video conferences, interactive meetings,
and tailor-made assignments and learning metrics that can provide a visual
representation of each student’s personal growth.
12.2 DEFINITION OF ARTIFICIAL INTELLIGENCE (FIGURE 12.1)
Artificial intelligence is described as a machine capable of mimicking intelligent
human behavior. It makes our software systems smarter and more streamlined. It
also elevates any technology’s reliability quotient. Artificial intelligence is an attempt
to create computers that historically could only do things through human cognition.
Over the years, computer scientists have experimented with many different mecha-
nisms. Technologists have sought in the last surge of AI passion to mimic human
intelligence by inserting detailed rules into computers, a methodology called expert
systems (Zeide, 2019).
12.3 CHARACTERISTICS OF ARTIFICIAL INTELLIGENCE (FIGURE 12.2)
• AI could engage in human or other computer interactions, interpreting con-
text, and formulating a suitable response.
• AI may view the information supplied and take appropriate action to achieve
its mandated objectives.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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173AI in Classrooms: Need of the Hour
• AI will internalize new information to optimize its efficacy and change its
actions accordingly.
• AI can carry out the majority of its decision-making processes without
needing human resources.
• AI helps in the easy assessment of the work done by the student.
FIGURE 12.1 Artificial intelligence.
FIGURE 12.2 What made artificial intelligence a household word?
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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174 Transforming Management Using Artificial Intelligence Techniques
12.4 COMPONENTS OF ARTIFICIAL INTELLIGENCE (FIGURE 12.3)
Methods for artificial intelligence (AI) are on the rise of education and have achieved enormous popularity in recent years. The 2018 Horizon Study, EDUCAUSE (2018), highlights artificial intelligence and advanced learning systems as important devel-opments in educational technology, with 2–3 years to be implemented.
1. Speech Recognition: Many intelligent systems, while a person talks to it, can hear and understand the language in terms of sentences and definitions. It can handle different accents, slang words, background noise, shifts in the sound induced by wind, etc.
2. Perception: Using perception, the world is studied by different human or artificial sense organs and internal processes that investigate the environ-ment by objects and their characteristics and connections to the surround-ing world. Evaluation is complicated by the fact that the same object will make several different appearances on different occasions, depending on the angle from which it is viewed.
3. Linguistic Intelligence: The ability to speak, recognize, and use phonolog-ical (speech sounds), syntactic (grammar), and semantic (meaning) mecha-nisms, for example Narrators and Speakers.
4. Learning: Learning is defined by different forms. The best is learning from trial and error. A simple program to solve mate-in-one chess problems, for example, can randomly attempt moves until one is found to achieve mate.
FIGURE 12.3 Components of artificial intelligence. (Adapted from Copeland (2000); http://www.alanturing.net/turing_archive/pages/Reference%20Articles/What%20is%20AI.html.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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175AI in Classrooms: Need of the Hour
The program considers the successful move, and it can automatically gener-
ate the answer the next time the computer has the same question.
5. Problem Solving: Problem-solving methods break into approaches for
unique and general uses. A special-purpose approach is adapted to a par-
ticular issue and often takes advantage of very different spatial aspects in
which the problem is located. A general-purpose approach refers to a vast
range of different problems.
6. Reasoning: It is the set of processes that enables us to base action,
decision-making, and prediction upon. There are two categories: inductive
and deductive reasoning.
Inductive Reasoning Deductive Reasoning
To make broad general statements, it
makes detailed analyses.
It opens with a general statement and discusses the
possibility of drawing a clear, logical conclusion.
Even though in a statement all the
premises are true, inductive reasoning
allows the conclusion to be false.
If something is generally true of a category of things,
it is also true of all those class members.
Example—“Rita is a teacher. Rita is a
graduate, so every teacher is a
graduate.”
Example—“All women over 60 years of age are
grandmothers. Sonali is 65 years of age. So, Sonali is
a grandmother.”
12.5 BENEFITS OF AI UTILIZATION
In addition to saving time, the capacity of information systems to provide this level
of insight can also provide the level of detail that teachers may not understand at face
value or may not be apparent. Classroom AI systems have the capability of evaluat-
ing and linking multiple data sources to known trends. This can determine the root
causes of issues and also contribute to more consistent results in different classes,
irrespective of the teaching staff’s background (Borge, 2016). Advances made in the
core research areas of AI, such as knowledge representation, comprehension of the
natural language, reasoning/inference, and learning/discovery, will inevitably con-
tinue to be expressed in the field of education, especially in education and diagnostic
systems (Jones, 1985).
1. 24/7 Accessibility
• Machines, like humans, do not require frequent breaks and refreshments.
• They can be designed for working long period of time and can always
do the job without getting bored, irritated, or even sick.
• Using computers, we can also expect the same kind of outcomes, regard-
less of time, season, and so on, those we cannot expect from people.
2. Development day by day
• A smartphone, along with dress, food, and shelter, has also become
the fourth necessity for humans in their daily needs. If you are using a
smartphone, it indirectly means you knowingly or unknowingly enjoy
the AI.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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176 Transforming Management Using Artificial Intelligence Techniques
• Designing automation approaches by thinking and interpretation has
become a common phenomenon in our daily lives.
• We have got Alexa, which works on the command given by the user.
• We also have lady Siri to help us out in iOS devices, or Cortana in
Windows devices.
• GPS support for long drives and journeys is also provided.
• Mobile is one of the apt indicators that show how we use artificial intel-
ligence daily to reduce day-to-day barriers.
• The artificial intelligence system identifies and senses the face of the
person and marks the individuals as we share the images on social net-
work sites.
• Artificial intelligence has widely been adopted for data processing, to
bring effective control by financial institutions and the banking sector.
Fraud identification is one of the best advantages of AI.
3. Virtual Help
• Highly advanced businesses have already put in place robots to con-
nect with their clients on behalf of people, minimizing manpower. It
is digital assistants or replicas that will greatly reduce the demand for
human resources.
• In AI machines, emotions can only be identified rationally.
• The robots cannot recognize a user’s emotional aspect. Also, it has been
programmed to think only critically and take the right software deci-
sions based on the machine’s current knowledge.
• The computers that cannot identify the emotions of the consumers will
eventually dissatisfy them. We require human intervention in this situ-
ation. This latency helps to rule out machine intelligence. But still, in
other ways, it benefits.
4. Managing Repetitive Jobs
• Repeated jobs are naturally boring. With the support of AI algorithms,
these things can be done quickly. These kinds of work do not necessitate
much knowledge about them.
• Robots can work much faster than humans and can do multitasking to
get the best results.
5. Error Reduction
• The benefit of artificial intelligence is that, with a greater accuracy, it
allows us to reduce errors and increase the likelihood of achieving a
greater precision.
• It can be used in several situations including a space technology process.
12.6 REQUIREMENTS OF AI (FIGURE 12.4)
• Games—AI plays an important role in the system in talking about the need
for a wide range of possible roles in strategy games based on deep under-
standing, for example chess, river crossing, and N-queens problems.
• Natural language processing—Interact with machines that interpret natural
language in human terms.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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177AI in Classrooms: Need of the Hour
• Expert systems—Users with explanations and guidance are created by a
computer or software.
• Vision systems—Systems recognize and explain machine vision sources
and identify them.
• Speech recognition—Most AI-based speech recognition systems can hear
and communicate in sentences and interpret the words people speak to
them. Siri and Google assistant are two examples.
• Handwriting recognition—The handwriting recognition app scans the text
written on a paper and recognizes and converts the letter types into editable
text.
• Intelligent robotics—Robotics can perform human instructions.
12.7 OPPORTUNITIES OF AI IN EDUCATION
The new challenges of the knowledge age warrant a drastic reform from the uni-
versity in its traditional educational canons. The models focused on artificial intel-
ligence promise a very substantial improvement in education for all the different
levels, with an unparalleled qualitative shift: to provide students with an objective
personalization of their learning according to their needs (Ocaña-Fernandez, 2019).
Artificial intelligence can have a significant impact on our educational future. There
are growing suppliers of technologies that are launching AI solutions to improve
new-age learning. Generally, the transformation of our institutional system comes
from the private sector, but innovations also trickle into important school districts
within states.
FIGURE 12.4 Requirements of AI. (Adapted from Introduction to Artificial Intelligence—
Selvamanikkam, 2018.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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178 Transforming Management Using Artificial Intelligence Techniques
At the same time, AI education can get more accessible and inclusive. Various
tutoring programs, i.e., learning applications with a skill-based curriculum, are being
developed around the globe. Such AI-enabled platforms will add eyes to the global
classrooms. It will empower not only students but teachers to upgrade to current
trends as well.
• Artificial intelligence has opened up a wide range of possibilities and
pros pects throughout the world and across different sectors including the
higher education industry.
• Artificial intelligence not only has the potential to enhance learning
effectiveness amongst students, but also reduces and optimizes the cost of
access to education. It can be utilized to leverage its impact both in learning
and in teaching.
• Artificial intelligence can be leveraged to customize the personal experience
of a learner by customizing the content, automated evaluations, admission
and on-boarding process, and personalized content delivery (Chatterjee,
2018)
• Artificial intelligence can serve as a tool to overcome the challenges faced in
the segment of admissions, student engagement, and placements. AI-enabled
chatbots can be conveniently used to address the common queries faced by
the students, which would prevent the students in waiting in long queues
to reach out to the admission counselors. The chatbot can be configured to
answer questions about financial assistance, scholarship, hostel facilities,
and placement opportunities, and the service is provided 24/7.
• Similarly, it can be leveraged in placements by campuses, too, in screening
the potential candidates and eliminating any human bias that may be there.
It would help in tapping the right applicant as per the requirements of dif-
ferent companies.
• These days when the college students are more comfortable with using
a rtificial intelligence, mobile technologies, and social media applications,
it would also enhance their engagement and learning as they can have
access to learning sessions and videos at a time that is convenient to
them.
12.8 REQUIREMENTS OF SHIFTS IN EDUCATION (FIGURE 12.5)
Artificial intelligence is reaching the education system. The use of artificial intel-
ligence will make certain tasks easier. Using artificial intelligence, for example,
grading can be done fast and easily (Lynch, 2018). As artificial intelligence (AI)
technology evolves and joins different industries, it might enable colleges and uni-
versities to pursue an adaptive, rigorous, and individualized student experience. AI
could potentially lead to a less expensive and more responsive approach to higher
education by enhancing student outcomes and helping institutions scale up educa-
tion quality. While artificial intelligence and education might seem like a futuristic
invention, it is present in today’s life and education systems. We can make life easier
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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179AI in Classrooms: Need of the Hour
for both the students and teachers with the help of artificial intelligence. Education
through artificial intelligence offers an incentive for any pupil to pursue professional
education and individualizes learning (Lynch, 2018).
Artificial intelligence is required in the field of education to create the following
parameters that might be advantageous for the students:
1. Paperless classroom.
2. Lifelong learning.
3. Personalized learning.
4. Learning analytics.
5. Collaborative learning.
6. Robot teachers.
7. Adaptive e-textbooks.
12.9 RESTRUCTURING EXAMINATION (FIGURE 12.6)
Teachers have to handle multiple responsibilities such as assessment, grading, paper
setting, creating mark sheets, and tracking each student’s performance. If these tasks
are made easy for them, they would focus more on the development of the course,
quality teaching, and skill development. AI programs will assist teachers in all these
activities, not only automating these tasks but also rendering them knowledgeable.
To incorporate artificial intelligence in the education sector, it requires a rigorous
change in the traditional way of conducting the examination. Artificial intelligence
brings into the picture the parameters shown in Figure 12.6.
12.10 THREATS DUE TO AI
AI is developing at an incredible speed, and sometimes, it seems magical. Investigators
and programmers claim that AI will become incredibly powerful that it would be dif-
ficult for humans to control it.
Human beings built AI systems by integrating into them all the knowledge, due to
which humans now seem to be at risk. There are also some obstacles that higher edu-
cation institutes are expected to face as they embrace artificial intelligence. Few of
FIGURE 12.5 Shifts in education. (Author’s compilation.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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180 Transforming Management Using Artificial Intelligence Techniques
them are provided below. AI should always limit authorization and provide economic
support to improved academic achievement and instructional support.
Privacy regulations are also a big limitation as they always need to be revised to
address AI frameworks’ ability to track and use information for scientific analyses.
Another constraint is the interface with students. In case AI assumes few current
job duties, for example, it will evaluate and respond to student inquiries, directors
and employees are most likely to move their concentration to respond to complex
questions and to interface with students on deeper dimensions (Jain and Jain, 2019)
• Privacy Threats
Theoretically, an AI system that recognizes speech and understands
natural language is capable of understanding any communication on e-mails
and phones.
• Human Dignity Threats
AI technologies have already started to replace the people of a few
industries. This should not substitute people in those professions where they
occupy dignified positions relevant to principles such as teacher, physician,
lawyer, and police officer.
• Security Risk
The auto-improving AI systems can become so powerful that it can be
very difficult to stop following their targets, which can lead to extreme
consequences.
12.11 IMPLICATIONS FOR TEACHING & TEACHERS (FIGURE 12.7)
By designing advanced data collection algorithms to provided detailed and person-
alized user input, AI shines like the most qualified, easily identifies the needs of a
student, and develops a fitting evaluation (Swathi, 2018). Artificial intelligence can
affect the instructional design, learning, and evaluation of individualization, provid-
ing some interesting perspectives for the future (“end of tests, your lifelong learn-
ing companion”) without becoming a target of tech hysteria. The immense legal,
FIGURE 12.6 Restructuring examination. (Author’s compilation.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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181AI in Classrooms: Need of the Hour
technological, and pedagogical problems that lie ahead are highlighted with the util-
ity of artificial intelligence.
12.12 WHAT ARTIFICIAL INTELLIGENCE IS PLANNING TO DO IN THE FIELD OF EDUCATION?
Paul Mumma, CEO of Cerego, said that
One major aspect coming into focus in the future is the use of AI to gather insights on
students and their knowledge. At Cerego, we use AI and machine learning to empower
educators with insights on what students are learning and mastering, and what they
are not. With more back-end awareness, educators can understand where students are
struggling and where they thrive. By having clear insights into students’ learning pat-
terns and their areas of struggle, teachers will, in turn, be better able to tailor their
material.
Technology has accelerated things by implementing better communication chan-
nels between teachers and students. Artificial intelligence has already become part
of our lives, and it is therefore essential that older students are exposed to ideas
around us. Instead of conventional assessments that focus on small samples of
what was learned by students, AI in education-driven tests will be integrated into
practical learning experiences, such as games and collaborative projects, and will
assess all the learning that takes place as it occurs (Johnson, 2019). Artificial intel-
ligence is anticipated in the USA. According to the Artificial Intelligence Industry
Study in the U.S. Education Sector, education will rise by 47.5% from 2017 to 2021
(Marr, n.d.). There are lot many advantages of artificial intelligence in the field of
education:
• Free up instructor time so they can work on assignments that still require
human intelligence.
FIGURE 12.7 Reorientation of teachers. (Author’s compilation.)
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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182 Transforming Management Using Artificial Intelligence Techniques
• Complement current learning metrics with just-in-time insights into the
achievements, difficulties, and desires of learners that can be used to
influence their learning experiences.
• Help learners to acquire skills in the 21st century by encouraging us to build
effective and relevant skills.
• Provide new insights from traditional assessments that are difficult or
impossible to ascertain.
• For example, datasets might help teachers understand how learners get
answers, not just when they have chosen the right answer.
• AI in education will help us get rid of the stop-and-test process that today’s
evaluation is going through.
• Instead of conventional assessments that focus on small samples of what
was learned by students, AI in education-driven tests will be integrated into
practical learning experiences, such as games and collaborative projects,
and will assess all the learning that takes place as it occurs.
Franz Chen, CEO of Ponddy Education, said that
With the integration of IoT and 5G, AI has the potential to replace classrooms with
virtual classrooms. We see this already as online schools, tutoring and course offerings
become more prevalent, and we move towards the mass customization of education.
Education will be redefined as continuous learning versus discrete curricula-based
courses. The industry will refocus our lens “in the classroom” from how AI tools are
being used to teach to how students are using AI to learn. We need to educate our stu-
dents not only with AI but about AI. Students need to be trained on how to use AI as
they become future workers who will shape the world.
REFERENCES
Borge, M. N. (2016). Artificial intelligence to improve education/learning challenges. International Journal of Advanced Engineering & Innovative Technology (IJAEIT), 2(6), 10–13.
Chatterjee, P. (2018). Artificial Intelligence and Higher Education. CXO Insights, Siliconindia.Chen, X. W. (2015). High-speed spelling with a noninvasive brain-computer interface.
Proceedings of the National Academy of Sciences, 112(44), E6058–E6067.Copeland, J. (2000). AlanTuring.net. Retrieved from http://www.alanturing.net/turing_
archive/pages/Reference%20Articles/What%20is%20AI.html.David McArthur, M. L. (2005). The roles of artificial intelligence in education: Current prog-
ress and future prospects. Journal of Educational Technology, 1(4), 42–80.EDUCAUSE. (2018). EDUCAUSE learning initiative and the new media consortium.
Retrieved from https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf.
González, V. R., Robbes, R., Salvador, G., and Medina, S. (2015). Measuring concentra-tion while programming with low-cost BCI devices: Differences between debugging and creativity tasks. In Schmorrow, D.D., and Fidopiastis, C.M. (eds), Foundations of Augmented Cognition, pp. 605–615. Springer, Cham, Switzerland.
https://backend.educ.ar/refactor_resource/getBook/1097.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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Jain, S. J. and Jain, R. (2019). Role of artificial intelligence in higher education—An empirical investigation. IJRAR-International Journal of Research and Analytical Reviews, 6(2), 144z–150z.
Jetawat, P. L., Lal Bhari, P. and Jetawat, A. (2017). The future potential trends, issues, and suggestions of artificial intelligence in the Indian education system. Imperial Journal of Interdisciplinary Research (IJIR), 3(1), 2078–2080.
Johnson, A. (2019). https://elearningindustry.com/ai-is-changing-the-education-industry-5-ways.
Jones, M. (1985). Applications of artificial intelligence with in education. Computers & Mathematics with Applications, 11(5), 517–526.
Keng, S. and Yizhi, M. (2018). Artificial intelligence impacts on higher education. Thirteenth Annual Midwest Association for Information Systems Conference (MWAIS 2018), At St. Louis, Missouri.
Lynch, M. (2017). 5 Examples of artificial intelligence in the classroom. Retrieved from https://www.thetechedvocate.org/5-examples-artificial-intelligence-classroom/
Lynch, M. (2018). 7 Ways that artificial intelligence helps students learn. Retrieved from https://www.theedadvocate.org/7-ways-that-artificial-intelligence-helps-students-learn/
Marr, B. (n.d.). https://bernardmarr.com/default.asp?contentID=1541.Mayer-Schönberger, V. and Cukier, K. (2014). Big Data: A Revolution That Will Transform
How We Live, Work, and Think. Eamon Dolan/Mariner Books, Boston.Ocaña-Fernandez, Y. V.-F. (2019). Artificial Intelligence and its Implications in Higher
Education. Propósitos y Representaciones, 7(2), 536–568.Pérez, M. C. (2015). Informing and performing: A study comparing adaptive learning to
traditional learning. Informing Science: The International Journal of an Emerging Transdiscipline, 18, 111–125.
Popenici, S. and Kerr, S. A. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning. 12, 1–33.
Selvamanikkam, M. (2018). Introduction to artificial intelligence. Retrieved from https://becominghuman.ai/introduction-to-artificial-intelligence-5fba0148ec99.
Sisodiya, C. D. (2019). Future education with artificial intelligence. International Journal for Research in Engineering Application & Management (IJREAM), 5(2), 893–897.
Swathi, V. S. (2018). Artificial intelligence and its implications in education. International Conference on Improved Access to Distance Higher Education Focus on Underserved Communities and Uncovered Regions, IDEA-2018.
Woolf, B. P., Lane, H. C., Chaudhri, V. K., and Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66–84. doi:10.1609/aimag.v34i4.2490.
Zeide, E. (2019). Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. Retrieved from https://er.educause.edu/articles/2019/8/artificial-intelligence-in-higher-education-applications-promise-and-perils-and-ethical- questions.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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185
13 The Role of Social Media in MarketingA Case Study of World Vision Rwanda
S. N. SinghUniversity of Kigali
Sunita SinghalKCC Institute of Legal and Higher Education
CONTENTS
13.1 Introduction 186 ..............................................................................................
13.2 Background of the Study 186 ..........................................................................
13.3 Problem Statement 187 ....................................................................................
13.4 Objectives of the Study 187 .............................................................................
13.4.1 General Objective 187 ......................................................................
13.4.2 Specific Objectives 187 .....................................................................
13.5 The Scope of the Study 188 .............................................................................
13.5.1 Geographical Scope 188 ...................................................................
13.5.2 Time Scope 188 ................................................................................
13.6 Defining Social Media 188 ..............................................................................
13.6.1 Conceptualization of Social media 188 ............................................
13.7 The Role of Social Media 188 .........................................................................
13.8 Conceptual Framework 189.............................................................................
13.9 Empirical Review 189 .....................................................................................
13.10 Impact of Social Media on Customer Relationship 190 ..................................
13.11 Impact of Social Media on Brand Name 190 ..................................................
13.12 How Social Media Are Formed? 190 ..............................................................
13.13 Research Design 191 .......................................................................................
13.13.1 Target Population 191 .......................................................................
13.13.2 Study Population 191........................................................................
13.13.3 Sample Design 191 ...........................................................................
13.13.4 Sample Size 191 ................................................................................
13.13.5 The Sample Size of the Study 192 ....................................................
13.13.6 Sampling Techniques 192 .................................................................
13.13.7 Data Collection Instruments 192 ......................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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186 Transforming Management Using Artificial Intelligence Techniques
13.1 INTRODUCTION
Rwanda is facing various challenges, and one of them is poverty. The government of
Rwanda and social media organizations, both local and international, are concerned
with this problem. In the process of reducing poverty, social media and partner-
ships are formed by the government and social media organizations. This research
focused on the role of social media in marketing to achieve objectives. In particular, a
social media organization, World Vision International, and how it partners with other
organizations, both public and private, form a collaboration that reduces poverty in
Rwanda.
13.2 BACKGROUND OF THE STUDY
World Vision International, a non-governmental organization, which is working on
an international level which was founded by Robert Pierce in 1950. It is an evan-
gelical Christian relief development organization that works as a humanitarian aid
and advocacy institution working for the betterment of children, upliftment of fam-
ilies, and improvement of communities to overcome poverty and injustice. It has
44,000 staff across the world and works with approx. 100 million people living close
to 100 countries worldwide. World Vision International, sometimes referred to as
World Vision Partnership, consists of different World Vision offices from across the
world, therefore forming a partnership on its own that is aimed at reducing pov-
erty and advocating for justice. World Vision Rwanda is one of the World Vision
International’s country offices and was established in 1987. World Vision Rwanda
currently runs 17 projects and employs 215 staff members. One of the issues that will
be explored is the role of social media in marketing.
13.13.8 Secondary Data 192 ..........................................................................
13.14 Results and Discussion 193 .............................................................................
13.14.1 Respondent Profile 193 .....................................................................
13.14.2 Gender-wise Classification of Respondents 193 ...............................
13.14.3 Age-wise Classification of Respondents 194 ....................................
13.14.4 Marital Status 194 .............................................................................
13.14.5 Education Level 195 .........................................................................
13.14.6 Respondents Working with World Vision Rwanda 195 ....................
13.14.7 Members Understand Social Media at Any Stage of the
Needed Service 195 ..........................................................................
13.14.8 World Vision Rwanda’s Operating Hours Are Convenient
for Clients 196 ...................................................................................
13.14.9 The World Vision Rwanda Tells the Members Exactly the
Time the Service Will Be Performed 196 ........................................
13.14.10 The World Vision Rwanda Gives the Members Prompt
Service 197.......................................................................................
13.14.11 Summary of Major Findings 197 ......................................................
13.15 Limitations of the Study 197 ...........................................................................
Bibliography 198..........................................................................................................
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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187Role of Social Media in Marketing
Rwanda is a mountainous sub-Saharan country surrounded by East African coun-
tries with a population of about 13 million. This relatively small country faces major
challenges including chronic poverty.
Various organizations, including government ministries, have over the years come
together to address the problem of poverty in Rwanda. This research aims not only
to contribute to the literature on poverty in Rwanda but also to add to the literature
on the role played by social media in marketing and the development of Rwanda with
an emphasis on a case study of World Vision International and its efforts to reduce
poverty in Rwanda.
13.3 PROBLEM STATEMENT
Social media in marketing is used to advertise the activities of some institutions
and other companies to sell their products at the local and international levels, but
in Rwanda, social media in marketing is not properly used as it must be in recent
times. In this chapter, the different types and characteristics of social media will
be discussed. Since World Vision Rwanda works with the government of Rwanda
as a partner, this chapter will look at their social media strategy. The role played by
international donors is explored in this research. One of the emerging questions is
whether the intervention of international donors is helpful to societies, in terms of the
“side effects” of these interventions. That is, how do World Vision Rwanda and its
collaborating partners sustain development initiatives even after their projects have
phased out?
13.4 OBJECTIVES OF THE STUDY
The objectives of the study have been divided into two parts as general and specific
objectives as depicted below:
13.4.1 GENERAL OBJECTIVE
The general objective of the chapter is to study the role social media play in market-
ing in Rwanda with a case study of World Vision Rwanda along with the study of the
nature of social media used by World Vision Rwanda and its partners in the process
of addressing poverty in Rwanda.
13.4.2 SPECIFIC OBJECTIVES
The specific objectives of the study are as follows:
a. To examine the impact of social media in the creation of brand awareness of
world vision in Rwanda
b. To assess the impact of social media on population relationship in world
vision in Rwanda
c. To determine the impact of social media marketing on the reduction of pov-
erty in the population of Rwanda.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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188 Transforming Management Using Artificial Intelligence Techniques
13.5 THE SCOPE OF THE STUDY
The scope of the study refers to the breadth of a study. This study will fall in the area
of social media as a marketing tool in Rwanda, specifically world vision located in
Nyarugenge District.
13.5.1 GEOGRAPHICAL SCOPE
The study was conducted at world vision located in Kacyiru in Gasabo District,
Kigali City, Rwanda.
13.5.2 TIME SCOPE
In this research, the study will be conducted for 3 months, and secondly, the study
will look for information of the last 5 years from 2017 to 2019.
13.6 DEFINING SOCIAL MEDIA
According to Ranchod, there are various and sometimes opposing definitions of
social media. Similarly, Seligman states that the concept of social media serves
different meanings to different people. Social media ranges from being perceived
as existing between the state and the market to being inseparable from the state.
According to Howell and Pearce, the concept of social media reflects a variety of
understandings of the relationship between the individual, state, and society.
13.6.1 CONCEPTUALIZATION OF SOCIAL MEDIA
According to Seligman (1992: ix), the idea of social media has its roots in the tradi-
tions of Western politics. Lewis states that social media was introduced after the
Cold War as a strategy by policymakers to strengthen democracy and development.
Howell and Pearce show that social media established itself at the beginning of the
21st century as an important concept in the field of policy development and practice.
Whitfield states that the origin of social media was as a theoretical construct used to
understand phenomena, rather than as an object of research. Furthermore, Whitfield
(2002:5) conceptualizes social media in two ways, namely as an idea and as a pro-
cess. Social media as an idea is perceived as a united entity that is separate from the
state and that has a relationship with democracy. Secondly, as a process, it involves
historical issues, personal social media, external influences, international relations,
and lastly, incentives involved in politics and resources.
13.7 THE ROLE OF SOCIAL MEDIA
Glasius, Kaldor, and Anheier view the role of social media as involving three
dimensions, namely new public management and welfare of the state; social capi-
tal and participation; and lastly, an instrument of achieving transparency, account-
ability, and improved governance. Similarly, Edwards states that social media
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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189Role of Social Media in Marketing
has three developmental roles, namely the economic role, which secures liveli-
hoods and provides services where the state is weak; the social role, whereby it
becomes a pool of caring, intellectual innovation, and cultural life; and lastly, the
political role, which involves promoting accountability, transparency, and good
governance.
13.8 CONCEPTUAL FRAMEWORK
The conceptual framework of any study is its summarized form given in the sche-
matic diagram. It establishes the relationship between the independent and depen-
dent variables used in the chapter. In this study, the given independent variables and
dependent variables have been used.
13.9 EMPIRICAL REVIEW
The resource dependency theory, as stated by Davis and Cobb, revolves around three
main ideas, namely the importance of social contexts; strategies used by organiza-
tions to enhance their self-reliance and pursue interests; and lastly, the importance of
power in understanding external and internal activities of organizations. According
to Davis and Cobb, power is something that distinguishes the resource dependency
theory from other approaches.
Similarly, Pfeiffer perceives power as “the basic energy to initiate and sustain
action translating into reality”. In other words, in structures that do not have hierar-
chies as a way of getting things done, power is an important tool to get ideas imple-
mented. According to Hillman, Withers, and Collins, in the resource dependency
theory, the issue of power is important for controlling vital resources. Organizations
often try to reduce others’ power over them by increasing their own power showing
supremacy over others.
The policy collaboration approach, according to Klijn, was strongly affected by
the resource dependency approach and the concept that organizational social media
can be studied in the context of organizational problems or resources. The resource
INDEPENDENT VARIABLES
MARKETING
Customers
Customer relationship
Brand name
Increase in sales
SOCIAL MEDIA
Internet
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190 Transforming Management Using Artificial Intelligence Techniques
dependency theory is concerned with the exchange processes between organiza-
tions, whereby organizations get certain resources from other organizations. Sharing
and exchanging resources is a rationale behind creating policy social media. These
resources may include money, staff, or services. Hearn and Mendizabal also state
that another function of social media is mobilizing resources to maintain resource
dependency. Some of these resources include funding and services that improve the
work of actors through capacity development.
13.10 IMPACT OF SOCIAL MEDIA ON CUSTOMER RELATIONSHIP
Social media, according to Kickert, Kljin, and Koppenjan, are characterized by
actors who are part of a collaboration as well as the relations these actors have with
each other. In other words, policy outcomes are a result of the actors’ relationships
within the collaboration. Important relationships are between the structure of the col-
laboration and the participants in the collaboration; the collaboration and its political
and socioeconomic contexts; and lastly, the policy collaboration and the outcome.
Furthermore, policy social media are also characterized by their non-hierarchical
perception of the policymaking process.
13.11 IMPACT OF SOCIAL MEDIA ON BRAND NAME
Social media play a key role in implementation. Pressman and Wildavsky state that
policy implementation depends on complex chains of joint interactions. Similarly,
O’Toole, Hanf, and Hupe argue that actors involved in implementation are not only
drawn from government units but rather may also come from other actors outside the
government. The term social media can be used to refer to these actors extending
beyond those included in the normal style of delivery which works from the center.
In other words, the term social media can be used in implementation to describe
a team that consists of both government and non-government actors who attempt
to implement a certain policy. With these said, however, Marsh and Smith argue
that, generally, there is not much agreement concerning the nature and role of social
media. However, the creation of social media across different organizational levels,
according to Sandstrom and Carlsson, is necessary because social media could lead
to more effective public management. The importance of social media, according to
Sandstrom and Carlsson (2008:507), is that they form part of a continuous process of
building and rebuilding institutional arrangements, but they also affect policymak-
ing and society in general.
13.12 HOW SOCIAL MEDIA ARE FORMED?
Sandstrom and Carlsson (2008:505) state that social media evolve with the realiza-
tion of participants that they need each other and to share the advantages of collective
action. Carlsson argues that collective action simply means a group of people united
by common interests. The aspect of collective action, therefore, is important in social
media.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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191Role of Social Media in Marketing
However, according to Hovland, the first step is to clarify the policy change or
objective that is being aimed at, followed by identifying all stakeholders, or interest
groups associated with the problem at hand, who might be interested.
13.13 RESEARCH DESIGN
This chapter used a descriptive design. The reason behind the use of the method is
the ascertainment of a systematic description of the characteristics of the variables.
Qualitative as well as quantitative data from both World Vision Rwanda and second-
ary sources have been used for analytical study and characterization of variables and
situations.
13.13.1 TARGET POPULATION
The total population of the research was 70 employees of World Vision Rwanda
(headquarters) located in the Gasabo District.
13.13.2 STUDY POPULATION
13.13.3 SAMPLE DESIGN
The sample size used in this study was 60 respondents representing 70 employees
working in selected departments: 25 employees from finance, 35 from accounting,
and 10 from auditing. The study considers a sample size within the cost constraint
and provides the potential to find an independent-variable effect.
13.13.4 SAMPLE SIZE
According to Kothari, the number of items to be chosen for the study, out of the pop-
ulation, is a sample size. Thus, the sample size of the population has been selected
using the following formula:
Nn =
1 2+ N e( )where n is the sample size, N is the population of the study, and e is the sampling
error (0.05).
Category Population Percentage
Finance 25 36
Accounting
Auditing
Total
35
10
70
50
14
100
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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192 Transforming Management Using Artificial Intelligence Techniques
70Therefore, n =
1 + 70( )0.05 2
70n =
1 + 0.175
70n = = 60
1.175
13.13.5 THE SAMPLE SIZE OF THE STUDY
13.13.6 SAMPLING TECHNIQUES
The study used a stratified random sampling method for its purpose. As the population
of the study is not homogeneous, the stratified random sampling combining both
stratified and simple random sampling methods has been used. This technique will
be helpful in the data collection, analysis, and interpretation as per the strata. This
chapter also used a purposive sampling technique that helps the researcher to choose
respondents who qualify the matching parameters required to fulfill the purpose of
the study, specifically the management of World Vision Rwanda.
13.13.7 DATA COLLECTION INSTRUMENTS
To collect correct, appropriate, adequate, and reliable information, the research work
used both types of data, i.e., primary and secondary, from World Vision Rwanda
data. The instrument's questionnaire method and documentary review have been
applied.
13.13.8 SECONDARY DATA
The sources of secondary data are textbooks and journals available. The collected
data include both qualitative and quantitative information. A very important source
of secondary data that helped the researcher in the study is the Web site of World
Vision Rwanda.
Category Population Sample Size Percentage
Finance 25 24 36
Accounting
Auditing
Total
35
10
70
32
4
60
50
14
100
Source: World Vision Rwanda Data.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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193Role of Social Media in Marketing
13.14 RESULTS AND DISCUSSION
13.14.1 RESPONDENT PROFILE
The personal information of the members includes their age, gender, marital status,
educational level, and working experience.
13.14.2 GENDER-WISE CLASSIFICATION OF RESPONDENTS
More than half (58.4%) of the respondents were female, and the remaining (41.6%)
were male. From the findings, the researcher observed that the number of female
respondents is more than the number of male respondents in World Vision Rwanda.
13.14.3 AGE-WISE CLASSIFICATION OF RESPONDENTS
The above statistical data indicate that 10 (17%) respondents were aged less than
25 years, 22 (37%) respondents were aged between 25 and 34 years, and 20 (33%)
respondents were aged between 35 and 44 years, and 8 (13%) respondents were aged
45 and over. This shows that the majority were in the age-group 25–34 years.
13.14.4 MARITAL STATUS
Gender Frequency Percent
Female 35 58.4
Male 25 41.6
Total 70 100.0
Age Frequency Percent
Less than 25 years 10 17
25–34 years 22 37
35–44 years 20 33
45 years and above 8 13
Total 60 100
Marital Status Frequency Percent
Single 20 33
Married 24 40
Others 16 27
Total 60 100
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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194 Transforming Management Using Artificial Intelligence Techniques
The above-given table indicates that 20 (33%) research respondents were single,
24 (40%) were married, and 16 (27%) were others. This shows that the majority of
respondents were married.
13.14.5 EDUCATION LEVEL
The table presents that 11 (18%) research participants have A2, 21 (35%) have
diploma, 24 (40%) have bachelor’s degrees, and 4 (7%) have others. This shows that
the majority have a bachelor’s degree.
13.14.6 RESPONDENTS WORKING WITH WORLD VISION RWANDA
The table indicates that 16 (27%) respondents had less than 1 year of experience, 24
(40%) had between 1 and 5 years of experience, 20 (33%) had more than 6 years of
experience. This shows that the majority had between 1 and 5 years of experience.
13.14.7 MEMBERS UNDERSTAND SOCIAL MEDIA AT ANY STAGE OF THE NEEDED SERVICE
Education Level Frequency Percent
A2 11 18
Diploma
Bachelor’s degree
Others
21
24
4
35
40
7
Total 60 100
Years of Experience Frequency Percent
Less than 1 year 16 27
1–5 years 24 40
6 years and above 20 33
Total 60 100
Frequency Percent
Strongly disagree 4 7
Disagree 12 20
Neutral 14 23
Agree 20 33
Strongly agree 10 17
Total 60 100
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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195Role of Social Media in Marketing
In the above table, the respondents indicate their views on social media through
Employees understand to the customer at any stage of needed service that shows 4
comprising 7% of respondents strongly disagree, 12 depicting 20% of respondents
disagree, 14 representing 23% respondents neutral, 20 showing 33% respondents
agree, and 10 representing 17% research respondents strongly agree. Then, consid-
ering the above results, the majority of respondents accepted the fact which allows
confirming that members do understand the importance of social media.
13.14.8 WORLD VISION RWANDA’S OPERATING HOURS ARE CONVENIENT FOR CLIENTS
Based on the above table, respondents indicate their views on services quality
through World Vision Rwanda operating hours is convenient to the clients that shows
7 members depicting 13% of respondents strongly disagree 10 answers showing 16%
of respondents disagree, 12 depicting 20% of research respondents are neutral, 21
depicting 35% of research respondents agree, 10 representing 16% of respondents
strongly agree. Then, considering the above results, more than half of respondents
accepted the fact which allows confirming that members do understand the impor-
tance of social media in marketing.
13.14.9 THE WORLD VISION RWANDA TELLS THE MEMBERS EXACTLY THE TIME THE SERVICE WILL BE PERFORMED
In the table, the respondents indicated their views on The World Vision Rwanda
tells the members exactly the time the service will be performed. 3 representing
Frequency Percent
Strongly disagree 7 13
Disagree 10 16
Neutral 12 20
Agree 21 35
Strongly agree 10 16
Total 60 100
Frequency Percent
Strongly disagree 3 5
Disagree 11 18
Neutral 10 17
Agree 12 20
Strongly agree 24 40
Total 60 100
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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196 Transforming Management Using Artificial Intelligence Techniques
5% of respondents strongly disagree, 11 representing 18% of respondents disagree,
10 answers representing 17% of respondents are neutral, agree shows 12 research
respondents representing 20% of research respondents agree, 24 representing 40%
of respondents strongly agree. Then, considering the above results, the majority of
respondents accepted the fact which allows confirming that members do understand
the importance of tells the members exactly the time the service will be performed.
13.14.10 THE WORLD VISION RWANDA GIVES THE MEMBERS PROMPT SERVICE
In the above table, the respondents indicated their views on the World Vision Rwanda
gives the members prompt service that shows 8 answers showing 13% respondents
strongly disagree, 10 showing 17% of research respondents disagree, 10 depicting 17%
of research respondents neutral, agree with shows 22 research respondents depict-
ing 36% of respondents strongly agree, 10 depicting 17% of research respondents
strongly agree. Then, considering the above results, half of the respondents accepted
the fact which allows confirming that members do understand the importance of
gives the members prompt service.
13.14.11 SUMMARY OF MAJOR FINDINGS
Based on the research question concerning strategies used by World Vision Rwanda
to reduce poverty through social media in marketing in Rwanda, it was deduced
from the interview responses and documents from World Vision Rwanda that this
organization uses community projects to drive development, which eventually leads
to improved lives, and thus reduces poverty though social media in marketing.
Furthermore, social media and partnerships with other organizations were found
to play an important role in the poverty reduction efforts through social media.
For example, some of the respondents identified the importance of social media as
leading to the wider impact of projects.
In other words, social media are generally seen as involving multiple actors and
information exchange. It can, therefore, be deduced that this organization is part
of the organizations discussed in this chapter as well as others that have not been
discussed, which share the same objectives.
Policy social media have been defined by Benson, a group of clusters of
or ganizations which are connected by dependencies on resources and also different
Frequency Percent
Strongly disagree 8 13
Disagree 10 17
Neutral 10 17
Agree 22 36
Strongly agree 10 17
Total 60 100
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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197Role of Social Media in Marketing
from other clusters by breaks present in the structure of resource dependencies. The
issue of resources, according to the interviews, is one of the major reasons World
Vision Rwanda is involved in social media. From the interviews, it was deduced that
there was a flow of resources into and from World Vision Rwanda and its partners.
13.15 LIMITATIONS OF THE STUDY
• Some respondents were hostile and unwilling to cooperate with the
researcher to get what he needs, but the researcher tried to convince them.
• The given study required lots of technical information, and the sources
were limited. This forced the researcher to apply multiple forms of data
collection.
• Some respondents did not understand English; this was a barrier to the
researcher to collect information quickly.
BIBLIOGRAPHY
African Development Fund. (2016). Annual Report 2016. http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/20120285-EN-AnnualReport-2006-UK.
Agranoff, R. (2006). Inside collaborative networks: Ten lessons for public managers. Policy Studies Review Public Administration Review. 66, 56–65. doi:10.1111/puar.2006.66.issue-s1.
Agranoff, R. and McGuire, M. (1999). Managing in collaborate settings. Policy Studies Review. 16(1), 18–41.
Ames, B., Bhatt, G., and Plant, M. (2011). Taking stock of poverty reduction efforts. Finance and Development, 39(2), 1-5.
Amrit, C. (2012). Improving Coordination in Software Development Through Social and Technical Collaborate Analysis. Unpublished Document.
Ki-moon, U. S.-G. (2011). The Millennium Development Goals Report 2011. United Nations, New York. https://www.un.org/millenniumgoals/pdf/(2011_E)%20MDG%20Report%202011_Book%20LR.pdf
Ki-moon, U. S.-G. (2015). The Millennium Development Goals Report 2015. United Nations, New York.
https://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf
Pathak, S, and Agrawal, R. (2019). Design of knowledge-based analytical model for organi-zational excellence. International Journal of Knowledge-Based Organizations (IJKBO), 9(1), 12–25.
Skelcher, C. (2011). Public-Private Partnerships and Hybridity. In Ferlie, E., Lynn, L.E., and Pollitt, C. (eds). The Oxford Handbook of Public Management. Oxford: Oxford University Press.
Sumner, A. (2007). Meaning versus measurement: Why do ‘economic’ indicators of poverty still predominate? Development in Practice, 17(1), 4–13.
Tahboula, R. (2010). Implementation in a Policy Collaborate Setting: A Case Study of the The Association For Rural Advancement’s Implementation Of The Farm Dwellers’ Project From 1994 Until Today. University of KwaZulu-Natal, Pietermaritzburg.
UNESCO. (2017). Poverty and participation in Social media. Proceedings of UNESCO/CROP Round Table Organized at the World Summit for Social Development. Paris: UNESCO Publications.
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-04-21 00:17:16.
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World Bank. (2016). Rwanda Sharing Growth by Reducing Inequality and Vulnerability: Choices for Change—A Poverty, Gender and Social Assessment. Report No. 46297. http://pubdocs.worldbank.org/en/596391540568499043/worldbankannualreport2016.pdf
World Bank. (2017). Rwanda Poverty Assessment. Report No. 13171. Available Online. http://pubdocs.worldbank.org/en/908481507403754670/Annual-Report-2017-WBG.pdf
World Vision. (2009). Rwanda. Available Online. Accessed on November 19, 2013. http://stg1.worldvision.org/staging/content.nsf/learn/world-vision-rwan
World Vision International. (2018). World Vision’s Theory of Change. Available Online. https://www.wvi.org/international/publication/world-visions-theory-change
World Vision Rwanda Annual Report. (2012). World Vision 2012 Annual Report.
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Index
Administrative Efficiency 19
AI and Credit Decisions 113
AI and Fraud Preventions 113
AI and Personalized Banking 114
AI and Process Automation 114
AI and Risk Management 113
AI and Trading 114
AI in Fashion 131
Alibaba 135
Amazon 135
Articulated Robotic Arms 70
Artificial Intelligence 2
Automatic Guided Carts (AGC) 68–70
Automating operations 132
Automation 2
Banking Sector 107
Banking Services 109
Benefits of AI in fashion 131
Big Blue 60
Big Data 1
Big Data Analytics 4
Building Brand and Reputation 66
Business Functions 110
Chatbots 12
Compensation Management 33
Content Personalization 57
Conventional Teaching System 171
Customer Engagement 2
Customer Experience 111
Customized 17
Deep Learning 16
Detecting Fraud 116
Diffusion Modelling 158
Digital Enterprise 152
Digital Marketing 6
Digitalization 13
Economic development 11
Ecosystem 66
Educational Institutions 97
Employee Attrition 57
Enhanced Customer Service 131
Evolutionary Learning 171
Forecasting Techniques 159
Healthier Inventory 65
HR and Deep Learning 59
HR and Machine Learning 57
Human Interactions 26
Human Resource Management 24
Humanitarian Aid and Advocacy 186
Image and Video Recognition 59
Imitation Influence 163
Improved Product Discovery 133
Industry 4.0 144
Information Support 84
Innovation 18
Innovation Diffusion 158
Intellectual Structure 82
Intelligent MKIS 78
Inventory Modelling 157
Likelihood Estimation 158
Machine Learning 6
Machine Assisted Designs 131
Managing Inventory 133
Manufacturing 68
Marketing Decision Support 74
Marketing Information System 78
Marketing Optimization 2
Monotonous and Repetitive Tasks 24
Netex Education 172
Nike On Demand 137
Non-Linear Least Squares Estimation 158
Nordstrom 133
Performance Management 26
Personalized Banking 108
Poverty 186
Production Process 129
Productivity Method 82
Recommendation Engines 59
Recruitment Using AI 26
Remember the Human Touch 61
Resource Dependency Theory 189
Restructuring Examination 179
Robotic Process Automation 27
Robotics 6
Smart Solutions 16
Social Media 3
Strategic Issues 26
Students Acquisition 17
Students Affairs 17
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-05-04 23:56:09.
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200 Index
Sub-Saharan Country 187
Sustainable “Green” Practices 65
SWOT Analysis 5
Teaching Methodology 99
Technological Advancement 6
VF Corporation 138
Virtual Stores 5
Visual Searching 133
Warehouse Management 64
Workforce Productivity 65
World Vision 186
World Vision International 186
Transforming Management Using Artificial Intelligence Techniques, edited by Vikas Garg, and Rashmi Agrawal, Taylor & Francis Group, 2020. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/undip-ebooks/detail.action?docID=6353352.Created from undip-ebooks on 2021-05-04 23:56:09.
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