Transforming Management Using Artificial Intelligence ...

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.

Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

Transforming Management

Using Artificial Intelligence

Techniques


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.crcpress.com

https://www.crcpress.com

Transforming Management

Using Artificial Intelligence

Techniques

Edited by

Vikas Garg and Rashmi Agrawal


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

First edition published 2020

by CRC Press

6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742

and by CRC Press

2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN

© 2021 Taylor & Francis Group, LLC

CRC Press is an imprint of Taylor & Francis Group, LLC

Reasonable efforts have been made to publish reliable data and information, but the author and

publisher cannot assume responsibility for the validity of all materials or the consequences of

their use. The authors and publishers have attempted to trace the copyright holders of all material

reproduced in this publication and apologize to copyright holders if permission to publish in this

form has not been obtained. If any copyright material has not been acknowledged please write and

let us know so we may rectify in any future reprint.

Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced,

transmitted, or utilized in any form by any electronic, mechanical, or other means, now known

or hereafter invented, including photocopying, microfilming, and recording, or in any information

storage or retrieval system, without written permission from the publishers.

For permission to photocopy or use material electronically from this work, access www.copyright.

com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA

01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@

tandf.co.uk

Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are

used only for identification and explanation without intent to infringe.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://www.copyright.com

http://www.copyright.com

mailto:[email protected]

mailto:[email protected]

https://lccn.loc.gov

https://lccn.loc.gov

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

..............................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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 ......................................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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!


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://taylorandfrancis.com

xiii

ContributorsShalini AggarwalChandigarh University

Mohali, Punjab, India

Jalaj AroraChandigarh University


Priyanka BhardwajChandigarh University


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



Deepa GuptaGL Bajaj Institute of Management &

Research


Mukul GuptaGL Bajaj Institute of Management &

Research


Vijay Prakash GuptaInstitute of Technology & Science

Ghaziabad, Uttar Pradesh, India

Teena HassijaManav Rachna International Institute of



Shikha KapoorAmity University Noida


Simran KaurManav Rachna International Institute of



Gurpreet Singh MatharouManav Rachna International Institute of



Geeta MishraAmity University Noida


Gaurav NagpalDepartment of Management

Birla Institute of Technology and

Science

Pilani, Rajasthan, India

Neha PuriAmity University Noida



Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

xiv Contributors

Sonam RaniGL Bajaj Institute of Management &

Research


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


Priyanka SrivastavaManav Rachna International Institute of



Nidhi TandonManav Rachna International Institute of



Munish TiwariMangalmay Institute of Management &

Technology



Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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..............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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:


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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 ................................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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:


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.

REFERENCES

Aayog, N. I. T. I. (2018). National strategy for artificial intelligence. Discussion Paper June 2018.

Agrawal, R., & Gupta, N. (2017) Educational data mining review: Teaching enhancement. S. Tamane, V.K. Solanki, N. Dey (eds.), Privacy and Security Policies in Big Data. IGI Global, Hershey, PA, 149–165.

Bass, D., & Huet, E. (2017). Researchers combat gender and racial bias in artificial intelligence. Bloomberg. Retrieved June 29, 2018. https://www.bloomberg.com/news/articles/2017-12-04/researchers-combat-gender-and-racial-bias-in-artificial-intelligence

Best, K. L., & Pane, J. F. (2018). Privacy and interoperability challenges could limit the ben-efits of education technology. RAND.

Borge, N. (2016). Artificial intelligence to improve education/learning challenges. International Journal of Advanced Engineering& Innovative Technology (IJAEIT), 2(6), 10–13.

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implica-tions. Science, 358(6370), 1530–1534.

Dall, I., Dickson, D., Payne, R., & Tierney, S. (2018). Transforming education: Empowering the students of today to create the world of tomorrow, published by Microsoft.com. https://news.microsoft.com/uploads/prod/sites/66/2018/06/Transforming- Education-eBook_Final.pdf

DFKI. (2015). Intelligent solutions for the knowledge society. The German Research Center for Artificial Intelligence. Enhanced Learning, 12(1), 1–10.

Dodgson, M., & Gann, D. (2017). Artificial intelligence will transform universities. Here’s how. World Economic Forum, The University of Queensland.

Gibney, E. (2017). Google reveals secret test of AI bot to beat top Go players. Nature News, 541(7636), 142.

Gleason, P., & Dynarski, M. (2002). Do we know whom to serve? Issues in using risk factors to identify dropouts. Journal of Education for Students Students Placed at Risk, 7(1), 25–41.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.bloomberg.com

https://www.bloomberg.com

http://Microsoft.com

https://news.microsoft.com

https://news.microsoft.com


Jalota, C., & Agrawal, R. (2019). Analysis of educational data mining using classification. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE.

Klutka, J., Ackerly, N., & Magda, A.J. (2019). Artificial intelligence in higher Education: Current issues and future applications. Learning House.

Miailhe, N., & Hodes, C. (2017). The third age of artificial intelligence. Field actions science reports. The Journal of Field Actions, (Special Issue 17), 6–11.

Perez, S. (2016). Microsoft silences its new AI bot Tay, after Twitter users teach it racism. Tech Crunch. March 24, 2016. https://techcrunch.com/2016/03/24/microsoft-silences-its-new-a-i-bot-tay-after-twitter-users-teach-it-racism/

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teach-ing and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22.

Schleicher, A. (2015). Schools for 21st-century learners: Strong leaders, confident teachers, innovative approaches. In International Summit on the Teaching Profession. OECD Publishing, Paris.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://techcrunch.com

https://techcrunch.com

http://publications.jrc.ec.europa.eu



https://www.forbes.com


https://hbr.org

https://www.indiatoday.in


https://www.blog.epravesh.com

https://www.blog.epravesh.com

https://49hk843qjpwu3gfmw73ngy1k-wpengine.netdna-ssl.com

https://media.nesta.org.uk

https://www.weforum.org

https://www.weforum.org

https://www.vaughn.edu


http://niti.gov.in

http://niti.gov.in

https://49hk843qjpwu3gfmw73ngy1k-wpengine.netdna-ssl.com



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 ............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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,


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://forbes.com


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://monster.com


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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:


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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:


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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,


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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

Acikgoz, Y. (2019). Employee recruitment and job search: Towards a multi-level integration. Human Resource Management Review, 29, 1–13.

Agrawal, A. (2018). The economics of artificial intelligence. McKinsey Quarterly, April 2018.Bailie, I. (April 9, 2019). What is the impact of AI on HR? myHRfuture. Available at: and

Automation https://www.myhrfuture.com/blog/2019/4/4/what-is-the-impact-of-ai-on-hrBiswas, S. (February 5, 2019). The beginner’s guide to AI in HR, HR Technologies. Available at:

https://www.hrtechnologist.com/articles/digital-transformation/the-beginners- guide-to-ai-in-hr/

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

use of technology. People Management [Online]. Available at: https://www. peoplemanagement.co.uk/news/articles/job-hunters-favour-face-to- facerecruitment-over-technology [Accessed: November 1, 2019].

Buck, B. & Morrow, J. (2018). AI, performance management and engagement: Keeping your best their best. Strategic HR Review, 17(5), 261–262. https://doi.org/10.1108/SHR-10-2018-145.

Buranyi, S. (2018). Dehumanising, impenetrable, frustrating: The grim reality of job hunt-ing in the age of AI. The Guardian [Online]. Available at: https://www.theguardian.com/inequality/2018/mar/04/dehumanising-impenetrable-frustratingthe-grim-reality-of-job-hunting-in-the-age-of-ai [Accessed: November 24, 2019].

Cardinal, L. B., Kreutzer, M., & Miller, C. C. (2017). An aspirational view of organizational control research: Re-invigorating empirical work to better meet the challenges of 21st century organizations. Academy of Management Annals, 11, 559–592.

Chui, M. & Francisco, S. (2017). Artificial intelligence the next digital frontier? McKinsey and Company Global Institute, Washington, DC, 1–80.

Davenport, T. & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, January-February 2018.

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

EY - Building a Better Working World report (2018). The new age: Artificial intelligence for human resource opportunities and functions. Available at: https://www.ey.com/Publication/vwLUAssets/EY-the-new-age-artificial-intelligence-for-human-resource-opportunities-and-functions/$FILE/EY-the-new-age-artificial-intelligence-for-human-resource-opportunities-and-functions.pdf

Galanaki, E., Lazazzara, A., & Parry, E. (2019). A cross-national analysis of e-HRM configurations: Integrating the information technology and HRM perspectives. Organizing for Digital Innovation, 27, 261–276.

i.e., the employees. AI helps in creating user-centric designs that help in understand-ing, designing, and innovating with the users, thus giving value to each individual.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.myhrfuture.com

https://www.hrtechnologist.com

https://www.hrtechnologist.com

https://www.peoplemanagement.co.uk



https://doi.org

https://doi.org

https://www.theguardian.com



https://yourstory.com

https://yourstory.com

https://www.ey.com

https://www.ey.com

https://www.ey.com

https://www.ey.com


Geetha, R. & Bhanu Sree, R. D. (July 2018). Recruitment through Artificial Intelligence: A conceptual study. International Journal of Mechanical Engineering and Technology (IJMET), 9(7), 63–70. Available at: http://www.iaeme.com/MasterAdmin/UploadFolder/IJMET_09_07_007/IJMET_09_07_007.pdf

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

IBM. (2018). IBM Watson talent frameworks. Available at: https://www.ibm.com/talent- management/hr-solutions/ibmwatson-talent-frameworks (October 20, 2018).

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbio-sis in organizational decision making. Business Horizons, 61(4), 1–10. doi:10.1016/j.bushor.2018.03.007

Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence applica-tion framework in human resource management. In ICEB 2018 Proceedings. https://aisel.aisnet.org/iceb2018/91

Kugasia, A. (April 10, 2019). Take a leap in payroll management using advanced AI capabilities. Azilen. Available at: https://www.azilen.com/blog/payroll-management-using-advanced-ai-capabilities/

McRobert, C. J., Hill, J. C., Smale, T., Hay, E. M., & Van der Windt, D. A. (2018). A multi-modal recruitment strategy using social media and internet-mediated methods to recruit a multidisciplinary, international sample of clinicians to an online research study. PLoS ONE, 13(7). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200184

Meister, J. (January 8, 2019). Ten HR trends in the age of artificial intelligence. Forbes. Available at: https://www.forbes.com/sites/jeannemeister/2019/01/08/ten-hr-trends-in-the-age-of-artificial-intelligence/#719176463219

Merlin, P. E. & Jayam, R. (2018). Artificial intelligence in human resource management. International Journal of Pure and Applied Mathematics, 119(14), 1891–1895. Available at http://www.acadpubl.eu/hub/

Nicastro, D. (March 12, 2018). 7 Ways Artificial Intelligence is reinventing human resources, CMS Wire. Available at: https://www.cmswire.com/digital-workplace/7-ways-artificial-intelligence-is-reinventing-human-resources/

Nowak, M. (July 10, 2019). How AI is transforming human resources. Monterail. Available at: https://www.monterail.com/blog/ai-transforming-hr

Pandey, A. (May 18, 2017). Role of Artificial Intelligence in HR. PC Quest. Available at: https://www.pcquest.com/role-artificial-intelligence-hr/

Pribanic, E. (March 17, 2018). Future of AI in corporate training and develop-ment. Tech Funnel. Available at: https://www.techfunnel.com/hr-tech/future-of-ai-in-corporate-training-and-development/

Pathak, S. & Agrawal, R. (2019). Design of knowledge based analytical model for organiza-tional excellence. International Journal of Knowledge-Based Organizations (IJKBO), 9(1), 12–25.

Rao, M. S. (2017). Innovative tools and techniques to ensure effective employee engagement. Industrial and Commercial Training, 49(3), 127–131.

Reilly, P. (2018). The impact of artificial intelligence on the HR function. IES Perspectives on HR 2018. Available at: https://www.employment-studies.co.uk/system/files/resources/files/mp142_The_impact_of_Artificial_Intelligence_on_the_HR_function-Peter_Reilly.pdf

Roby, K. (August 15, 2018). AI in corporate learning and development: It’s here. Training Industry. Available at: https://trainingindustry.com/articles/learning-technologies/ai-in-corporate-learning-and-development-its-here/


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://www.iaeme.com

http://www.iaeme.com

http://HR.COM

https://www.oracle.com


https://www.ibm.com

https://www.ibm.com

https://aisel.aisnet.org

https://aisel.aisnet.org

https://www.azilen.com

https://www.azilen.com

https://journals.plos.org



http://www.acadpubl.eu

https://www.cmswire.com

https://www.cmswire.com

https://www.monterail.com

https://www.pcquest.com

https://www.techfunnel.com

https://www.techfunnel.com

https://www.employment-studies.co.uk



https://trainingindustry.com

https://trainingindustry.com



Sen, S. (2017). AI in HR: Impact, adoption and future workforce. AIHR Digital. Available at: https://www.digitalhrtech.com/ai-in-hr-impact-adoption-automation/

Tata. (2018). Cognitive Diversity: AI and the Future of Work, Tata Communications.Tiliakos, P. A. (January 2019). Infosys next generation payroll services. Available at: https://

research.nelson-hall.comUpadhyay, A. & Khandelwal, K. (2018). Applying artificial intelligence: Implications for

recruitment. Strategic HR Review, 17(5), 255–258Verma, R. & Bandi, S. (January 6, 2019). Artificial intelligence & human resource man-

agement in Indian IT sector. In Proceedings of 10th International Conference on Digital Strategies for Organizational Success. Available at SSRN: https://ssrn.com/abstract=3319897 or http://dx.doi.org/10.2139/ssrn.3319897

Yawalkar, V. (2019). A study of artificial intelligence and its role in human resource manage-ment. International Journal of Research and Analytical Reviews (IJRAR), 6, 20–24.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.digitalhrtech.com

https://research.nelson-hall.com

https://research.nelson-hall.com

https://ssrn.com

https://ssrn.com

http://dx.doi.org


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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;


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


FIGURE 4.2 AI and its usage in HR.

FIGURE 4.3 Skills required in AI for success.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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/


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://www.ijims.com

http://watson.analytics.ibmcloud.com

http://www.acadpubl.eu

https://www.raconteur.net



https://www.digitalhrtech.com


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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 ................................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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/


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.dhl.com

https://www.dhl.com

https://pdfs.semanticscholar.org

https://pdfs.semanticscholar.org

https://www.digitalpulse.pwc.com.au


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.mhlnews.com

https://www.mhlnews.com

http://www.businessinsider.com.au



https://www.starship.xyz

https://www.starship.xyz

http://www.scdigest.com

http://www.businessinsider.com.au

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 ................................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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,


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


FIGURE 6.5 Inner structures.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.

.Dickson, P.R. (1994), Marketing Management, The Dryden Press, Orlando, FL.Durkin, J. (1994), Expert Systems: Design and Development, Macmillan, New York.Eisenhart, T. (1990), “Computer-aided marketing: After ten years of marketing decision

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.toyota-global.com

https://www.toyota-global.com

89

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 ..............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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;


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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).


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://Volley.com


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).


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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).


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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/


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://discovery.ucl.ac.uk

https://elearningindustry.com

https://en.unesco.org

https://gradecam.com

http://ijaeit.com


https://www.marketsandmarkets.com

https://www.sciencedirect.com

https://telrp.springeropen.com

https://www.virtusa.com

https://www.virtusa.com


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


105

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 ................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.................................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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:


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


• 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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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;


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


• 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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


• 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;


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


• 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/


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.finextra.com

https://www.finextra.com

https://www.livemint.com

https://www.wipro.com

https://www.opentext.com

https://www.paymentsjournal.com

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 ..............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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,


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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).


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://Newgenapps.com

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://data-flair.training

https://data-flair.training

http://Allerin.com

https://www.allerin.com

https://www.allerin.com

https://towardsdatascience.com



https://emerj.com

https://emerj.com

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].


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

https://www.whichplm.com

https://www.whichplm.com

http://Newgenapps.com

https://www.newgenapps.com

http://Forbes.com



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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.


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/.)


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

http://www.gelifesciences.com

145A Road map to the Future of Industry 4.0

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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://blog.atheerair.com

https://blog.atheerair.com


• 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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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/.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://www.pwc.com

https://www.pwc.com

https://www.pwc.com


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://www.pwc.com

https://www.pwc.com

https://www.pwc.com

https://www.pwc.com


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://www.pwc.com

http://40-survey


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://www.pwc.com

https://www.pwc.com


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://www.pwc.com

https://www.pwc.com


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

https://www.etmm-online.com

https://www.etmm-online.com

https://www.myforesight.my

https://www.pwc.com

157

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..............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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).


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.

REFERENCES

Abad, P.L. and Jaggi, C.K. (2003). A joint approach for setting unit price and the length of the credit period for a seller when end demand is price sensitive, International Journal of Production Economics, 83, 115–122.

Bass, F.M. (1969). A new product growth model for consumer durables. Management Science, 15, 215–27.

Bass, F.M. (1980). The Relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations, Journal of Business, 53, 51–67.

Bass, F.M., Jain, D.C., and Krishnan, T. (2000). Modelling the Marketing-Mix Influence in New-Product Diffusion. New-Product Diffusion Models, Springer, New York.

Bass, F.M., Krishnan, T.V., and Jain D.C. (1994). Why the bass model fits without decision variables. Marketing Science, 13(3), 203–223.

Bayus, B.L. (1992). Have diffusion rates been accelerating over time? Marketing Letters, 3(3), 215–226.

Bayus, B.L. (1994). Are product life cycles really getting shorter? Journal of Product Innovation Management, 11, 300–308.

Ben-Daya, M. and Raouf, A. (1993). On the constrained multi-item single period inventory problem, International Journal of Production Management, 13, 104–112.

Ben-Khedher, N. and Yano, C.A. (1994). The multi-item joint replenishment problem with transportation and container effects. Transportation Science, 28, 37–54.

Benhabib, J., Perla, J., and Tonetti, J. (2019). Reconciling Models of Diffusion and Innovation: A Theory of the Productivity Distribution and Technology Frontier, NBER Working Paper No. 23095.

Bhattacharya, D.K. (2005). On multi-item inventory. European Journal of Operational Research, 162, 786–791.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


Blackman, W.A. (1975). The market dynamics of technological substitutions. Technological Forecasting and Social Change, 6, 41–63.

Bretschneider, S. and Mahajan, V. (1980). Adaptive technological substitution models. Technological Forecasting and Social Change, 18, 129–139.

Bridges, E.C.K., Yim, R. and Briesch, A. (1995). A high-tech product market share model with customer expectations. Marketing Science, 14(1), 61–81.

Brown, S.D. and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002.

Chen, J.M. & Chen, T.H. (2005a). The multi-item replenishment problem in a two- echelon supply chain: the ef fect of centralization versus decentralization. Computers & Operations Research, 32, 3191–3207.

Chen, T.H. & Chen, J.M. (2005b). Optimizing supply chain collaboration based on joint replenishment and channel coordination. Transportation Research Part E: Logistics and Transportation Review, 41, 261–285.

Danaher, P.J., Hardie, B.G.S., and Putsis, W.P. (2001). Marketing-mix variables and the diffusion of successive generations of a technological innovation. Journal of Marketing Research, 38(4), 501–514.

Dekimpe, M.G., Parker, P.M., and Sarvary, M. (1998). Globalization: Modelling technology adoption timing across countries, Technological Forecasting and Social Change, 63, 25–42.

Dockner, E. and Jorgensen, S. (1988). Optimal advertising policies for diffusion models of new product innovations in monopolistic situations, Management Science, 34, 119–130.

Dolan, R. and Jeuland, A. (1981). Experience curve and dynamic demand models. Journal of Marketing, 45, 52–62.

Dutilh, G., Forstmann, B.U., Vandekerckhove, J., and Wagenmakers, E.J. (2013). A diffusion model account of age differences in posterior slowing. Psychology and Aging, 28, 64. doi:10.1037/a0029875.

Easingwood, C. (1987). Early product life cycle forms for infrequently purchased major prod-ucts. International Journal of Research in Marketing, 4, 3–9.

Easingwood, C. (1988). Product life cycle patterns for new industrial products. R&D Management, 18(1), 23–32. 1989. An analogical approach to the long term forecasting of major new product sales. International Journal of Forecasting, 5, 69–82.

Easingwood, C., Mahajan, V., and Muller, E. (1981). Nonsymmetric responding logistic model for forecasting technological substitution. Technological Forecasting and Social Change, 20, 199–213.

Easingwood, C., Mahajan, V., and Muller, E. (1983). A non-uniform influence innovation diffusion model of new product acceptance. Marketing Science, 2, 273–295.

Fisher, J.C. and Pry, R. (1971). A Simple substitution model for technological change. Technology Forecasting and Social Change, 3, 75–78.

Germar, M., Schlemmer, A., Krug, K., Voss, A., and Mojzisch, A. (2014). Social influence and perceptual decision making: a diffusion model analysis. Personality and Social Psychology Bulletin, 40, 217–231. doi:10.1177/0146167213508985.

Goyal, S.K. (1973). Determination of economic packaging frequency for items jointly replenished. Management Science, 20, 232–235.

Grasman, R.P.P.P., Wagenmakers, E.J., and van der Maas, H.L.J. (2009). On the mean and variance of response times under the diffusion model with an application to parameter estimation. Journal of Mathematical Psychology, 53, 55–68. doi:10.1016/j.jmp.2009.01.006

Graves, S.C. (1979). On the deterministic demand multi-product single-machine lot schedul-ing problem. Management Science, 25, 276–280.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


Hadley, G. and Whitin, T.M. (1963). Analysis of Inventory Systems. Prentice-Hall, New York.Hahm, J. and Yano, C.A. (1995). The economic lot and delivery scheduling problem: the

common cycle case. IIE Transactions, 27, 113–125.Hahn, M., Park, S., Krishnamoorthy, L., and Zoltners, A. (1994). Analysis of new

product dif fusion using a four segment trial repeat model. Marketing Science, 13(3), 224–247.

Hambrick, M.E. (2019). Investigating GoPro and Its Diffusion of Innovations. SAGE Publications: SAGE Business Cases Originals, London.

Hammer, M. (2001). The superefficient company. Harvard Business Review, 79, 82–91.Hausman, W.H., Lee, H.L., and Zhang, A.X. (1998). Joint demand fulfillment probability in a

multi-item inventory system with independent order-up-to policies. European Journal of Operational Research, 109, 646–659.

Horsky, D. (1990). The effects of income, price and information on the diffusion of new consumer durables, Marketing Science, 9(4), 342–65.

Horsky, D. and Simon, L.S. (1983). Advertising and the diffusion of new products. Marketing Science, 2, 1–18.

Jain, D. and Rao, R.C. (1990), Effect of price on the demand for durables: Modelling, estimation, and findings. Journal of Business Economics and Statistics, 8(2), 163–170.

Jeuland, A.P. and Dolan, R.J. (1982). An aspect of new product planning: Dynamic pricing. TIMS Studies in Management Science, 18, 1–21.

Jiang, Z. and Jain, D.C. (2011). A generalized Norton Bass Diffusion Model for multi- generational diffusion. Management Science, 58(10), 1887–1897

Johnson, L.A. and Montgomery, D.C. (1974). Operations Research in Production Planning, Scheduling, and Inventory Control, John Wiley & Sons, New York.

Kalish, S. (1985). A new product adoption model with price, advertising and uncertainty. Management Science, 31, 1569–1585.

Kamakura, W. and Balasubramanian, S. (1987). Long-term forecasting with innovation diffusion models: The impact of replacement purchases. Journal of Forecasting, 6, 1–19.

Kamakura, W. and Balasubramanian, S. (1988). Long-term view of the diffusion of durables: A study of the role of price and adoption influence process via tests of nested models, International Journal of Research in Marketing, 5, 1–13.

Kao, E.P.C. (1979). A multi-product dynamic lot-size model with individual and joint set-up costs. Operations Research, 27, 279–289.

Krishnan, T.V., Bass, F.M., and Jain, D.C. (1999), Optimal pricing strategy for new products. Management Science, 45, 1650–1663.

Kumar, S. (2019). Social capital and diffusion of innovations. Agriculture Innovation Systems in Asia towards Inclusive Rural Development, Taylor and Francis, Abington, pp. 253–275.

Kuo, C.W. and Huang, K.L. (2012). Dynamic pricing of limited inventories for multi- generation products. European Journal of Operational Research, 217, 394–403.

Lee, C.Y. and Lee, D. (2014). An efficient method for solving a correlated multi-item inventory system. Operations Research Perspectives, 5. doi:10.1016/j.orp.2017.11.002.

Lerche, V. and Voss, A. (2016). Model complexity in diffusion modelling: Benefits of making the model more parsimonious. Frontiers in Psychology.

Lilien, G.L., Rao, M.G., and Kalish, S. (1981), Estimation and control of detailing effort in a repeat purchase diffusion environment, Management Science, 27(5), 493–506.

Lilien, G. L. and Yoon, E. (1990). The timing of competitive market entry: An exploratory study of new industrial products. Management Science, 36, 568–585.

Mahajan, V., Mason, C. H., and Srinivasan, V. (1986). An evaluation of estimation procedures for new product diffusion models. Mahajan, V. and Wind, Y. (eds.) Innovation Diffusion Models of New Product Acceptance, Ballinger, Cambridge, pp. 203.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.

Schmittlein, D. C. and Mahajan, V. (1982). Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Science, 1(1), 57–78.

Schrady, R.G. and Choe, U.C. (1971). Models for multi-item continuous review inventory policies subject to constraints. Naval Research Logistics Quarterly, 18(4), 451–464.

Speece, M. and Maclachlan, D. (1995). Application of a multi-generation diffusion model to milk container technology. Technological Forecasting and Social Change, 49, 281–295.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


Srinivasan, V. and Mason, C.H. (1986). Nonlinear least squares estimation of new product diffusion models. Marketing Science, 5(2), 169–78.

Srivastava, R.K., Kim, N., and Han, J.K. (2001). Consumer decision making in a multi- generational choice set context. Journal of Business Research, 53(3), 123–136.

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.

Uthayakumar, R. and Priyan, S. (2013). Pharmaceutical supply chain and inventory management strate gies: Optimization for a pharmaceutical company and a hospital, Operations Research for Health Care, 2, (3), 52–64.

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.

Vejlgaard, H. (2018). Culture as a determinant in innovation diffusion. The Future of Television - Convergence of Content and Technology. doi:10.5772/intechopen.80806.

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.

Wilson, L.O. and Norton, J.A., (1989). Optimal entry time for a product line extension. Marketing Science, 8, 1–17.

Winer, R.S. (1985). A price vector model of demand for consumer durables: Preliminary developments. Marketing Science, 4(1), 74–90.

Young, H.P. (2009). Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review, 99(5), 1899–1924.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://www.diva-portal.org

http://www.diva-portal.org

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..............................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


• 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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


• 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?


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://www.alanturing.net



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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


• 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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


• 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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.)


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


• 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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://AlanTuring.net



https://library.educause.edu

https://backend.educ.ar

https://library.educause.edu


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.



https://www.thetechedvocate.org

https://www.theedadvocate.org

https://bernardmarr.com

https://becominghuman.ai

https://becominghuman.ai

https://er.educause.edu



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 ......................................................


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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..........................................................................................................


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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

WhatsApp

Facebook

Twitter

Instagram

Internet


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Disagree 10 16

Neutral 12 20

Agree 21 35


Total 60 100

Frequency Percent


Disagree 11 18

Neutral 10 17

Agree 12 20


Total 60 100


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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


Disagree 10 17

Neutral 10 17

Agree 22 36


Total 60 100


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://www.afdb.org

http://www.afdb.org

https://www.un.org

https://www.un.org

https://www.un.org

https://www.un.org


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.


Cop

yrig

ht ©

202

0. T

aylo

r &

Fra

ncis

Gro

up. A

ll rig

hts

rese

rved

.

http://pubdocs.worldbank.org




http://stg1.worldvision.org

http://stg1.worldvision.org

https://www.wvi.org

https://www.wvi.org

199

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

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


Cop

yrig

ht ©

202

0. T

aylo

r & F

ranc

is G

roup

. All

right

s re

serv

ed.

Transforming Management Using Artificial Intelligence ...

Documents

Transcript of Transforming Management Using Artificial Intelligence ...