Master’s Thesis 15 credits

Specialization: Management Control

Department of Business Studies

Uppsala University

Spring Semester of 2021

Date of Submission: 2021-06-02

Implementation of Robotic

Process Automation - A case study of issues, challenges and

success factors for RPA

implementation in banking and

financial services

Haris Camo

Niklas Grufman

Simon Harnesk

Supervisor: Jan Lindvall

Acknowledgements

We would like to thank all respondents from the case-, and consulting company for your

valuable time and engagement in the study. We would also like to use the opportunity to thank

our supervisor Jan Lindvall for your valuable input and support throughout the process.

Haris Camo, Simon Harnesk & Niklas Grufman

Stockholm, 2nd of June 2021

Abstract

Robotic Process Automation (RPA) is an emerging lightweight IT solution that aims to

automate digital but manual business processes and has been particularly useful in the banking

and financial services industry. Whilst interest for RPA has been increasing along with the

increased pressure on organizations to leverage technology for efficiency to remain

competitive, studies show upwards of 50 percent of RPA implementations fail. Building on

problematizations of RPA implementation failures and scholarly urges for further research

within the field, this study aimed to investigate; issues and challenges to overcome, as well as

how RPA implementation may be facilitated. Based on a literature review including existing

implementation frameworks, a case study of semi-structured interviews with managers and

expert consultants was conducted, and project material was reviewed.

Several differences and similarities between existing theory and the respondents' experiences

were identified. Almost all issues and challenges emphasized by respondents were related to

the challenge of achieving adequate; change management, communication, education around

RPA, choosing the right processes to automate, and post-implementation and scale up. Several

of the common issues and challenges mentioned in theory, such as technical aspects, were not

experienced by the respondents and these mitigated issues and challenges may be dependent

on contextual preconditions. The most important facilitating actions were identified as; building

RPA knowledge, educating management and employees within the organization,

communicating new processes, RPA benefits and reasons for implementation, ensuring support

processes and establishing internal groups for RPA scale-up and stewardship, and carefully

choosing the right process to automate. Contextual preconditions likely facilitating the RPA

implementation for the case company were identified and interpreted as potential success

factors for RPA implementation. A structured approach with visualized project steps was

emphasized as important, and whilst the existing implementation framework compared to the

case was accurate and useful, the study suggest implementation frameworks may need to

include further attention to; change management, communication and education, and the

relative importance of the initialization- and post-implementation phases. The research is

concluded with a discussion on limitations and suggestions for research opportunities.

Keywords:

Robotic Process Automation, RPA Implementation, Implementation Framework, RPA in

Banking and Financial Services, Loan Administration.

Table of contents

1. Introduction 1

1.1 Problem background 1

1.2 Research gap 2

1.3 Research purpose 3

1.4 Research question 4

1.5 Study delimitations 4

2. Theoretical framework 5

2.1 IT and Business processes 5

2.2 Robotic process automation 5

2.3 RPA in Banking and Financial Services Industry 6

2.4 Benefits RPA 6

2.5 Issues and disadvantages with RPA 7

2.5.1 Limited use-cases and suitable processes 8

2.5.2 Issues with implementing RPA 8

2.6 Guidelines and models for implementation of RPA 9

2.6.1 Frameworks for RPA implementation 9

2.6.2 Implementation theory for technology fields outside RPA 14

2.7 Conclusions and analytical framework 15

2.8 Analytical model 16

2.8.1 Simplified analytical model 17

3. Methodology 18

3.1 Research approach 18

3.2 Research design 19

3.2.1 Qualitative case study design 19

3.2.2 Case company and participants 19

3.3 Empirical data collection process 21

3.4 Data analysis 22

3.5 Reliability, validity and replicability 23

3.6 Method discussion 24

4. Empirical findings 25

4.1 Structure 25

4.2 Fundamental aspects about the case and reasoning behind the RPA implementation

project 25

4.3 Phases and activities of the project 26

4.3.1 Initialization 26

4.3.2. Implementation 27

4.3.3 Post-implementation 28

4.3.4 Support-processes 29

4.3.5 Model and summary of the implementation project phases 30

4.4 Issues and challenges 30

4.4.1 Human and organizational issues 31

4.4.2 Issues with choosing the right process to automate 32

4.4.3 Issues related to changes in systems 33

4.4.4 Issues for broader adoption and scale-up 33

4.5 Specific aspects the respondents would have done differently 34

4.6 Success factors and facilitating actions 35

4.6.1 Preconditions and contextual factors 35

4.6.2 Aspects to facilitate implementation 36

5. Analysis 39

5.1 Intentions and goals 39

5.2 Issues and challenges 40

5.3 Implementation approach and success factors 42

5.3.1 Success factors and aspects facilitating implementation 42

5.3.2 RPA implementation project approach 44

6. Conclusions 48

6.1 Issues and challenges for RPA implementation projects 48

6.2 Managing an RPA implementation project 49

6.3. Limitations and suggestions for future research 50

References 52

Appendices 57

List of figures

Figure 1. A typical RPA Process ................................................................................................ 6

Figure 2. Consolidated framework for RPA implementation projects ..................................... 11

Figure 3. Analytical model ....................................................................................................... 16

Figure 4. Simplified analytical model ...................................................................................... 17

Figure 5. Summary of the implementation project as visualized by the case company........... 30

List of tables

Table 1. Sample overview ........................................................................................................ 20

Table 2. Interview themes ........................................................................................................ 22

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1. Introduction

This chapter starts by providing a descriptive background of Robotic Process Automation, and

associated challenges with the field. Further, we will cover the current streams of academic

research within the field in order to confirm a research gap and ensure the originality of the

thesis. Moreover, we will present our motivations for the research purpose, reflecting why the

study is practically needed and called upon in the given context. Thereafter, the research

questions are stated and followed by the discussion on expected theoretical, managerial and

societal contributions.

1.1 Problem background

The rise of emerging technologies and innovations has transformed our economic and business

world, presenting businesses with unprecedented opportunities to incorporate, exploit, and

leverage these modern digital solutions and innovations (Nwankpa and Merhout, 2020; Lin,

2018). As a consequence, businesses are under increased pressure to prioritize digital

investment and to turn it into creative business practices, new technologies, and business

models (Chae, 2019). Digital investment, which is defined as a firm's strategic technology

investment aimed at determining how ingenious digital technologies can potentially

differentiate its business, transactions, and processes (Nwankpa and Datta, 2017), is viewed as

a critical imperative for firms seeking to remain competitive and maintain market positions

(Syed et al., 2020; Yoo et al., 2012). Since many business processes are already performed by

computers, digitalization opens up a plethora of opportunities for optimization (Fischer et al.,

2020), such as the analysis and improvement of business processes.

Robotic Process Automation (RPA) is a recent emerging technology that aims to automate

digital but manually performed business processes (Lacity et al., 2016; van der Aalst et al.,

2018a). Unlike other process automation technologies, RPA ‘sits on top of’ the other IT systems

accessing these platforms through the presentation-layer, thus no underlying systems

programming logic is touched (Aguirre and Rodriguez, 2017; Lacity & Willcocks, 2015a). This

solution has been particularly useful in the banking and financial services industry (Madakam

et al., 2019), since the business processes are often managed through a combination of legacy

systems, new technologies, and manual processes (Vishnu, et al., 2017).

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During recent years, interest for RPA in research and in terms of search-numbers in search

engines has been growing rapidly, indicating an increasing importance of RPA (Santos et al.,

2019). According to Gartner (2020), a leading IT research and advisory company, the RPA

market is expected to grow at double-digit rates annually through 2024. A successful RPA-

implementation can have major benefits for organizations, such as increased productivity

(Alberth and Mattern, 2017; Madakam et al., 2019; Vishnu et al., 2017; Syed, et al., 2020), full-

time employee (FTE)- and cost reductions (Lacity and Willcocks, 2015b; Santos et al., 2019)

and increased reliability and continuity of service (Santos et al., 2019). Moreover, RPA can

often be introduced more quickly and cheaper than IT solutions relying on APIs for system

integration, taking two to four weeks rather than months or years (Asatiani and Penttinen,

2016). Since this light-weight automation approach hence allows for return on investment

(ROI) to be achieved in a shorter time, RPA is often a strategically valuable alternative (van

der Aalst et al., 2018a).

Although the benefits from the implementation of RPA are well documented, it cannot be taken

for granted that an implementation of RPA will result in benefits for organizations (Syed et al.,

2020). In a study performed by EY, 30 to 50 percent of initial RPA projects fail (Lamberton,

2016), and only about 20 percent of organizations that implemented RPA in 2019 achieved a

business value that exceeded what they had expected (Willcocks et al., 2019). The field of RPA

has been described as an unexplored field, and academic research in implementation and

utilization has only recently begun to rise (Lamberton, 2016). Research on so-called RPA

"methodologies" take the form of lessons learned, recommendations, best practices, and

experience-reports from RPA implementations within organizations (Syed et al., 2020) and the

current access to established guidelines or best practices for maximizing the benefits of RPA

implementations (from adoption to delivery) are scarce (ibid.). As a result, finding a systematic

approach to optimizing the benefits from an RPA implementation becomes an open issue to

address (ibid.).

1.2 Research gap

Provided the many potential benefits of RPA described in literature, and the inherent difficulties

with implementation - the difficulties and eventual solutions is evidently relevant to investigate.

In terms of previous scholarly contributions to RPA, the field can be described as recent and

unexplored (Santos et al., 2019). This is also supported by several scholars including

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Lamberton (2016) who suggests RPA remains an unexplored field and academic research in

implementation and utilization has only recently begun to rise, and Herm et al. (2020)

suggesting that “[...] from a research perspective RPA is poorly understood and only in the

early stage of scientific research. Hence, several areas have not yet been sufficiently

investigated and pose challenges [...]”. With regards to specific research on implementation

issues, solutions, and suggested approaches, scholars have also urged for extended research on

certain fields within the generally unexplored field of RPA. According to Syed et al. (2020),

guidelines and best practices for benefit realization from deploying RPA rarely exist and hence,

development of approaches to support benefit realization is an open issue to address.

A few scholars have presented general frameworks for implementation considerations, and

Gotthardt et al.´s (2020) suggest additional research is needed on implementation. In relation

to Santos et al.´s (2019) conceptual model for RPA implementation (presented in the theoretical

framework of this thesis) the authors suggest “future research should focus on applying the

proposed model on conducting a CS [case study], by using the steps identified and considering

RPA main topics and then refine the model based on the experience of conducting a CS, if

needed”. As will be seen, the conceptual model suggested to be adapted to a case has great

similarities with other models suggested by literature - pointing to the fact that adaptation and

evaluation of specific implementation models could be beneficial for the RPA implementation

field in general.

Conclusively, in addition to the obvious relevance of RPA knowledge described in the problem

background, the logic of this research will build on fundamental pillars of research gaps related

to; the field of RPA in general, best practices and RPA implementation approaches, as well as

application of previous RPA implementation frameworks on new cases.

1.3 Research purpose

Based on the practical relevance described in the problem background and the identified need

for further research within the field of RPA implementation, the aim of this study is to explore

common issues and challenges associated with RPA implementation. Furthermore, based on

existing frameworks and theory, the study aims to explore how projects may be managed in

order to facilitate implementation.

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1.4 Research question

In order to address the research gap and contribute to the overall purpose of this research, the

following research questions were developed;

1) What are the issues and challenges for RPA implementation projects?

2) How can RPA projects be managed to facilitate implementation?

The study aims to investigate these questions in the context of the banking and financial

services industry, as will be described in the study delimitations.

1.5 Study delimitations

As the purpose of this study is to perform an overall mapping of problems, facilitating aspects

and suggested project approaches for RPA implementation, this study will focus on RPA

literature specifically and do not intend to make cross-disciplinary comparisons between other

fields of research due to the limited time and scope. Accordingly, this study will not include

BPM (Business Process Management) which is a common subject related to RPA in previous

literature. Provided the similarities and inherent connections between IT-implementations,

however, some references are made to ERP (Enterprise Resource Planning) implementation.

In this study, the term ‘implementation’ (of RPA) refers to the activities of automating new

processes and includes activities and long-term considerations of both initializing - before the

actual ‘implementation’ - and expanding the number of activities automated - after the

‘implementation’ of an automation. The same scope of ‘implementation’ is implicit in RPA

literature. The term ‘successful’ implementation is seemingly established in literature and

however remains undefined by scholars. Whilst this thesis will try to incorporate goals for the

case company´s implementation to put the conclusions in a context, implementation ‘success’

will be broadly defined as the foundational automation of a targeted process.

The fact that the case study only examines one company in the banking and financial services

industry, and with has certain pre-condition (to be described), may have a negative impact on

the generalizability and hence usefulness of this study. Moreover, provided the focus on

describing the entire process of an implementation and hence using a limited number of

centrally involved respondents along with triangulation through reviewing project

documentation - the study does not include e.g. end-users or other case company stakeholders

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to describe challenges from different perspectives than the project team(s) and their focus on

project completion.

2. Theoretical framework

In this chapter, a theoretical foundation of previous research within the field of RPA and RPA

implementation will be presented. Based on the key topics identified, the theoretical framework

is concluded with an analytical model to be used for answering the research questions.

2.1 IT and Business processes

A fundamental part of business and management is that organizations seek to increase their

operational performance by redefining and managing their business processes in order to stay

competitive. In accordance with the definition provided by Pettigrew (1997, cited in Bryman,

2012) this research will define a process as; “a sequence of individual and collective events,

actions, and activities unfolding over time in context’. In order to achieve the objective of

staying competitive, information technology (IT) plays a major role (Syed et al., 2020). This

requires new work organizations that encourages agility and teamwork and concentrates more

on cognitive tasks that generates “know-how” and value. However, substantial parts of

organizations resources are often occupied by routines and repetitive tasks (van der Aalst et al.,

2018b). Despite the extensive use of process-aware information systems, most contemporary

businesses still rely on employees to initiate or stop procedures, to adapt them to different

situations, or to use their results for various purposes (van der Aalst et al., 2018b). Robot

process automation (RPA), an emerging trend in the area of business process automation, had

been proclaimed a possible solution to tackle this.

2.2 Robotic process automation

RPA is defined as a technology tool that uses software and algorithmic programmed robots

acting as human beings in system interaction to support efficient business processes (Lu et al.,

2017; Santos et al., 2019; Syed, 2020; van der Aalst et al., 2018). RPA is designed to relieve

employees of the burden of performing repetitive, simple tasks (Aguirre and Rodriguez, 2017).

It does not replace other systems. Instead, it is sitting on top of the other systems and performs

tasks which it has been programmed to complete (Lacity & Willcocks, 2015a). Hence, most

times, integration of RPA does not require any change in the existing systems (Lacity &

Willcocks, 2015b). In the same way as a human would interact with a software system by

clicking and typing, the RPA does it but with greater execution (Lu et al., 2017). However, it

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would be no problem for a human to be reinstated and execute the task instead if needed

(Asatiani and Penttinen, 2016). RPA accesses systems in the same way a human does – with

its own username and password (Lacity & Willcocks, 2015a). One example where an RPA

would be suitable is when an employer logs in to one system, gathering data and processing

them using rules, then uploading this data into another system (Lacity & Willcocks, 2015a).

This process is illustrated in Figure 1.

Figure 1. A typical RPA Process (Adapted from Lacity & Willcocks, 2015a).

2.3 RPA in Banking and Financial Services Industry

With the widespread adoption of virtual banking, banks must invent new ways to provide the

best possible customer experience while reducing costs, maintaining security, and meeting

regulatory and compliance requirements (Vishnu, et al., 2017). To optimize operations and

improve efficiency, financial institutions have to focus on improving the speed and accuracy

of the core business processes – this objective can be achieved using RPA (ibid). The industry

is one of the most suitable for the technology (Madakam et al., 2019), as the organization's core

business processes generate a high volume of documents and are managed through a

combination of legacy systems, new technology, and manual processes (Vishnu, et al., 2017).

2.4 Benefits RPA

Several benefits with RPA is prominent in literature. One of the main benefits with RPA is that

the robots can operate around the clock, which provides reliability and continuity of services

(Syed, 2020), allowing for FTE reductions (Lacity and Willcocks, 2015; Santos et al., 2019),

and reduction of entry costs by upwards of 70 percent (Anagnoste, 2017). In fact, based on

FTEs replaced by robots, RPA technology has been shown to reduce the cost of human

resource-related spending by 20–50 percent and transaction processing costs by 30–60 percent

(Syed, 2020). By implementing robots for routine tasks, workers may concentrate on more

interesting value-adding activities that involve personal interaction, problem-solving and

decision making (Syed, 2019), also contributing to increased job satisfaction and employee

retention (Santos et al., 2019).

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In comparison to humans, robots also make less mistakes and perform with higher efficiency,

resulting in increased productivity (Alberth and Mattern, 2017; Madakam et al., 2019; Vishnu

et al., 2017; Syed, 2020). RPA is also useful from a risk and compliance perspective as it can

maintain a log of work completed to ensure that the automated tasks and processes comply with

regulatory requirements (Syed, 2019). Furthermore, RPAs may be deployed more quickly than

other IT solutions that rely on APIs to integrate with systems, often taking two to four weeks

rather than months or years to introduce (Asatiani and Penttinen, 2016). As a result, RPA often

enable a fast ROI (Lacity and Willcocks, 2017; Santos et al., 2019) attractive for organizations.

2.5 Issues and disadvantages with RPA

In addition to scholarly attention to the many opportunities and potential benefits of RPA

provided in the previous section, several authors have highlighted limitations and potential

challenges with RPA. One of the main challenges with the use of robotics and RPA is

maintenance whereby complexity and costs of adapting the RPA solution to changing IT

infrastructure or organizational needs may be underestimated (Stolpe el al., 2017). When

systems change, robots often have to be updated which is time consuming and costly (Santos

et al., 2019). Furthermore, whilst RPA is an IT tool, the processes automated often belong on

the business side, whereby division of responsibilities between the IT and business side is

sometimes unclear during implementation and maintenance (Santos et al., 2019).

Lacking understanding of the term RPA and what it means, has also been described as an issue

whereby the technology may be interpreted as related to robotics, rather than software robots

(Santos et al., 2019). Furthermore, whilst providing efficient processing of tasks through

prompt actions, there is no human checking for errors before task execution meaning the robots

can make mistakes faster, not waiting for responses from applications (Santos et al., 2019).

Literature has also described that whilst some employees allocate working hours into new tasks

when RPA decreases the need for human processing, others simply replace their workers with

robots (Syed et al., 2020; Santos et al., 2019). Issues hence also appear with regards to impact

on employees who may see robots as opponents for job opportunities, which may have a

negative impact on the workplace (Asatiani & Penttinen, 2016). The buzz around RPA and its

potential impact for efficiency and reduction of workforce needed has provoked some public

opinion against automation software (Santos et al., 2019; Asatiani & Penttinen, 2016), as well

8

as created vast expectations and promises of impact which may be difficult to achieve

(Rutaganda et al., 2017).

2.5.1 Limited use-cases and suitable processes

The limitations in terms of suitable use-cases for RPA and that such limitations need to be

considered, is recurring in literature. Several criteria for when RPA is especially useful is

evident (Santos et al., 2019) including that the process to be automated preferably should;

include high volumes of transactions or information (Asatiani & Penttinen, 2016; Fung, 2014;

Lacity & Willcocks, 2015), be standardized and mature in terms of knowledge and experience

around it (Willcocks et al., 2017) and frequently interact with multiple systems (Asatiani &

Penttinen, 2016; Fung, 2014; Lacity & Willcocks, 2015). Santos et al. (2019) argue it is

important for companies to know whether their processes are suitable for automation, and

companies hence need to consider these disadvantages and limitations in terms of suitable

processes when adopting RPA to automate processes.

2.5.2 Issues with implementing RPA

Whilst the benefits that may be gained from implementing RPA are well documented, benefits

realization from RPA deployment cannot be taken for granted (Syed et al., 2020). The literature

is clear with regards to that RPA implementation is not always smooth sailing and that many

potential implementation issues may arise (Gex & Minor, 2019; Rutaganda, 2017; Santos et

al., 2019; Herm, 2020; Gotthardt at al., 2020). Accordingly, implementation challenges

represent important aspects of RPA which organizations need to consider (Santos et al., 2019).

Despite the existence of many RPA products, vendors and consultants in the market,

uncertainties about how to successfully implement and utilize the technology is common (Syed

et al., 2020). Although RPA is considered an easy to implement technology, in debt knowledge

within the field is necessary for successful implementation success (Herm et al., 2020).

Whilst the use of RPA has been steadily increasing, RPA solutions is to be considered as a

relatively new subject and the academic research within the field of RPA implementation and

utilization has only recently begun to rise, whereby systematic approaches for benefit

realizations of RPA implementation is still an open issue to address (ibid.).

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In the ´Digital Directions´-report, produced with Microsoft, EY (2020) point to indications on

that upwards of 40 percent of RPA projects are considered as failed, and that EY´s consultants

experience common mistakes include; automating too much of a process, targeting the wrong

processes, disregarding IT infrastructure, underestimating skills needed, and underestimating

the need for continuous efforts post implementation. From a broader perspective, Willcocks et

al. (2018), however conclude that implementation issues are often a consequence of mistakes

from management rather than related to the tool. In an article for McKinsey, Leslie Willcocks

also mentioned change management and leadership as important aspects for RPA

implementation (Lhuer, 2016). Accordingly, Rutaganda et al. (2017) highlights incorrect

leadership at the top level of an implementation project as a common and critical mistake. In

their research they found that projects that were IT-led failed to a greater degree than those that

were business-led (ibid.). This is a common misunderstanding among organizations - that

emerging technologies such as RPA, are the territory of the IT-function (ibid).

Earlier in this chapter, the researchers (hereon after occasionally called ‘we’ in this study)

introduced potential benefits and purposes of RPA, however despite the many potential pitfalls

for implementation, and the seemingly high rate of failures and disappointments with

implementing RPA as mentioned above - Syed et al. (2020), claim that (then) existing literature

“lacks a clear framework on what the critical success (or failure) factors are”. The below

sections will present existing implementation guidelines for RPA systems identified, and

considerations of scholarly contributions on technology implementation outside the field of

RPA will be mentioned.

2.6 Guidelines and models for implementation of RPA

Whilst the overall literature on RPA implementation approaches is scarce, some contributions

regarding implementation frameworks exist. In this section, we will present the most developed

frameworks for RPA implementation along with some minor perspectives on implementation

outside the field of RPA.

2.6.1 Frameworks for RPA implementation

Some scholars have addressed RPA implementation approaches in recent publications. As will

be seen, most emphasis is made to general processes and ‘parts’ needed in implementation

projects, without much attention to specific actions. These frameworks may, however, provide

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some helpful guidelines of the overall RPA implementation projects and the insights collected

for implementation improvement thus far.

Santos et al.´s (2019) conceptual model for RPA implementation success

In their review-article “Toward robotic process automation implementation: an end-to-end

perspective” aggregating insights from existing RPA implementation studies, Santos et al.

(2019) provides an overarching conceptual model of the main RPA topics including three broad

steps for successful RPA implementation, namely; strategic goals, process assessment, and

tactical evaluation. The first step “strategic goals”, suggest automation objectives need to be

established based on- and aligned with the company goals in order to motivate automation and

to provide a benchmark to evaluate and measure if objectives are achieved after eventual

implementation (ibid.). According to the model, these goals must consider the benefits,

limitations and future challenges of RPA in order to understand potential advantages

automation may bring, to avoid establishing unrealistic objectives which cannot be attained,

and to enable a long-term perspective for actions to address common challenges (ibid.). After

strategic goals have been established, Santos et al. (2019) suggests the process to be automated

needs to be assessed based on common RPA difficulties and avoiding processes requiring

adaptation to many changes in systems, a lack of rules, or other issues mentioned in literature.

Finally, after the most suitable processes have been chosen, the conceptual model suggests a

tactical evaluation of how to implement the RPA automation is conducted based on the same

factors identified in the literature (opportunities, difficulties and future challenges) to identify

e.g. integration needs.

Herm et al.´s (2020) ‘Consolidated Framework for Robotic Process Automation

Implementation Projects’

Based on a review of case studies and validation of the findings with RPA experts, Herm et al.

(2020) recently presented a consolidated and refined model for RPA implementation projects

in which the common stages of RPA implementation are segregated, and general guidelines are

suggested for each stage of the implementation process. The model is based on case studies

from a variety of heterogeneous contexts and the findings were generalized to capture general

RPA implementation projects (ibid.). The framework is divided into three phases for

implementing RPA projects, namely; initialization, implementation, and scaling, in which

some stages are performed once during each project, whilst others may be repeated (ibid.).

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Many stages will likely be overlapping, as described in the below model adapted from the

authors (see Figure 2).

Figure 2. Consolidated framework for RPA implementation projects (adapted from Herm et

al., 2020).

Herm et al. (2020) describe their review suggest RPA implementation project initialization

phase starts with identification of automation needs through e.g., workshops, surveys,

interviews, documentation analysis, business discussions and through conversation with

employees of relevant department(s). The authors also emphasize that early alignment with

business strategy to ensure RPA implementation can support strategic goals is an important

part of the initialization phase. Here, companies need to consider potential usefulness,

importance, and added value of introducing RPA early on in the project, identifying success

factors and obstacles/ interests for their organization and stakeholders (ibid.). Furthermore,

Herm et al. (2020) suggest the initialization phase include screening of different (RPA)

technologies to determine whether RPA can be applied usefully and which technology and/ or

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vendor is most suitable (not necessarily RPA but also e.g. AI or traditional BPM systems). The

authors also note such screening may be done proactively or exploratory, along with or after

the identification of automation needs (ibid.).

After verifying RPA as a suitable solution for the organization, Herm et al. (2020) suggest the

succeeding implementation phase include process selection focused on the prioritization and

selection of processes to be automated which require information from stakeholders and end-

users for better decision making. The authors explain previous case studies predominantly

consider low-complexity processes for testing and initial implementation, and suggest; level of

standardization, maturity (often synonym with level of existing documentation), execution

frequency and volume of processes should be considered to enable increased efficiency of the

automation (ibid.). The implementation phase also includes RPA software selection in which

the organization needs to consider aspects such as; eventual prior implementations, cost of the

software and skill requirements, in their decision-making (ibid.). Herm et al. (2020) also

emphasize that the increasingly maturing RPA market seems to allow for greater impact of

organizational factors rather than technical factors in software selection, and also that; vendor

reputation and support, data security of cloud solutions, as well as the available skills with

eventual external consultants often affect the decision.

Along with a longer period of evaluation of the business case with the eventual RPA solution

(which is commonly performed from early on in the initiation phase to late in the

implementation phase), the authors also suggest the implementation phase include a more

small-scaled verification of functionality, called proof of concept (PoC) (ibid.). Whilst the latter

PoC is aimed at determining whether the implementation of RPA is financially and technically

reasonable - preferably during several months to provide a detailed data-driven decision - the

business case evaluation should focus on ensuring organizational support based on indicators

such as improved processing times, human error rates and IT costs should be considered (ibid.).

After such activities has been completed to a sufficient degree, and decisions have been made,

Herm et al. (2020) describe RPA rollout takes place, comprising all activities with activating

the robots in the daily operations. The authors suggest RPA rollout strategies may not be RPA-

specific but apply to other software processes - the model lacked empirical data on how this

should be conducted.

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After the defined business case and successful PoC has resulted in a rollout, and hence that the

implementation project has been completed, the authors suggest a phase of scaling starts

whereby companies can consider expansion of the RPA portfolio (ibid.). The authors suggest

such scaling and adoption is facilitated by templates and RPA libraries of techniques, that the

complexity of processes automated is gradually increased to allow for gradually improved

automation knowledge, that external service providers may be used as a support for increased

complexity of processes, and that affected employees must be involved in early stages of new

automations in order to ensure a positive (ibid.).

Herm et al. (2020) further suggest two additional categories of organizational considerations

for RPA implementation, included outside the model´s three phases of initialization,

implementation and scaling, namely that; a center of excellence (CoE) should be set up, and

that RPA support processes are key to ensure efficient use of robots. The CoE should be set up

to support monitoring and maintenance of software robots, including the definition of roles,

KPI:s, skills etc. (ibid.). The CoE is often anchored in the business side rather than the IT

department and provided the required resources, implementation of a CoE is more feasible for

large corporations, however even smaller companies should ideally have at least one FTE to

manage RPA knowledge and future projects (ibid.). Furthermore, the authors underline that

RPA support processes including top management support with regards to financing and

strategic awareness for RPA, governance guidelines and integration of IT and change

management is crucial for RPA implementation to ensure long-term success (ibid.).

Herm et al. (2020) conclude there is no generally valid procedure however that using a majority

of these steps will likely be a good fit for many implementations. The model, authors say;

“provides a clear methodological contribution on how to approach RPA implementation

projects comprehensively and it is of practical value for companies [...]” based on interviews

with RPA experts (Herm et al., 2020). The study was recently published and our literature

review identified no recognition-, criticism- nor application of the framework. The overall

description of RPA implementation considerations of Herm et al. (2020) also includes the main

points of Santos et al. (2019); strategic goals, process assessment, and tactical evaluation.

Overall, it is the most comprehensive model our literature review could identify - and it hence

seems well suited as a basis for our analysis.

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2.6.2 Implementation theory for technology fields outside RPA

Technology Acceptance Model

Previous RPA implementation research has used external models such as Technology

acceptance model (TAM) to describe how users of technological tools experience technological

development (Legris et al., 2003). TAM is a model developed to describe an individual's

attitude towards technology. This model is based on “perceived ease of use” where the

experience towards technology is affected by how user-friendly and problem-free the

technology is, where higher ease of use has a positive impact on the individual's attitude to

technology. “Perceived usefulness” on the other hand is affected by how useful the technology

is and to what extent this technology will assist and boost the performance of the users (Davis,

1985). This study will only touch on the concept of TAM and will not go deeper into details as

the focus of this study concerns the implementation and not the experience around it provided

that we have an implementation method-oriented approach.

Learnings from ERP system implementation

Looking at implementation studies from business technology outside RPA, some literature can

be found investigating common issues and takeaways for system implementation. In their

Learning from adopters’ experiences with ERP: problems encountered and success achieved,

Markus et al. (2000) provide a list of common issues with ERP implementation and their

respondents conclude several actions could have been taken to avoid such implementation

issues. Markus et al. (2000) underline that companies experience problems in all phases of

system life cycles with business systems such as ERP, and note that many of the problems

companies may experience in later phases originated earlier however remained uncorrected or

unnoted. A greater, early attention to common issues should hence decrease risks for later

setbacks with system implementation (ibid.). The actions suggested to improve implementation

success comprise;

1. Doing a much better job of end-user training during the project phase.

2. Starting the project phase with plans for long- term maintenance and migration.

3. Documenting the reasons for configuration decisions, not just the parameters,

so that people not involved in the project phase can get up to speed quickly.

4. Not disbanding the project team when the project goes live, but instead staffing

a competence center for managing future evolution and learning.

Markus et al. (2000, p. 263)

15

Whilst these suggestions are based on implementation of comprehensive ERP systems with

different technology and scopes of RPA projects, the actions presented in their learnings

correspond well with the RPA specific recommendations for successful implementation

provided by Herm et al. (2020) and Santos et al. (2019).

2.7 Conclusions and analytical framework

In this theoretical framework we have briefly presented the fundamentals of RPA and its

commonly mentioned benefits. Identifying and describing foundational functionalities and

potential benefits is naturally relevant to gain an understanding of what the technology may be

used for, and potential benefits to realize with RPA implementation - to provide a basis for the

research on how such benefits may be realized through facilitated implementation.

Furthermore, the most common issues and disadvantages with RPA described by literature were

presented, including the commonly cited issue of maintenance efforts needed, as well as the

limited suitable use-cases for RPA automation. The latter, limited use-cases, may provide

criteria and guidelines to consider when evaluating processes to potentially automate. Previous

scholarly contributions describing suitable processes to be automated point to the fact that

banking and financial services may provide suitable use-cases for RPA. This industry

perspective supports the relevance of studying the this specific industry, and may also be used

for analysis to put the case company, it's characteristics and our findings in a broader context.

Based on the literature reviewed, many potential-, and common issues with RPA

implementation were presented. Although previous sources point to statistics displaying high

levels of RPA implementation failure, few sources discuss practical and detailed courses of

action to facilitate successful RPA implementation. Despite lacking research within the field,

especially two recent and (relatively) comprehensive models describing RPA implementation

success factors were identified and highlighted. Furthermore, the framework also included

results from literature searches outside the field of RPA implementation including TAM and

ERP technology implementation learnings. Similarities between the two RPA implementation

models were identified, and similarities could also be seen with implementation guidelines

outside the fields of RPA, providing a basis for analysis and comparison with empirical

findings.

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Conclusively, whilst existing literature on RPA implementation methodology and best

practices for benefit realization is limited, the identified scholarly contributions and research

gaps provide starting points for a research design aimed at expanding empirical evidence on

best practices and implementation success factors.

2.8 Analytical model

Based on the findings presented in the theoretical framework, and the ambitions to describe

how RPA-implementation may be facilitated, the following analytical model (seen in Figure

3) was created with the intention to categorize important themes on the subject to be used in

our analysis.

Figure 3. The analytical model.

The first analytical component of ‘Potential benefits to realize and reason for RPA usage’ is

used to include fundamental drivers of RPA usage (to enable analysis of what companies may

want to achieve with the implementation and hence what the most important potential issues to

mitigate are). The central part of the analytical model is found under the horizontal divider,

namely ‘common issues and challenges potentially obstructing implementation’ and

‘implementation models and approaches suggested by literature’ focusing the analytical efforts

on identifying problems potentially causing implementation issues, as well as to identify best

practices and approaches to avoid issues and facilitate implementation. Through focusing on-

and applying the implementation model suggested by Herm et al. (2020) we intend to

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benchmark previous implementation models with the approach used by the case company.

Inspired by Herm et al. (2020), large parts of the data-collection and analysis will be structured

around the models suggested phases of implementation projects, and the suggested actions for

each respective phase.

2.8.1 Simplified analytical model

Simplified further, the logic of the analytical model of this thesis and the above-described

approach can be visualized as seen in Figure 4 below, moving from understanding goals and

intentions, common issues and challenges to overcome to achieve these goals, and finally

looking at potential approaches to avoid/overcome issues and generally facilitating

implementation projects.

Figure 4. Simplified analytical model.

Based on this analytical model, both eventual similarities and discrepancies between theory

and empirical data within each theme should contribute to answering the research question. In

below sections, the methodological considerations made to ensure relevant data-collection

and practical analysis in line with the analytical model will be presented.

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3. Methodology

The purpose of this chapter is to present the methodological choices made to meet the purpose

of this study and to create conditions to answer the research question, including argumentation

and reflections regarding the suitability and potential consequences of these respective choices.

3.1 Research approach

This study tried to capture the experiences of respondents having implemented RPA in their

organization concerning their perception of common challenges and best practices for

implementing RPA. The intention of this study was to capture experienced best practices and

compare these to previously developed frameworks regarding RPA implementations, with an

ambition to find possible areas of improvement. In order to investigate how, but also why, RPA

is implemented - to understand goals and eventual priorities during the implementation and

contextualize the findings - the researchers, hereon after called ‘we’ in this chapter, also

attempted to include considerations for the intended value to be created by implementation.

The epistemological stance of this study was reflected in different research approaches and

methodological considerations made. Three research approaches are common in research,

including; an deductive approach is centered around producing hypotheses to be confirmed or

rejected by testing already generated theories whilst an inductive approach instead aims to

generate new theory from our empirical experiences (Bryman, 2012). However, based on

Dubois and Gadde´s (2002) description of that an abductive approach can be beneficial if the

study intends to discover possible “areas of improvements” that perhaps can contribute to new

concepts and a development of existing models, we found the abductive approach to be an

appropriate choice for this study as it intended to examine best practices for RPA

implementation. By applying models and frameworks for successful RPA implementation

provided by previous scholars against empirical insights from the case company´s

implementation, the aim was to contribute to the development of existing theories and

frameworks.

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3.2 Research design

3.2.1 Qualitative case study design

According to Bryman (2012), a qualitative study is suitable for studies which strive to create a

deeper understanding of various patterns in a specific event by capturing the perception of the

participants. Accordingly, to increase the understanding around what parts that may cause RPA

implementations to fail and which factors that may be decisive for the implementation – based

on actual experiences - a qualitative case study was considered to be the most suitable approach.

Supporting this logic, Yin (2009) highlighted that case studies are often considered suitable to

create an understanding of a company’s decision-making process and what impact these

decisions have on a company's approach. An additional factor that separates a case study from

other studies is that it may generate an in-depth understanding of a real event or phenomenon

(Yin, 2009). Accordingly, a case study may contribute to a more specialized illustration of how

to best implement RPA based on the case company´s perceptions and experiences from

practice. Criticism has however been directed at case studies related to their tendency to be

influenced by preconceived notions (Yin, 2009), which the researchers of this study reflected

on and constantly tried to avoid. According to Bryman (2012), researchers have to decide on

what level the empirical data is to be collected on. This research is based on a single case design

based on a convenience sample. This approach was deemed suitable provided the intentions to

capture a detailed picture of the implementation process and it´s different steps, which would

have been difficult to achieve if several companies were to be analyzed during a short period

of time. Moreover, provided the inclusion of expert consultants, the single case study design

would still allow for data collection based on vast experiences from several previous

implementations and cases – applied to and explained in the context of the case studied.

3.2.2 Case company and participants

According to Bryman (2012), theoretical sampling is an ongoing process where data is

collected and analyzed in order to know what data to collect next, and where to find that data.

In accordance with Bryman (2012) description that convenience sampling is based on what is

available to the person(s) conducting the study, the researchers had an established relationship

with the case company. The case, however, also matched the purpose of the study in many

regards. The relevant case company is a Swedish venture capital- and advisory group providing

loans, financing and business development advisory to small and medium sized companies.

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The group has roughly five hundred employees, which compared to the major players in a

Swedish banking and financial services context is relatively small - however the company's key

business activities revolve around credit which is a key business of the industry. Furthermore,

the case company's communication regarding the RPA project was positive and indicated the

project was ‘successful’, theoretically increasing their opportunities for investigating

facilitating aspects. As mentioned in previous chapters, banking and financial services business

offer suitable opportunities for RPA (Syed et al., 2020), motivating our choice of sector - and

provided that the company's processes were typical for the industry and seemingly considered

their project as successful, we deemed the case to be suitable for the study.

Through conversations with the company, it was revealed that they hired consultants in their

implementation process and a snowball sampling (as described by Bryman, 2012) began where

new respondents emerged over time from various recommendations. This selection process

resulted in contact with three respondents (presented in Table 1) with key roles- and vast insight

into the project and the steps taken during the implementation. Respondents has been given

abbreviated titles in order to;

1) ensure anonymity and to disclose neither the participants’ identities, nor the names of

their respective firm, and to;

2) facilitate the reader's ability to see statements and quotes from the respective respondent

in relation to whether he or she has a consultant or client perspective.

The coded names are based on whether the respondent belongs to the client organization (the

case company implementing RPA), abbreviated as “CL”, or the consultant organization (the

firm assisting the case company with the RPA implementation), abbreviated as “CO”. The

respondents from the same organization are distinguished by numbering (1 or 2).

Table 1 – Sample overview

Participant* Profile Interview

duration

Interview

setting

CL1 Internal project manager for the RPA project, heavily involved in initiating

RPA usage. Has started and managed the customer service department

within the case organization.

65 min Video call

CL2 Head of digitalization with the case company. Three plus years of experience

within the case company and broad background within innovation, tech and

startups.

63 min Video call

CO1 CEO of the consulting group's subsidiary focused on Automation. Has 15

years of experience in management with a focus on IT. Extensive experience

58 min Video call

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with automation and four plus years of experience with RPA. Ultimately

responsible for the delivery.

CO2 Business analyst and project manager from the consulting firm.

Three plus years of RPA implementation before the project, focused on areas

such as identification and evaluation of process candidates, team lead of

development teams, testing, deployment, maintenance, CoE and strategy.

N/A E-mail

correspondence

*Participants' designated names are based on if the respective participant is part of the client-

(case company) or consultant organization.

3.3 Empirical data collection process

In order for the research question to be answered following the logic of the analytical model,

semi-structured interviews were considered to be the best choice as this technique makes it

possible to create a more in-depth understanding of the company’s implementation. Semi-

structured interviews have been described as appropriate to create a broader understanding of

a specific case (Kvale & Brinkmann, 2015). Accordingly, we found semi-structured interviews

suitable to facilitate empirical data collection from practical experiences and increased

understanding of how respondents experienced an implementation, the different phases and

actions, difficulties and success factors - to answer our research question. Semi-structured

interviews thus became the obvious choice as we were able to create an in-depth understanding

of the case.

Kvale and Brinkmann (2015) describe semi-structured interviews can create an exchange of

knowledge between the interviewee and the respondent in which the questions should be

adapted based on the person being interviewed, to achieve the best possible result. Accordingly,

the focus and follow-up questions was somewhat adjusted between the interviews of CL- and

CO respondents to best capture the case company´s and the ‘expert’ perspectives respectively.

Respondent CO2 could only participate in the study via email correspondence due to time

constraints, and hence - since the project phases had already been described by the other

respondents - the interview questions sent to respondent CO2 excluded theme C (described in

Table 2).

In order to ensure relevant knowledge regarding the implementation process all respondents

included in the study had key positions during the implementation project. Including only the

knowledgeable, key actors from the project - meant a restricted number of participating

respondents. In order to counteract eventual downsides of the smaller sample-size, the data

22

collection also comprised reviewing project material and most importantly the case company´s

own visualization of the project and its phases. As described by Bryman (2012), such a multi-

method and reviews of additional material enables and triangulation and hence increased

reliability. This approach enabled comparisons of the respondents' answers with the additional

material, increasing the reliability through triangulation and ability to double check the answers

provided by the respondents.

To answer the research question, the empirical data collection followed the logic of the

analytical model (presented in Figure 3), in turn based on the purpose, theoretical framework,

previous research and research gaps presented in this study. The intention was to capture RPA

implementation intentions, issues and challenges, success factors and approaches used - in

order to facilitate greater understanding of how RPA implementation may be facilitated. In the

below table (table 2), the main themes of the interview guide and the logic behind their

inclusion is presented. The full interview guide can be found in Appendix 1.

Table 2 – Interview themes

Theme Objective

A Interviewee profile and role To understand the background and experience of the interviewee,

contextualizing the collected data and ensuring relevance of participation

B The project -

Phases and actions

To capture the overall approach and actions during initialization,

implementation, post-roll-out and support processes (based on Herm et al.´s

2020 categories to provide structure and enable comparison)

C Issues and challenges To capture eventual issues and challenges obstructing RPA implementation

and how these were solved

D Success factors To capture keys to facilitate RPA implementation

3.4 Data analysis

A common approach to qualitative data analysis is thematic analysis (Bryman, 2012), which

allow a deeper understanding of text than words or keyword-in-context (KWIC), via an analysis

that permits researchers to identify thematic subjects with both explicit and implicit expressions

(Guest et al., 2011). To identify themes that were true to our data set, we followed Ryan and

Bernard's (2003) guidelines which emphasize attention to thematic- or linguistic clues

contained in respondents' responses, and as suggested by Guest et al. (2011), a hierarchical

structure with themes and sub-themes was used. Provided that the analytical model and the

interview guide revolve around distinct, separate themes, the thematic analysis naturally tended

23

to follow the same structure. We found these methods for data analysis favorable for

distinguishing and presenting the meaning of our data in a logical order.

All interview data files were temporarily saved, transcribed, and then deleted. The transcription

began after the first interview and were conducted simultaneously as the remaining interviews,

which enabled us to recall the key findings and to identify suggestions of useful questions and

questions for the remaining interviewees. During the transcription we tried to reflect the

statements of the respondents in literal and also non-verbal statements, such as chuckling or

emphasizing certain words, so that we could reflect and analyze their responses fairly and

appropriately.

3.5 Reliability, validity and replicability

According to Bryman (2012), a study's credibility is reflected in three underlying criteria which

are validity, reliability, and replicability, all of which form the basis of the study's credibility

within the field of business economics research. Bryman (2012) describe that a study's

credibility can be measured based on "external reliability", which shows the study's

replicability. With this in mind, we have chosen to ask questions regarding the implementation

of RPA to a company that has recently worked with this in the financial sector. It was assumed

respondents ability to recall important aspects of the implementation would be dependent on

their memory and hence time passed since implementation. We hence assumed a study of a

recently completed project should provide more accurate answers, which affected the choice of

case. We have also worked with the internal reliability of the study, which implies that the

content of the study is perceived in a similar way by several observers (Bryman, 2012). Since

we are three authors, we were able to discuss the interviews afterwards and reflect upon the

answers, increasing the likelihood that the answers have been interpreted correctly.

Furthermore, Bryman (2012) highlights that internal validity concern whether the respondents'

answers reflect upon the theory development generated by the study. As the study sought to

investigate existence of eventual areas of improvement in the suggestions made by previous

studies, the ambition and focus was to further develop existing frameworks by finding and

highlighting eventual differences in the case company's implementation approach and success

factors, compared to theory. Finally, Bryman (2012) also presents the external validity, which

demonstrates the extent to which the outcome of the study is generalizable. As this is a case

24

study, we are aware that the generalizability may decrease as the answers are based on a specific

company and adjust interpretations of our results accordingly. As previously described

however, the case company was chosen based on our initial perceptions of that the case

company and its products were somewhat typical for the industry, in an attempt to increase

generalizability.

3.6 Method discussion

As described in previous sections, the methodological approaches are based on careful

considerations and attempts to answer the research question in the most accurate way possible.

Several weaknesses are however inevitable provided the methodological choices made. As

emphasized by Bryman (2012), data collection in a qualitative study can easily be affected by

subjectivity, and researchers interpretations in the analysis. Furthermore, it is argued that the

outcome of qualitative studies can rarely be generalized (ibid). To reduce the risk of

subjectivity, the interview guide was developed from a neutral position based on our analytical

model (based on previous literature) and especially on previous implementation frameworks.

Furthermore, in accordance with the recommendations related to ethical research described by

Diener and Crandall (1978), we have been careful not to disclose any information that may

reveal the companies nor respondents' identities, carefully informed respondents on the

research purpose and how the data will be used, and made sure to make fair representations of

previous literature and our findings.

Conclusively, the methodological choices made in this study is built on wide and conscious

considerations of pros and cons with different alternative approaches. Irrespective of the

potentially negative aspects mentioned herein, we deem the methodological choices made as

suitable for the purpose of the research provided the contextual conditions.

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4. Empirical findings

In this chapter, the findings of the qualitative data collection will be presented in clusters of

themes and headings, following the structure of our interview guide and analytical model.

4.1 Structure

The empirical findings in this chapter will be presented in accordance with a thematic analysis

conducted based on the main themes of the analytical model and interview guide (in turn based

on the literature review, research gaps identified, consequential research question and purpose

of this research). First, a brief description of the relevant automation and the case company´s

fundamental intentions with implementing RPA will be presented, followed by a breakdown of

the different actions taken and considerations made during the main phases of the project,

namely initialization, implementation and post-implementation, as well as overarching support

processes. Subsequently, issues and challenges- as well as aspects facilitating the RPA

implementation will be presented. References to respondents will be made through the assigned

abbreviations (previously described in Table 2).

4.2 Fundamental aspects about the case and reasoning behind the RPA

implementation project

In order to understand the project and the characteristics of the automation to facilitate analysis,

an initial objective of the data collection process was to capture; what processes the project

intended to automate and the main intentions for the case company to implement RPA.

The RPA implementation was described as part of long-term strategic digitalization- and

efficiency initiatives, in relation to which the group had considered RPA during several years.

The primary, specific drivers described by respondents CL1 and CL2, was an increased need

for processing capacity and efficiency, evident opportunities for ROI - and high alternative

costs for meeting the increased need for capacity - as well as ambitions to decrease processing

mistakes and to minimize the amount of administrative and “boring” activities performed by

employees. The targeted process to automate was related to loan-application with the ambition

to remove time consuming and manual processing performed by advisors through moving

information from the client’s loan applications into- and between the different systems needed

to document, process and make a decision regarding the application.

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4.3 Phases and activities of the project

A key objective of the empirical data collection was to capture what activities the case

company performed during the implementation of the RPA solution. In below sections, these

phases and the respective activities will be presented.

4.3.1 Initialization

With regards to the initial phases of considering automation and the early discussions leading

up to the RPA implementation project, it was evident throughout the interviews that

automation-initiatives and analysis of characteristics and functioning of processes had been

considered long before the RPA solution was launched. Following the major strategy work that

was carried out in 2018 where the key word was digitization, the company's digitization

manager, respondent CL2 carried out a survey and interviews regarding the processes and

activities for the two main products, in order to investigate possible processes digitization and

streamline, creating a clear process map of each product. What was seen according to

respondent CL2 was "[...]one product had an unstructured process, while the other was

structured". Respondent CL2 concluded that “…the product-process was time consuming,

inefficient and boring to handle for the employees”. Respondent CL1 explained that "[...]

during this work we talked about RPA and robotics [...] I was given the mandate to spend time

diving into RPA and reading reports and going to seminars and creating an image for myself".

Moreover, respondent CL1 got in touch with different RPA-vendors in the industry to gain

knowledge about how RPA works and how to implement the technology. Realizing they did

not have the knowledge or resources to build their own robot internally, the case company,

according to respondent CL1 and CL2, turned to their own IT supplier for insights.

The business case

A recurring theme regarding the initialization phase of RPA projects was the focus of

establishing a business case. According to Respondent CL2, the pandemic was "[...]what got

us started". Respondent CL1 described they realized that they would get more resources from

the management and owners to meet the expected increased demand for their structured

product. Respondent CL2 recalled that "[...]march showed an incredible demand for the

product. We went up by 300% compared to the same period last year. To be able to handle this

there were two options. Either we automate, or we hire an army of people to handle that."

Respondent CL1 and CL2 presented RPA as a solution to this problem for the top management.

27

The top management approved the initiative to start the RPA project as a solution and according

to respondent CL2 time efficiency and ROI were the deciding factors.

Selecting process and vendor

An evident part of the implementation project was selecting the process to automate and the

vendor to provide the services. Selecting process was rather easy according to the case

company. The goal was to automate processes related to the product which saw a big increase

in demand. For this goal, the case company turns to the consulting company for help with

automation. Based on respondent CO1’s experience, this was "[...]a classic example of where

you choose a process based on subjective assessment", which respondent CO1 suggests can

increase the risk of a failed implementation. Ideally, in this phase, according to respondent

CO1, processes with high business value and low complexity should be chosen. The respondent

described this evaluation can be visualized in a matrix, which has been attached in Appendix

2. They also need to meet the criterias of being "[...]high volume, repetitive and rule-based with

few exceptions”. According to respondent CL2 which functions and units would be involved

in the large interview work that took place whose purpose was to map every click end users

made step by step of the particular aimed process. According to respondent CO1, an important

part of this phase is to work iteratively and having weekly meetings for identifying faults

increases at the same time as questions regarding the robot or project can be answered.

According to respondent CL2, UiPath was chosen because they could deliver a cloud-based

solution in Sweden, and respondent CO1 believes that the choice of UiPath was appropriate as

the organization works with big data. Respondents concluded great similarities between

vendors and that the consulting firm's relation with the provider was important. Respondent

CO1 adds that "It can be devastating if you choose the wrong platform", and explains the choice

of platform should be based on organization´s data-processes, systems and applications.

4.3.2. Implementation

As seen in above sections on the initiation phase, the respondents emphasize much of the work

related to RPA is conducted as preparations before the more technological aspects of the

implementation. A quote from respondent CO1´s illustrates this attitude, saying; “I do not think

it [the phase] is that interesting, honestly”. Instead, the respondent said, it is more of a sanity

requirement to have a sound way of working and a systematic approach. The steps needed for

28

implementation, however, are described by respondents throughout the interview and included

in the material and models received by the case company; Acceptance Tests (AT), Request For

Change, and Deployment, as described in Figure 5, were categories of activities included in

the implementation phase.

Developing and testing the robot

The consulting company was responsible for the testing of the robot according to respondents

CL1 and CL2, whilst the general project team interacted with workers, to validate the

functionality (following the recommended approach from the consulting firm). Respondent

CO1 believes that this is not a difficult phase in the project as long as it is done in a systematic

way, describing that; "From a development perspective, it is not so revolutionary. You work on

validating requirements, you develop a proposal for a solution, you validate the solution, you

build on the solution and acceptance test it before it is put into production ".

Robot roll-out

Once everything had fallen into place, the last phase of the implementation began, to deploy

the robot. Before making this happen, a request for change was applied to a specific group

within the organizations which decides for every new IT-implementation. In this case, RPA

was approved and thus the robot was deployed. An important aspect in this phase is according

to respondent CO1 "[...] involve the business" and work iteratively. In this regard, respondent

CO1 considers that the case company did not succeed and explains that "[...] once the case

company had built the robot and would put it into production, they were not clear enough in

the communication with the business". Respondent CO1 continues "It had not been ensured

that the end users received the information and gained sufficient understanding about the robot.

It may be small things, but it creates a friction that can be negative in the long run if you want

to scale up the automation".

4.3.3 Post-implementation

Immediately after the roll-out was made, its functionality was monitored by the consulting

company around the clock for two weeks according to respondent CL2, remediating bugs and

deviations. It was decided that the consulting company would continue to assist with

maintenance, delivery of the platform and support, but without round-the-clock surveillance.

Respondent CO1 explains that the most common errors that occurred and still are occurring are

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not due to the robot, but due to "[...] people who pushed or changed where they should not,

which can ruin the robot." Respondent CL1 emphasizes that it has not been an unproblematic

post-implementation. Changes in the underlying system have been made since the deployment

without informing the consulting company affecting the functionality of the robot. To

counteract these problems, respondent CL1 emphasizes that organizations should view the

robot as a living being that needs to support and be evaluated. Respondent CO1 has a similar

view “"A robot needs a supervisor who evaluates its work on an ongoing basis". Respondent

CL1 also emphasized that stewardship and maintenance of the knowledge and further

development of RPA (as mentioned in the maintenance and management in Figure 5) is

important, however that the case company may not have fully succeeded with these activities

due to other priorities.

4.3.4 Support-processes

In the following sections, the findings related to support functions such as center of excellence

and management support are presented.

Center of excellence and internal RPA knowledge

When talking about support processes and when asked about allocating resources or

establishing a new part of the organization to work with RPA in and manage knowledge,

respondent CL1 answered that; "my straight answer is no” [...] the intention has been that it

should be someone or some people who work with it within the organization but have not got

there”. Respondent CL2 also points out the importance of their own competence about the

robot within the organization "I think in the long run that it is best if the case company builds

up its own department for RPA''. Respondent CO1 agrees in this matter and emphasizes the

importance of having abilities within the organization, post-implementation. This can be

managed by either hiring or establishing knowledge within the organization. Both the case

company and consulting firm feels that the management has not prioritized or allocated

resources for this post-implementation.

Management support

During the phase of identifying technologies and ways of digitizing within the case company,

both respondents CL1 and CL2 were given a mandate by the management to set aside time for

investigating RPA. When asked directly if they experienced that they received management

30

support, respondent CL2 answered the following "We received very strong support from above,

our CEO bought into RPA directly and saw that this was the future". Throughout, the

management support seems to have been high during the implementation phase. However, the

respondents from the case company suggest that the support was not forthcoming when asked

for resources to scale up RPA within the organization.

4.3.5 Model and summary of the implementation project phases

As described in the project documentation material received by the case company and

respondent CL1, the different specific phases of the project were described and visualized in a

process the consulting company tends to use for the same purpose. The documentation received

in our data collection concerns especially the part of the process after involving the consulting

firm and not the extensive preparations and initial phases described in previous sections of this

chapter. In summary, this breakdown of the project´s main parts were described as consisting

of first; establishing contact with the relevant department of the consulting firm, identifying

roles internally and creating a project organization before developing a working-process for

RPA. The latter project phases were visualized through a number of fundamental sections in a

simplified flow-chart. An adapted version of this flow-chart can be found in Figure 5. The

activities of each respective phase are described in Appendix 3.

Figure 5. Summary of the implementation project as visualized by the case company

(adapted from material received from the case company).

Whilst this process description is brief and simply provides an overview of the main phases,

respondent CL1 was ascertained transparency and visualization of the process and ‘the steps to

be taken’ was crucial in order to make sure the organization was onboard with the process.

4.4 Issues and challenges

Throughout the data collection and interviews, a large number of potential issues and

challenges with RPA implementation have been identified. In general, we have seen that the

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respondents' emphasized the human and organizational issues to a larger degree than the

technical aspects. This relationship and general predominance of human and organizational

challenges is exemplified with a quote from respondent CL1, emphasizing that; “[...] what I

thought was going to be problematic was not problematic… and that was the technological

aspects. [...] The challenge is the human behind [the technology] and the organization”. In

accordance, most of respondent CO2´s issues (and solutions) revolved around human aspects.

In below sections, the most prominent issues and challenges as described by the respondents

will be presented.

4.4.1 Human and organizational issues

During the conversation with the respondents of the study, it emerged that change management

was one of the problematic factors that was shown to influence the implementation of RPA.

Respondent CL2 believes that there was opposition from certain parts of the organization

regarding robotization, where the robot was associated with a cutback in the workforce. The

implementation initially gave the impression of "here comes a robot" which made employees

think about whether their job was at risk of being automated, which would have an impact on

their employment. With regards to the previous emphasis to that respondent CL1 did not

consider the technological aspects as problematic, but instead that the human- and

organizational aspects were the most challenging - the same respondent provided a breakdown

of how these problems were evident in respective parts of the project. Before the project was

started, respondent CL1 explained, it was challenging to make people feel comfortable working

with a robot. During the process, when the robot was configured, it was challenging to get the

organization to stick to implementing the robot without evaluating why things are happening

the way they are at the moment. It was not the robot’s task to rationalize elements, it was to

map elements as they already did in the organization. CL1 says that this has been a challenge

in their PDD but also for their acceptance testers to stick to the focus issues where they should

not develop the process but rather take it as it is. Developing the process is another workshop

and not the main purpose of their implementation. However, according to CL1, the consultants

were pretty good at putting an end to this when they started to slip away from the main subject.

Moreover, as will be described in relation to the below issue of choosing the right process to

automate, respondents CL1 and CL2 also described that it was challenging to ensure that

employees recalled and mapped the processes correctly, including all necessary steps.

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4.4.2 Issues with choosing the right process to automate

A common theme of potential issues in the RPA implementation process, recurring throughout

our interviews, was the potential issues with choosing the right process to automate.

Respondent CL1 believes that if they would have an organization that realized the benefits with

RPA and observed the needs for more robots, they would probably have ended up in a situation

where several processes could have been automated. Then the organization could have

processed which robots to focus on first. This is something they have not come to use, but if it

had been possible to present about 10, 15 or 20 processes to choose from and to get a good

decision support for, a scale up of the RPA implementation could more easily be carried out.

This was something they did not find available at the start of the implementation process.

Furthermore, respondent CL2 believes that the first time you perform an RPA, you take very

large scoops. It is a lengthy process that must be automated that goes over a number of units,

departments and systems. Respondent CL2 believes that this process is problematic and even

though they implemented the robot, not all parts were automated, and a few parts had to

continue as they did before. As described by respondent CL2, one of the biggest challenges

when choosing a process was to choose a process that is achievable to automate and not to take

too big "steps" directly "and divide the implementation into smaller sub-processes.

According to respondent CO1, this is a classic mistake as it often leads to choosing a process

of subjective judgments or political decisions. The challenge when rolling out is therefore

whether you have captured the requirements regarding the process that is to be automated, i.e.,

to have the people in the business to truly understand the importance of having to set

requirements at a detailed level or explain at a detailed level how the process works and how

this can be automated. Furthermore, respondent CO1 highlights that the harder part of change

when automating processes is when people start to become redundant. Then it is important to

get help from those with expertise in the area. Change management is therefore important,

which is about managing expectations among people within the organization through

information and education. It is also important to emphasize that automating without

maintenance is not optimal as processes no longer work after changes. Therefore, respondent

CO1 believes that the choice of process to be automated should also be based on the process

remaining as it is and not a process that undergoes constant development. When a development

in a process takes place, it will affect the robot where it would have to be reprogrammed at each

such event.

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4.4.3 Issues related to changes in systems

The robot is programmed to copy a work routine and perform a task in a specific way.

Therefore, changes in the underlying system will have a direct impact on the robot's

performance. Respondent CL2 states that if there was an update where a specific bottom

changed color or was moved to another location, the robot would no longer understand how to

move forward as it was programmed to follow a specific pattern. Getting the organization and

colleagues in key positions to think about the robot was therefore challenging in this case as a

simpler change could have a major impact on the robot's work process. Respondent CL1 also

shares this view and says that no one wants to oppose the automatization as it would be

considered a direct misconduct towards the organization. On the other hand, it can be

considered difficult in situations where everyone works very hard in a pandemic, to remember

to think about the possible outcome in the event of a change. It is the human factor where the

organization must think about how a change in some way can affect the robot's working

process. This applies not only in the first stage but also in the 2nd and 3rd stage where the robot

works. It was thus clear from the respondents who worked with the implementation of RPA

that changes made by people who did not understand how this will affect the robot were

considered problematic, as each change entails the need for reprogramming so the robot can

relate to the new way of working.

4.4.4 Issues for broader adoption and scale-up

When it comes to scale-up of the RPA implementation, it is something that has currently been

deprioritized. CL1 believes that if it was up to the IT-department to decide, they would have

had significantly more robots than they currently have. The problem with scale-up is that the

organization does not see the benefit of further automating processes in today’s situation. The

decision model in the company is based on the fact that the organization needs to demand a

further development of robotization, something that may be due to the lack of knowledge and

insight into the usefulness of the robot and what it can achieve. This is something that could

possibly be due to fear and uncertainty, which has resulted in IT not being able to continue

working with their ideas, since no formal request has been made regarding further development.

Furthermore, respondent CL1 claims that this may be due to the increased workload on

managers that has arisen in connection with the pandemic, which may be the main reason why

the scale-up process has suffered. Furthermore, respondent CL2 highlights that if the

organization decided to implement additional robots, they can reveal that such a process is

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already available and put on “standby”. They already know how the teams should be composed.

Since the first robot has been implemented with the help of respondent CO1, a more realistic

schedule and cost calculation can also be presented, something that respondent CL2 sees as

something positive where they are ready for more.

Furthermore, respondent CO1 explains that the problem of scale-up can be explained by the

lack of understanding regarding the automation process where the end users did not gain a

sufficient understanding during the implementation of the robot. All of a sudden, there was a

robot doing parts of their jobs. According to respondent CO1, this can create friction within the

organization and have a negative impact on the long run and harm the scale-up process.

4.5 Specific aspects the respondents would have done differently

Whilst the respondents’ descriptions of the issues and challenges mentioned in this chapter

included indications of lessons learnt and hence potential actions to counteract negative effects

of these challenges, the interviewees also mentioned a small number of specific lessons learnt

and aspects they would have handled differently throughout the RPA implementation process.

Three of these will be highlighted below as especially evident.

A recurring and specific insight was that the respondents (CL1, CL2 and CO1) emphasized that

a small supporting organization or resources dedicated to RPA and maintaining, developing

and scaling the usage within the organization would have been beneficial. Furthermore,

respondent CL2 specifically advocated that, a posteriori, “having all the facts in hand, one

should probably take a larger amount of small processes which you automate with human

control-points in between them. That would have facilitated the work and I think it would have

been quicker”. By automating smaller parts of processes at a time with short sprints and many

smaller ‘victories’ along the way, a more positive attitude could have been created, meaning

that selling RPA implementation to the organization would have been easier. This, the

respondent said, is something they take with them for forthcoming implementations.

Furthermore, the theme of skills and education of employees was a recurring theme the

respondents emphasized as important. Accordingly, respondent CL1 highlighted that he would

have focused increased RPA education in the organization and with the board of directors

especially, if the process were to be repeated. If I were to re-do the journey [of the RPA

implementation] again, he said; “[...] We could perhaps have started with a trainee-program

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for group management within the field of RPA. Then, this program [and the knowledge] would

have seeped down the decision-lines in order to make sure everyone is aboard the train and to

say that ‘this is not dangerous’. That is something we should have wanted to do, if we were to

do it again. Some sort of educational module”. The same respondent also expressed that he

underestimated the overall RPA skill-level needed to implement such a solution, and that they

had to revise their ambitions of managing the implementation themselves, serving as a learning

on that it is relevant to seek professional assistance.

4.6 Success factors and facilitating actions

A key objective of the data collection and interviews was to capture what aspects the

respondents considered as important to facilitate the RPA implementation. Whilst the many

issues and challenges presented in earlier sections of the empirical findings represent clear and

many times straight forward aspects to carefully consider in order to enable implementation -

the respondents provided many opinions on what is important to succeed. The most prominent

of these actions and considerations mentioned will be presented below.

4.6.1 Preconditions and contextual factors

To begin with, in addition to actions actively taken to facilitate RPA implementation, we found

some aspects of the case company's situation as potentially affecting their abilities to implement

a RPA solution, based on the respondents' descriptions. A central aspect of this case company's

situation is that they had outsourced their IT to the company presented as ‘the consulting

company’ in this report. As respondent CL1 described it, the consulting company already had

an established relationship with the case company, already “[...] managing our digital

infrastructure with servers and knew how we worked etcetera. That meant we did not have to

start all over again [with a new relationship and setup]”. Similarly, respondent CL2 described

that IT-infrastructure was not an issue since the consulting firm already managed their

infrastructure and that a infrastructure-project had recently been conducted. Should the

consulting company have had another existing IT provider and hired the consulting company,

the respondent (CL2) said, “[...] it would likely have been a lot messier. Now [with the existing

relationship and insights into the IT environment] they [the consulting firm] could talk- and

solve a lot internally”.

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Moreover, the fact that the case company had already conducted work to understand and map

their processes - was described as a facilitating factor by especially respondent CL, naturally

facilitating- and shortening the process selection and mapping. In addition, the respondents

emphasized that the circumstance of the Covid-19 pandemic and the drastic effects it had on

the demand for their loans, created a clear business case and facilitated internal marketing of

the RPA solution.

4.6.2 Aspects to facilitate implementation

Selecting the right process

A recurring theme throughout the data collection process was the question of selecting the right

process and the work that should go into such decisions. In a conclusive manner, respondent

CO1 summarized what he perceived as important aspects for a successful RPA implementation,

in three key parts, two of which relate to the process to be automated, namely; (1) selecting the

correct process, (2) making sure you have really understood the process and why you want to

automate it. The respondent also explained that companies should avoid the common mistake

of choosing a process based on subjective judgement or politics. Regarding (1), both

respondents CO1 and CO2 recommended targeting the least complex process for automation -

for the reason that these are easiest to robotize while it can be used as a first example when

promoting RPA within an organization. As described in previous sections of the empirical

findings, respondent CL1 pointed to that they developed a retrospective list of evaluation

criteria for future decisions between processes to automate (attached in Appendix 2). A proper

foundation for such a decision, he said, is something he recommends companies focus on in

case several processes are potential targets. According to the descriptions of respondent CL2,

it is furthermore easy to choose a too broad process scope and instead indicate it is better to

automate small parts of the process at a time. When the process is selected, respondent CO2

emphasizes the importance of “[...] involve the business and the process experts throughout the

development”. This leads us to the importance of change management in RPA implementation.

Change management, communication and education

The importance of change management and communicating with employees with regards to

the RPA project, it´s impact on (and benefits for) the organization, was one of the most

prominent points made by the respondents throughout the interviews and collected material. As

mentioned under previous sections on issues and challenges for RPA implementation, the

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respondents often highlighted that the organizational and human aspects are perceived as more

problematic than the technological parts. Accordingly, the descriptions of keys to facilitate

RPA implementation and ‘success factors’ were to a large degree focused on how to manage

people and the organization. In respondent CO1´s summary of aspects to manage in order to

succeed with RPA an implementation project, the third and final aspect was described as

“[...]making sure the end user understands how it affects them when the RPA is in production”.

Communication with the end-user to make sure how their work is affected and may have to be

adopted, he described, is crucial. On the same theme, respondent CL2 described that an

important part of their approach was to perform seminars and weekly demos of the RPA within

the organization (for those interested) in order to spread the word about the implementation and

make sure employees feel involved and prepared for the robot.

Similarly, the word ‘education’ of advisors (employees) was recurring, and CL2 described

education as crucial for those whose chores were to be automated in order to make sure they

are involved. According to CL2, their approach was to involve a smaller number of managers

and ‘champions’ first in order to facilitate buy-in and let them spread the word about the project

and education to the rest of the teams. When showing the ‘advisors’ the boring things they no

longer had to perform, he said, the RPA solution was easily accepted. Furthermore, related to

the importance of change management, are also the questions of expectations according to

respondent CO1 who described a common issue to avoid is that many ‘people’ have too high

expectations, thinking “[...]it's just a click and drag and then you have 3000% ROI”. In regard

to how to succeed with RPA implementation, respondent CO1 also summarized these points

and the general need for change management and communication by saying change

management in RPA implementation projects is important and should revolve around

“[...]managing expectations among people within the organization through information and

education”.

RPA knowledge

Another evident theme throughout the data collection process was RPA knowledge.

Respondent CL1 highlighted that the RPA initiatives must be derived from and driven by ‘the

business side’ rather than from the IT department and if RPA knowledge is limited, decision

making regarding RPA will be harder. The skill requirements for RPA implementation projects

is, according to respondent CO1, underestimated provided that RPA is “low code” and hence

easier to get started with, however that the processing of fully developing and using an RPA

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solution is no less complex than any other software solution. Hence, he emphasized, it is also

important to have experienced developers involved.

Structure and approach is important

References to that structure and RPA implementation approaches are important, and were

recurring during the interviews. According to respondent CL2, having intermediate goals

during the project would have been a better approach to have. That way, it would have gone

faster, quicker and more qualitative, while not creating too high expectations within the

organization that can not be met. Moreover, respondent CO1 emphasizes having a systematic

way of working with the implementation phase by structured approaches to testing, validating

and launching the RPA solution represent no icing on the cake but is rather simply required.

The model for this approach streamlined the ongoing work by documenting in a structured way

all the steps the robot would do. When approval was needed from managers to proceed in

different phases of the project, this documentation facilitated the decision-making of managers

according to respondent CL1.

The importance of a clear business case

Throughout, the respondents emphasized the importance of knowing why to automate before

one automates. Without a business case, they agree that the risk of failing with RPA

implementation increases. According to respondent CO1, the importance of having a business

case before choosing to automate a process is one of the most important success factors.

Respondent CL1 agrees and believes in particular that time and money are aspects that must be

evaluated before choosing to invest in a new solution.

4.7 Final remarks regarding the empirical findings

In this chapter we have presented the results of a thematic analysis based on the analytical

model to investigate issues, challenges and facilitating aspects of RPA implementation. The

themes are evidently interconnected and many of the respondents' answers and the data

collected from project material reconnect to intentions, challenges and potential facilitating

aspects in the same points made. In the subsequent analysis, these findings will be condensed

and presented in accordance with the logic of the analytical model.

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5. Analysis

In this chapter, we will analyze the empirical findings with the ambition to gain a deeper

understanding of the respondents' perceptions of; issues and challenges of RPA

implementation, how implementation projects should be managed, and how these empirical

findings relate to theory.

5.1 Intentions and goals

The logic of the analytical model of this thesis was to capture the case company's goals and

intentions with the RPA implementation and hence facilitate understanding of priorities for

issue-mitigation during implementation. Many aspects of the company's reasoning are

interesting in relation to theory. The main driver of the implementation was the need for

increased efficiency and processing capacity in order to meet higher demand, faster and with

greater cost efficiency than what could have been achieved through hiring high numbers of

employees. Based on the common scholarly descriptions of that robots allow for increased

efficiency and FTE reduction (Lacity and Willcocks, 2015; Santos et al., 2019), may reduce

entry costs (Anagnoste, 2017) and may be developed much quicker than IT solutions relying

on APIs (Asatiani and Penttinen, 2016) allowing for faster ROI (Lacity and Willcocks, 2017;

Santos et al., 2019); the case company´s main drivers are much in line with the benefits and

drivers mentioned in theory. Furthermore, the secondary goals of RPA implementation

identified in the empirical data also match scholarly descriptions of RPA benefits, including;

allowing for more value-adding activities (Syed, 2020) and increased job satisfaction (Santos

et al., 2019). Security and compliance aspects (as described by Syed, 2019; Santos, 2019) was

not emphasized as equally important. Moreover, the respondents descriptions of why the

processes was automated, is much in line with both descriptions of the suitable use-cases for

RPA, including the processes high volume of information, interaction with several systems and

that they are standardized in terms of knowledge and experiences (as described by Asatiani &

Penttinen, 2016; Fung, 2014; Lacity & Willcocks, 2015, 2017) and also fit Vishnu et al.´s

(2017) descriptions of typical banking and financial services companies processes.

Altogether, the case company's goals with- and drivers for RPA implementation are; (1)

pronounced and thoughtful, (2) in line with theoretical descriptions of RPA benefits (with some

exceptions), (3) typical for the industry and match theoretical descriptions of suitable use-cases

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(indicating generalizability related to similar companies and processes), and; (4) manifold,

hence requiring diverse considerations for benefit realization in implementation.

No specific and pronounced effects could be isolated and derived from these individual

intentions, although some consequences were found to be likely. The case company´s

characteristics, clear business case- and urgent need for the implementation, evident ROI and

hence consequential; tolerance of costs, usage of consultants and generally systematic approach

for implementation - may have created a suitable context for RPA implementation. These

contextual aspects are relevant for the analysis of success factors and eventual mitigation of

issues and challenges, in accordance with theory highlighted in below sections.

5.2 Issues and challenges

A key objective of this research was to investigate issues and challenges of RPA

implementation. When comparing the empirical findings with theory, it was evident that several

of the common issues and challenges mentioned in the literature were not relevant in the case,

and directly dismissed by respondents, including; a lack of understanding of what RPA means

and that its definition (Santos et al., 2019), IT infrastructure (Stolpe et al., 2017), nor

uncertainties whether IT or business was leading responsible for the implementation (Santos et

al., 2019). As mentioned in previous sections, preconditions and previous actions may have

mitigated issues, in this case related to; hiring the existing IT supplier who were in control of

the IT infrastructure as the consulting firm and clearly defining roles. Furthermore, costs related

to RPA implementation, suggested to be a common issue by Stolpe et al. (2017) was not

described as an issue even though the costs became higher than expected. This latter mitigation

of cost-issues could likely be explained by high expectations of ROI, strong financial resources

and a clear business case. Furthermore, related to mitigation of common issues, the challenge

of choosing a suitable process to automate (as described by Santos et al., 2019) was emphasized

by respondents, although their previous work of mapping and understanding their processes,

and evident need to automate the specific part of their business - facilitated the case company's

decision making.

Whilst the scholars included in our literature review has emphasized that a series of more or

less technical issues are common during RPA implementation (e.g. Stolpe et al., 2017; Santos

et al., 2019), almost all issues and challenges emphasized by respondents relate to the challenge

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of adequate; (1) change management, (2) spreading of information, and (3) education- and

engagement of management and employees around RPA and the solution. One example with

additional theoretical connections, was making sure employees were fully informed and

involved in the changes before and during roll-out, and this was described to have led to friction

when handled somewhat deficiently by the case company. As described in theory (by e.g.

Santos et al., 2019; Asatiani & Penttinen, 2016), employees may be afraid to lose their job when

tasks are automated, and the same challenge was mentioned by the respondents who

emphasized the importance of vast change management efforts to counteract such feelings of

uncertainties. Similar attitudes and fears of the effects of technology are also described in the

Technology Acceptance Model (Legris, 2004) which, we conclude, may be a useful theory to

describe these attitudes the respondents said may occur during RPA implementation projects.

Moreover, the challenges of achieving adequate communication and change management, as

described by the respondents, led to undesirable actions amongst employees which altered the

conditions in the system. Such issues with changing IT environment/ information for the robot

to work with, has also been highlighted by Santos et al. (2019).

Although considered as successful, the case company´s RPA implementation was not free from

issues and challenges, which is consistent with the prevailing literature which has concluded

that issues and challenges are common (e.g. Gex & Minor, 2019; Rutaganda, 2017; Santos et

al., 2019; Herm et al., 2020; Gotthardt at al., 2020). Especially the empirical findings related to

that respondents described; (1) post-implementation issues and scaling of RPA was some of

the most challenging parts of the project, and (2) that technical aspects are considered as less

problematic than human/ change management questions - was surprising in relation to the

common issues emphasized in literature. Along with other current priorities during Covid-19,

the scale-up- and post-implementation issues were described as mostly related to lacking

managerial RPA knowledge and hence ambitions to further automate and utilize existing RPA

know-how, and generally keeping the organization attentive towards the robot regarding

decision making and system changes.

Based on the case-specific pre-conditions, including; long preparatory work on process

mapping and RPA functionalities, and an existing IT supplier who had expertise in RPA

implementation, it is reasonable to speculate that many of the common technical issues

regarding RPA implementation may have been mitigated. In accordance with the descriptions

of Lacity and Willcock's (2018) on that issues and challenges are often related to management

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mistakes than with the RPA tools themselves - we conclude many of the main issues and

challenges described by respondents are related to human aspects, management and

communication. Accordingly, since these issues and challenges were described as related to

lacking or insufficient actions - these prevailing issues were also described as seemingly

possible to counteract. The most common examples of such suggested actions are described in

the section below.

5.3 Implementation approach and success factors

Whereas comprehension of issues and challenges are necessary to identify opportunities for

improvement, the main objective was to investigate eventual suggested solutions and aspects

facilitating RPA implementation, and how these insights relate to previous scholarly models.

The content of these findings has been divided into two main topics and sections below.

5.3.1 Success factors and aspects facilitating implementation

A fundamental objective of this study has been to identify aspects facilitating RPA

implementation and solutions to common issues. As described in previous sections, we found

that the contextual preconditions, as described by respondents, likely facilitated the RPA

implementation with regards to; having clearly defined IT roles (outsourced), established

contact with a consulting firm knowledgeable within RPA, recently had reviewed their IT

infrastructure and mapped their processes, and that an urgent need for increased capacity

created an evident business case for RPA. Whilst the IT structure and knowledge likely

mitigated potential technical issues, as concluded in previous sections, the careful mapping of

processes is in line with Santos et al.´s (2019) emphasis on the importance of understanding

and choosing the right processes for automation in order to facilitate implementation.

Moreover, the precondition of having an urgent need for RPA and clear business cases is in

line with Herm et al.´s (2020) urge for creation of- and recurring reviews of a business case for

RPA, to facilitate buy-in and implementation. These pronounced preconditions are hence to be

seen as facilitating aspects according to both respondents and theory.

In accordance with the Microsoft-produced Digital Directions´- report´s emphasis on the level

of RPA knowledge is often underestimated, the empirical data collection showed the

respondents considered RPA knowledge as an important facilitating aspect and that the

complexity of RPA implementation should not be underestimated (EY, 2020). Although this

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insight may seem obvious, it can also be connected to the respondent’s emphasis on the

importance of educating management and employees within the organization - as well as

communicating both aspects related to; RPA knowledge to increase learning, benefits and

logics behind the implementation for change management purposes, and generally avoiding

misinformed employees potentially disturbing the RPA work. These issues should be avoided

for several reasons as described in the previous section. In general, and as also mentioned in

the above section on issues and challenges, many of the important activities to facilitate RPA

implementation mentioned in the empirical findings - revolved around the human aspects,

leadership and change management. Whilst change management is described in Herm et al.

(2020), and the importance of leadership was mentioned by (Rutaganda et al., 2017), the

attention to both RPA education, communication and change management was not highlighted

as crucial success factors by the literature reviewed in this research. These empirical findings

indicate a potential need for additional attention in RPA implementation theory, or at least

further research to validate or discard such need.

Moreover, support processes were also recurring in the empirical findings regarding facilitating

aspects. Whilst the case company had not established an internal RPA-team to conserve

knowledge and expedite future projects - most respondents claimed such internal constellations

would be highly desirable to facilitate implementations. This is in line with Herm et al.´s (2020)

suggested usage of a CoE and emphasis to support processes. Moreover, many of the

facilitating aspects identified seemingly match Markus et al.´s (2000) “actions suggested to

improve implementation success” for ERP systems, including; “doing a much better job of end-

user training”, having “[...] plans for long- term maintenance and migration”, and; “not

disbanding the project team when the project goes live, but instead staffing a competence center

for managing future evolution and learning” (Markus et al., 2000). Although no efforts were

made to further investigate guidelines for ERP implementation and eventual coherence to our

findings, these similarities may indicate that connections between different areas of IT

implementation- and RPA implementation literature may be useful.

Conclusively, as mentioned in previous sections - the empirical and theoretical findings on

issues, challenges are in many ways interconnected with the facilitating aspects mentioned

herein. As will be described below, the connections between the identified issues, challenges

and facilitating aspects, can very much be reconnected to the overall approach and structure of

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how an implementation model may be constructed to provide guidelines on how to facilitate

RPA implementation.

5.3.2 RPA implementation project approach

In an attempt to investigate how RPA implementation should be managed according to theory

and empirical experiences, the structure of the data collection and analysis of this research has

revolved around scholarly suggested phases and models for RPA implementation. A

fundamental conclusion is that the perceived, empirical success factors (facilitating aspects) are

interconnected with the structure and activities under the different phases of the implementation

project. Both literature (e.g. Herm et al., 2020; Santos et al., 2019) and respondents highlighted

that a structured approach for RPA implementation outlining phases and activities is valuable

for implementation projects. This insight is also in line with the scholarly suggested need for

additional research within the field of implementation approaches and models.

Furthermore, a key finding of this research is that the phases and key activities described by

implementation literature, and our main theoretical implementation framework by Herm et al.

(2020), very much correspond with the activities mentioned by respondents both in terms of

actions, considerations and respective order of each type of action.

Additional takeaways also exist in relation to the respondents' descriptions of the extent,

relative length and importance of what Herm et al. (2020) call the initialization phase - which

was seemingly much greater than Herm et al. (2020) indicate, compared to the other activities

included in the model. We also conclude this focus and emphasis corresponds with the success-

factors identified (described in previous section) related to; carefully understanding-, choosing-

and mapping processes to automate; creating and evaluating a business case, and; focusing on

communication and creating knowledge around the benefits and requirements of RPA from

early on. The importance of the initialization and preparatory work with implementation

projects should hence be recognized.

Compared to what is indicated in Herm et al.´s (2020) visualization at first glance, the empirical

data collected suggest ‘process selection’ is a comprehensive activity including discussing-,

understanding-, mapping-, and finally deciding on a suitable process. Whilst the same activities

are reflected in Herm et al.´s (2020) description, we suggest more emphasis is made to the

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importance of these activities, and that the visualization of ‘process selection’ is increased in

Herm et al.´s (2020) model. Furthermore, in contrast to the respondents suggestions on that

(what Herm et al., 2020 would describe as) ‘software selection’ is not to be seen as difficult,

and that one respondent suggested this selection can take place later on in the project - we

actually found the case company used the ‘software selection’ process as part of their, what

Herm et al. (2020) would call, ‘screening’ of RPA in the first place, talking to vendors to

understand the technology and what it may contribute with. Consequently, no unambiguous

suggestion on how existing models could be adopted with regards to software selection can be

derived from our research.

Furthermore, based on the empirical data collected, we found that the Herm et al.´s (2020)

‘proof of concept’- and ‘roll-out’ phases (described as relatively un-problematic by the

respondents) was performed in an efficient and structured way based on previous knowledge.

Especially the ‘request for change’-process included a wide variety of smaller controls,

managed in a structured manner based on routines and existing governance. Whilst Herm et

al.´s (2020) model also visualizes the ‘roll-out’ phase as concise, our interpretation is that the

outsourced IT and consultant assistance may have facilitated roll-out with the help of know-

how and consultancy processes.

An important conclusion is also that the post-roll-out and scaling activities, described as the

last phase of Herm et al.´s (2020) implementation model, received relatively much attention in

the empirical data collected - compared to what the model indicated. The case company´s own

visualization of the project included a management and maintenance phase, and respondents

described that; continuous monitoring and reconfigurations of the robot, management of RPA

knowledge and initiatives within the organization (closely related to support processes) and

especially scaling the usage of RPA in the organization, as also mentioned by Herm et al.

(2020), was one of the most challenging aspects.

Provided the focus on phases and timeline-oriented structure of Herm et al.´s (2020)

implementation model, it is not difficult to see why ‘support-processes’ received restricted

amounts of attention. What is interesting, however, is the vast attention and focus the

respondents made to questions regarding humans and change management. Although briefly

mentioned by scholars including Leslie Willcocks (cited in McKinsey & Company, 2016) and

also included but not explained nor commented in Herm et al.´s (2020) model, the respondents'

46

great emphasis on the importance of human questions and change management (based on the

issues and challenges and success-factors described in this report) is not equivalently

represented in RPA implementation literature and guidelines. Establishing a COE, however, as

mentioned in Herm et al.´s (2020) model, was also suggested by respondents, especially to

facilitate future projects and scale-up. As suggested by one of the consultants and as can be

concluded based on the recurring suggestions on ensuring- and percerving RPA knowledge -

the mere actions of collecting and using RPA knowledge may be the central actions needed,

rather than creating an CoE.

Although the activities suggested by Herm et al.´s (2020) implementation framework was

similarly described by respondents, and that the chronological order of these activities to a large

extent matched the order of the model - an important finding was that the project included

iterations and simultaneous activities, moving back and forth between activities. Whilst Herm

et al. (2020) primarily pronounce the importance of iterations, in describing a ‘continous cycle’

is needed for CoE and support processes, the empirical findings suggest that especially the early

activities and ‘proof of concept’ likely need to involve iterations. Implementation models would

hence potentially reflect the true project process if such iterations and complexity were

illustrated.

Conclusively, the empirical findings show extensive similarities between Herm et al.´s (2020)

‘Consolidated framework for RPA implementation projects’ and the project approach used and

suggested by the respondents, but also some evident discrepancies. Whilst Herm et al. (2020)

framework seemingly reflect the overall approach and suggested actions to be taken during

RPA implementation, the empirical findings also indicate that development of the framework

to further emphasize the importance of; change management, communication and education,

and the relative importance-, and demands of both the initialization- and post-implementation

phase. The differences in emphasis throughout the project approach is seemingly in line with

the empirical discrepancies compared to theory identified in the previous issues and challenges

section. The conclusive recommendations made in relation to the project approach were hence

built on what was perceived as challenging as well as facilitating for the RPA implementation.

The research approach of this case study did not allow for isolation of eventual impacts from

preconditions and contextual aspects, however as described in previous sections, several

aspects of the case company's situation and actions previous to the project may have facilitated

47

implementation in relation to especially the technical implementation questions. Other banking

and financial services companies hence need to take these eventual effects into consideration.

The overall highlighted importance of a structured approach, and the usefulness of visualized

project steps, also supports the relevance of developing implementation models and reviewing

the included activities to ensure relevant questions are considered. This finding also seems to

support the relevance of this study and the suggested need to further develop guidelines for

implementation.

48

6. Conclusions

Many practitioners seem to struggle with RPA implementation and researchers have urged for

further research within the RPA implementation field in general, as well as for application of

existing implementation framework to new empirical cases, in particular. The purpose of this

study was to investigate issues and challenges associated with RPA implementation.

Furthermore, based on existing implementation frameworks and theory, the aim was to explore

how projects may be managed in order to facilitate implementation. Semi-structured interviews

were conducted with employees and consultants having recently implemented a RPA solution

in the banking and financial services industry, documents describing the implementation

approach was reviewed, and the empirical findings were substantiated through theoretical

comparisons to answer the research questions:

1. What are the issues and challenges for RPA implementation projects?

2. How should an RPA project be managed to facilitate implementation?

6.1 Issues and challenges for RPA implementation projects

Whilst many potential issues and challenges were identified in the data collection and analysis,

some were especially evident and in line with theory, including; challenges with choosing the

right processes to automate, post-implementation and scale up issues - although the latter was

not equivalently emphasized in literature. Furthermore, the analysis concluded that almost all

issues and challenges emphasized by respondents relate to the challenge of achieving adequate;

(1) change management, (2) spreading of information, and (3) education- and engagement of

management and employees around RPA and the solution. The case company generally did not

experience several of the common issues and challenges mentioned in theory, such as technical

issues and cost-acceptance. Based on commonly described success-factors, the analysis

concluded much of the mitigated issues and challenges may be dependent on contextual

preconditions and actions taken by the case company before the implementation project. The

relevance of the common issues and challenges described in theory may hence not be ruled for

other contexts.

The analysis also concluded that most of the key issues and challenges identified in the case

study were directly linked to suggestions on how to facilitate implementation - indicating that

issues and challenges may be mitigated, as described in the section below.

49

6.2 Managing an RPA implementation project

To answer the question on how to manage RPA implementation projects, both (1) the

implementation structure and approach, and (2) particular success factors and facilitating

actions suggested by the respondents were considered and compared with existing theory. The

analysis concluded that the success-factors and implementation approach suggested by the

collected data, is profoundly interconnected. The most important facilitating aspects and

success-factors were identified as; building RPA knowledge and focusing on educating

management and employees within the organization, communicating (RPA knowledge,

benefits and logics behind the implementation for change management purposes), and carefully

choosing the right process to automate. The value of support processes including management

support, change management and establishing internal groups for RPA scale-up and

stewardship, was also emphasized.

The analysis also concluded that the key facilitating aspects emphasized by respondents

seemingly match scholarly contributions related to how one may improve implementation

success for ERP systems. Although no efforts were made to further investigate guidelines for

ERP implementation and eventual coherence to our findings, these similarities may indicate

that connections between different areas of IT implementation literature and RPA

implementation may be useful.

Furthermore, the analysis concluded that certain contextual preconditions and actions taken

before the project may have facilitated the RPA implementation for the case company, through

having; clearly defined IT roles (outsourced), an established contact with a consulting firm

knowledgeable within RPA, recently had reviewed their IT infrastructure and mapped their

processes, and that an urgent need for increased capacity created an evident business case for

RPA. Moreover, a structured approach with visualized project steps was also emphasized as

important to facilitate implementation. This conclusion also supports the relevance of

reviewing implementation models and the included activities to ensure relevant questions are

considered. This finding also seems to support the relevance of this study and the suggested

need to further develop guidelines for implementation.

One of the most important conclusions of this study is that Herm et al.´s (2020) ‘Consolidated

framework for RPA implementation projects’ provided a seemingly accurate breakdown of

50

most necessary actions and considerations also suggested by respondents. The empirical

findings however also indicate that implementation frameworks such as Herm et al. (2020) may

need increased attention to; change management, communication and education, and the

relative importance-, and demands of both the initialization- and post-implementation phase.

6.3. Limitations and suggestions for future research

This study has concluded several interesting differences and similarities between theory and

the empirical findings of the case company. An analysis of why differences may be evident was

presented, and new potential considerations for implementation frameworks was discussed.

The authors would like to emphasize several limitations of the study.

The fact that the study only examined one company, in the banking and financial industry, and

with certain pre-condition, may have had a negative impact on the generalizability and hence

usefulness of this study. Moreover, provided the focus on describing the entire process of an

implementation and hence using a limited number of centrally involved respondents along with

triangulation through reviewing project documentation - the study did not include e.g. end-

users or other case company stakeholders to describe challenges from different perspectives

than the project team(s).

Whilst the research questions and overall attempts to describe how projects should be

conducted are normative, the researchers have tried to highlight that contextual individual

differences likely have vast effects on how implementation should be conducted.

Generalization of the results presented in this study is hence limited to especially the banking

and financial services industry and processes surrounding lending. Provided the extensive

harmony between the universal and context independent implementation framework analyzed

and the empirical findings - many of the conclusions may however be interesting for

practitioners outside the targeted industry of this study.

In order to increase reliability and generalizability, the researchers suggest future studies within

the field of RPA implementation should include multiple companies, investigate and compare

additional contexts-, industries-, and types of companies with and without assistance from

consulting firms. Provided the assumed effects of contextual preconditions described in this

study, attempts to isolate and investigate issues and facilitating aspects in different contexts

51

would likely increase the practicality of RPA implementation theory. In addition, the

researchers also suggest future research to be cross-disciplinary and include increased focus

on; implementation theory related to other software solutions, as well as on organizational-,

and change management questions, to develop RPA implementation models further.

52

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Appendices

Appendix 1

Interview guide

Checklist before the interview:

- Describe our research, the purpose and the themes we will cover

- Define the case and limitations of scope (describe the specific implementation project

we are referring to and that the respondent may answer the questions based on

previous experiences if that is stated)

- Describe the practical aspects with the interview

- Describe ethical considerations (including their anonymity)

- Ask for permission to record and describe how the recordings will be used

1. Interviewee profile and role in project

1.1 Experience and role definition

- Can you tell us about your professional role? (e.g. tasks, special field of knowledge,

years of experience etc.)

- Have you worked with RPA technology or implementation before this project? If yes:

for how long/ approximately with how many projects?

1.2. Role and responsibility in this specific project

- What was your role in the RPA implementation project

- What responsibilities did you have? What tasks did you perform?

- During what timespan were you involved?

2. The project

2.1 Initialization

- Tell us about the initiation of the project? What was the background of the project?

- Was there a business case? If yes;

- What was the reasoning and;

- When was it formulated?

- Was there a connection between the RPA implementation and your business

strategy? If yes, how?

- What were the first steps/ actions your organization took? Preparations?

- Tell us about the process of choosing an RPA vendor/ software?

- Tell us about the choice of which process to automate?

- Could you conclude certain criteria for processes suitable? If yes; which

criteria?

2.2 Implementation

- Did you work with testing and verifying the functionality of the robot? If so,

how?

- By this time of the project - had the business case changed/ had your

perceptions of usefulness changed?

- Tell us about the roll-out phase.

- Did you have any specific strategy?

- Did you use some sort of pilot-roll-out or certain processes?

58

2.3 Completion of the project and post roll-out activities

- After the implementation of the software was completed and the process(es) were

automated… Can you describe any actions taken “post-implementation”?

- Were there any activities aimed at scaling up the robot-technology usage within the

organization?

Eventual support processes

[Over the entire project and afterwards]

- Can you describe any support processes, functions, activities etc. that

has been practiced during/ after the project?

- E.g. change management, governance, IT integration etc.

- Did the organization establish a “center of excellence” or some sort of

group to support monitoring and maintenance of the robots?

- Has any measures been taken to capture and maintain RPA

knowledge within the organization?

- How would you describe the level of “management's support” for the

project? [From the board, executives, and downward]

3. Issues and general approaches

- What were the most challenging/ problematic aspects of the RPA

implementation project?

- How were these challenges/ problems solved?

- It appears that upwards of 50% fail with their RPA implementation. What do you

think may be the reason for this, based on your own experience?

Specific examples of common issues [to be asked if the respondent does not

mention the specific problem(s) during the interview]

- Was there any issues with targeting the right process/ selecting the right

scope for automation?

- If yes, how did you overcome these issues?

- Was there any issues regarding IT infrastructure?

- If yes, how did you overcome these issues?

- Was there any issues regarding the skills needed for the implementation

project?

- If yes, how did you overcome these issues?

4. Keys to success

[Reconnect back to the different phases and activities mentioned by the respondent and ask

what they did right. Do they consider any actions as particularly important?]

- What aspects of the RPA implementation project and process do you consider

as especially important for the success of the project?

- Which part of the implementation process did you consider to be most

decisive for the final result?

- In addition to this part, were there other “decisive” factors that

contributed to the outcome of the implementation?

- Was there any part in the implementation process you should have done

differently

- If this is the case, how?

59

5. Other questions

- With regards to RPA implementation success… is there anything you feel we have

failed to ask about?

- Any preconceptions you had before the project and things you felt you

learned?

[Finish the interview, thank the respondent for their participation, provide information on

publication and ask if they would like to take part of the thesis when published]

60

Appendix 2

Suggested evaluation criteria for process selection

Business impact vs. complexity-matrix for process evaluation

(Adapted based on descriptions from respondent CO1)

Evaluation factors for choosing what process to automate

Adapted from the case company´s retrospective lessons learnt PowerPoint presentation.

61

Appendix 3

Activities included in the respective phases of the case company´s project overview

Phase Activities

Evaluation As described by respondent CL1 the evaluation phase is orientating in

nature, aimed at learning about RPA and finding arguments for and

against using RPA or instead e.g. hiring additional advisors and

generally evaluating the process to automate and whether RPA is

suitable.

Configuration The consequent configuration phase consisted of mapping the process to

be automated in a Process Definition Document (PDD), developing

scenarios for the robot to process and mapping all the detailed steps the

robot need to perform through workshops with different internal parties

and interviewing employees normally performing the tasks. The PDD

was then reviewed and accepted by relevant decision makers before the

Solution Design Document (SDD) was created (mostly by the consulting

company), followed by acceptance tests (AT) within the organization,

observing the robots work, ensuring the robot is conducting the processes

correctly throughout all steps.

Implementation In the implementation phase, a Request For Change has to be approved

from an IT perspective (following the same decision-process as all other

IT changes in the organization, e.g. ensuring that the robot does not

negatively affect other systems, deciding what server should be used, and

safeguarding GDPR compliance - before finally deploying the robot.

Management

and

Maintenance

The final phase of the visualized project plan overview is management

and maintenance of the RPA involving reconfiguring and developing the

robot in accordance with changes in e.g. processes (as described in

previous sections of this chapter).

(The summary is based on reviewing visualizations received from the case company and on

descriptions from respondents)