Using AI to Augment RPA

USING AI TO AUGMENT RPA:
A CONCEPTUAL FRAMEWORK
Research Paper
Inge van de Weerd, Utrecht University, Utrecht, The Netherlands, i.vandeweerd@uu.nl
Bart Nieuwenhuijs, Utrecht University, Utrecht, The Netherlands, bart@nieuwen.nl
Floris Bex, Utrecht University, Utrecht, The Netherlands & Tilburg University, Tilburg, The
Netherlands, f.j.bex@uu.nl
Iris Beerepoot, Utrecht University, Utrecht, The Netherlands, i.m.beerepoot@uu.nl

Abstract
Robotic Process Automation (RPA) has seen significant uptake in practice over the last years due to its
ability to cut costs and rapidly connect legacy applications. However, organizations struggle to apply
RPA to tasks with higher complexity. The use of Artificial Intelligence (AI) capabilities to augment RPA
promises to overcome this limitation. Nevertheless, little detail is available on how these technologies
can complement each other. Drawing on the theoretical links of the Task-Technology Fit model, we
propose a set of AI capabilities that fit specific RPA tasks. Based on these fits, we propose a typology ofuse cases that AI enable in RPA. These results are derived from a literature study, nine case studies,and 15 expert interviews and validated through a case simulation and expert evaluation.
Keywords: Robotic Process Automation, Artificial intelligence, Intelligent automation, Tasktechnology fit.

1

Introduction

Robotic Process Automation (RPA) is an automation tool that operates on a computer system’s userinterface like a human would (Aalst et al., 2018). A user of RPA can configure one or more scripts,
enabling RPA bots to mimic or emulate certain structured and repetitive tasks (Stoudt-Hansen et al.,2019). It functions on top of existing applications, which means there is no need to develop or replacelegacy systems (Lacity and Willcocks, 2015). Moreover, RPA can be configured without the need foradvanced programming skills (Lacity and Willcocks, 2015).
RPA has seen significant uptake in practice over the last years due to its ability to cut costs and rapidly
connect legacy applications (Aalst et al., 2018). Even though RPA has shown to bring various benefits,
many organizations struggle to apply RPA to more complex processes. Currently, RPA can only follow
simple, logical rule-based processes and cannot handle unstructured data. However, both academia andindustry see more potential in RPA by enhancing it with Artificial Intelligence (AI). Van der Aalst et al.
(2018), for example, posit that for “more widespread adoption, RPA needs to become smarter”, and theuse of AI and machine learning might make RPA suitable for “more complex and less defined tasks”.
Also, in industry we see a broad interest in augmenting RPA with AI (e.g. Ray et al., 2019).
While multiple studies suggest that augmenting RPA with AI is the next step to allow RPA to support awide variety of tasks (Anagnoste, 2017; Aalst et al., 2018; Ivančić, Suša Vugec and Bosilj Vukšić,
2019), little detail is available on how these technologies can complement each other. Therefore,

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multiple authors call for future research in this area (Hofmann, Samp and Urbach, 2019; Santos et al.,2019; Enríquez et al., 2020; Syed et al., 2020). Also, industry calls for clarity. Gartner states that “there
is a lack of guidance helping organizations to assemble RPA with other tools, causing theseorganizations to miss out on strategic business values” (Ray et al., 2019). Moreover, a survey from
Deloitte showed that executives consider the identification of appropriate use cases as the mostsignificant barrier for successful RPA and AI implementations (Watson et al., 2019).
The goal of this study is to capture the potential of RPA augmented by AI and provide guidance inapplying these technologies in an organizational context. To support this goal, we will draw upon theTask-Technology Fit (TTF) model from Goodhue and Thompson (1995), which enables us to match thefunctional capabilities of AI to characteristics of organizational tasks that can be automated by RPA.
Based on literature, case studies, and expert interviews, we propose a conceptual framework that
contains set of AI capabilities linked to specific tasks that might be automated with RPA.
In the following section, we will first outline our research method. In Section 3, we present thetheoretical background (including the results of our systematic literature review). This section results inthe proposal of a conceptual framework for augmenting RPA with AI. Section 4 presents five tasktechnology fits that we could identify from our analysis of the literature, cases and expert interviews. In
Section 5, we present the results of a focus group in which we evaluated these fits. Finally, in Section 6,
we present our conclusion and discussion.

2

Research method

In this study, we follow the Design Science approach as described by Peffers et al. (2012). Our study
was carried out from November 2019 to July 2020 and the data collection took place from February2020 to May 2020. The details of the study are documented in Nieuwenhuijs (2020).
In phase 1, we studied the literature and talked to RPA and AI experts to identify the problem andmotivate our research. The result of this phase is presented in the introduction. Then, in phase 2, we
conducted a systematic literature review to design objectives of a solution. Specifically, this meant we
identified and defined relevant characteristics of tasks that might be automated with RPA, as well as AI
capabilities. Subsequently, in phase 3, we designed and developed our conceptual framework. We
carried out case studies of existing RPA and AI projects and interviewed experts. Phases 4 and 5
comprise the demonstration and evaluation of the framework. We conducted a case simulation andexpert evaluation in two focus groups. The goal was to evaluate whether the framework can help users
in recognizing opportunities for RPA and AI. Finally, in the communication phase, the results have beenpresented to professionals and will be published for a scientific audience.

2.1

Defining objectives for a solution

In phase 2 of our study, we conducted a systematic literature review following Okoli (2015). With thisreview we aimed to answer several questions, such as ‘What AI capabilities used within RPA?’ and‘What tasks are being automated by RPA and AI?’ Figure 1 shows the process we followed and the
choices that were made. We selected 51 sources from which we identified and defined relevant task
characteristics (for RPA) and AI capabilities. Based on this, we established a first version of our solution:a conceptual framework that links tasks characteristics and AI capabilities.

Figure 1.

Systematic literature review process

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2.2

Developing the conceptual framework

In the third phase, we developed the framework using methodological triangulation (Kaplan andMaxwell, 2005). This approach allowed us to study RPA and AI in a realistic setting by performing a
multiple-case study (Yin, 2017) on deployments of RPA augmented by AI. However, considering the
combination of RPA and AI is a new phenomenon, it cannot be assumed that observing all scenarios ispossible. Therefore, as a second research method, we use expert interviews (Bogner and Menz, 2009).
The main advantage of this type of interview is that it is possible to formulate conceptualization on thestudied phenomenon without the need to observe them all. In the following sections, we will outline
how we carried out the case studies and expert interviews.
2.2.1

Multiple-case study

The case study is characterized as a multiple embedded case design (Yin, 2017). Multiple cases increase
generalizability and produce more insight (Runeson and Höst, 2008). Moreover, the embedded design
allows us to compare the cases using the TTF constructs consistently. As part of the sampling approach,
we aimed for maximum variation sampling by including different industries and AI applications. As
criterion sampling, all AI technologies need to conform to the definition of this research, and RPA needs
to be applied to (parts) of tasks. To recruit potential study participants, we used the network of a large
consultancy firm. Evidence in the cases is collected by performing systematizing expert interviews inwhich we acquire process knowledge (Bogner and Menz, 2009) and documentation (e.g., presentation
slides). Table 1 provides an overview of the context information on the type of business process, sector,
location, and the operational RPA bots on a local level1. The size of the organizations ranges from 50.000
to 500.000 fte. For each case, we interviewed one informant and studied documentation such as processdescriptions. We recorded and transcribed the interviews. Based on the documentation and interviews,
we drew process models for each case that was reviewed by the case informant.
#
1
2
3
4
5
6
7
8
9

Business process
Insurance claim
ReportingKnow your customer
Document extraction
Password reset
Invoice processing
Invoice processing
Invoice processing
Procurement

Table 1.
2.2.2

SectorInsurance
RetailBanking
Audit
Government
Logistics
Government
Consumer goods
Construction

Location
Italy
Netherlands
FranceNetherlands
Netherlands
UK
Netherlands
Netherlands
USA

RPA bots
10-20
80-90
70-80
10-20
30-40
50-60
30-40
0-10
0-10

Informant
External consultant
External consultant
Internal consultant
Internal manager
External project leader
External consultant
External consultant
External project leader
Software vendor founder

Case overview
Expert interviews

In addition to the multiple-case study, we carried out 16 expert interviews. We interviewed three
categories of experts: (1) service providers that implement RPA and AI solution, (2) vendors thatdevelop RPA solutions that include AI capabilities, (3) end-users of RPA within the industry. As
criterion sampling, the interviewees had at least one year experience with RPA, experience withaugmenting RPA with AI, and were involved in the delivery of RPA and AI deployments. Table 2
provides an overview of the interviewees, their role, and primary industry. The interviews took place as
1

The number of operational RPA bots is presented on the local level. For example, if the company is active worldwide but thecase is a deployment in Italy, we only present the number of RPA bots in Italy

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semi-structured interviews and focused on obtaining interpretative knowledge (e.g., orientations, rules,
points of view, and interpretations) (Bogner and Menz, 2009). The topics covered in the interviews
included the tasks that were automated as well as their characteristics (e.g., diversity of input andvariability of output), the type of automation (RPA and/or AI), the automation level, and the perceived
performance impact.
#
1

Type
Role
Service provider Sr. consultant

2

Service provider

3
4

Service provider
VendorConsumer 10
End usergoods
Manager Tax
Tax
11 Service provider
CTO EMEA Software 12
End user

5

Service provider

Director Tax

6

Vendor7Service provider

8

Invoiceprocessing

Table 2.
2.2.3

Industry
Public#9TypeService provider

Sr. manager

Tax

13

VendorTechnical
consultant
Freelancer

Software 14

VendorSoftware 15

End user

Director

Financial 16
services

End user

Role
Manager
Head robotics
Manager
Robotics technicallead
Global VP
automation
Digital workforce
evangelist
Information
manager IT
Innovation manager automation

Industry
Financialservices
Assetmanagement
Public sector
Banking
Software
SoftwareGovernment
Insurance

Overview of interviewees
Analysing the data

The goal of our data analysis was to identify AI technology capabilities and task characteristics related
to RPA and the fit between these. We coded the transcripts of the case interviews, the documentation,and the transcripts of the expert interviews in NVivo. The first level of our coding scheme was based on
the main constructs TTF model, e.g. technology capabilities, Task-Technology Fit, and task
characteristics. The second level consisted of technology capabilities and task characteristics that
resulted from our literature review (see Section 3). For example, in case 1 we found that the RPA bot
had to extract signatures from documents with a high variety in structure. In this deployment, the AI
component had to classify whether there is a signature on some of the documents. This was coded as a
fit between the AI technology capability ‘Search’ (can extract structured data from unstructured
documents) and the task characteristic ‘Variety’ (of input documents). This task characteristic isexpressed in a complexity dimension (in this case ‘Variety’) related to an input component (‘Input’). An
elaborate overview of our coding scheme can be found in Nieuwenhuijs (2020).

2.3

Demonstrating and evaluating the framework

Evaluation of artifacts in Design Science is a central and critical part of Design Science research(Hevner, March and Park, 2004). We conducted two confirmatory focus groups (Tremblay, Hevner andBerndt, 2010), consisting of RPA experts; practitioners that all had experience with RPA projects.
In the first phase of the, participants applied the framework on a case simulation (Sonnenberg and vom
Brocke, 2012) in order to find out whether the framework helps users in detecting automation
opportunities for RPA and AI. The participants applied the framework on a loan application process thatwas published by Dumas et al. (2013). The first two authors of this paper also identified automation
opportunities and compared our results with the results of the participants. Secondly, the participants
performed an expert evaluation on the framework to find out how the experts perceive the framework.

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The expert evaluation took place as an interactive questionnaire, in which participants first voted on astatement after which they saw the results from the other participants. Considering that participants
voted without seeing other answers, the effect of social pressure or differences within the group was
limited (van Zolingen and Klaassen, 2003). We considered the four evaluation criteria for models fromMarch and Smith (1995): completeness, fidelity with real-world phenomena, internal consistency, andlevel of detail. Since practitioners would be using the framework, we added understandability –practitioners need to be able to understand it before they can use it, and usability – practitioners need toable to use the model in their profession. Based on these criteria, we constructed a questionnaire
containing two statements per criterion.
Both case simulation and expert evaluation are considered artificial forms of evaluation. However, since
the evaluation was carried out by real users who also validated the realism of the case, we strengthened
the real task (Sun and Kantor, 2006).

3

Theoretical backgroundIn this section, we explain the theoretical foundations of our conceptual framework for RPA and AI. We
elaborate on the existing work on RPA and AI, explain the Task-Technology Fit model of Goodhue andThompson (1995) that forms the foundation of our framework, and we identify the task complexity
dimensions and AI capabilities that we use as ingredients for the framework. The task complexity
dimensions and AI capabilities are results of our systematic literature review mentioned in Section 2.1.

3.1

Robotic process automation and Artificial Intelligence

Artificial Intelligence (AI) is, broadly speaking, concerned with making artificial systems behave
intelligently. One way to define an AI system is according to what it can do: perceiving and acting
rationally in complex environments or, as Burgess (2018) puts it, capturing information and using it tofind out what is happening and why. Another way to define AI is according to the specific techniques
used. Currently, data-driven (deep) machine learning is most often mentioned, although there are manyother AI techniques (e.g., knowledge graphs, search algorithms etc.). For our definition of AI, we focus
on ’what it can do’, adopting the AI capabilities from Burgess (2018) (see Section 3.4). We further limit
our definition of AI to machine learning techniques. This allows us to clearly delineate AI (machinelearning) and RPA (rule-based techniques) and is in line with the currently popular view of AI, which
many of the case study informants and experts we interviewed subscribe to.
Augmenting RPA with AI is still in its early development, with only 18% of organizations having
experimented with it (Jędrzejka, 2019). Authors emphasize the lack of understanding of how AI can add
value to RPA (Gotthardt et al., 2019; Kirchmer and Franz, 2019) and stress that this should be studied
from an implementation perspective (Gotthardt et al., 2019; Syed et al., 2020).
According to literature, the predominant use of AI to augment RPA is converting unstructured or semistructured data into structured data (Burgess, 2018; Flechsig, Lohmer and Lasch, 2019; Kirchmer andFranz, 2019; Scheer, 2019). Moreover, task complexity increases as data and rules become lessstructured or defined, the number of steps increases, and the amount and variety of data increases (Lacity
and Willcocks, 2018). AI can enable RPA to perform such more complex, less clearly defined andchanging tasks (Ansari et al., 2019; Teli and Prasad, 2019; Kirchmer and Franz, 2019). Finally, many
authors propose a combination with chatbots to bridge the gap between RPA (i.e., back-end automation)and customer service (i.e., front-end) (Taulli, 2019b; Syed et al., 2020).

3.2

Task-Technology Fit

An exploration of the applicability of specific technologies on specific tasks should be based oncomprehensive insights into the overarching conditions that affect the performance impact of atechnology. In this study, we draw on the Task-Technology Fit (TTF) model (Goodhue and Thompson,

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1995). The TTF model focuses on matching the functionality of a technology to the requirements of atask (Dishaw and Strong, 1999). As such, it perfectly fits our goal of matching the capabilities of AI
with the characteristics tasks that might be automated with RPA. We use the fit-as-deviation approach(Junglas, Abraham and Watson, 2008), which means that we measure the TTF indirectly by measuring
the task characteristics and technology characteristics separately (Teo and Men, 2008).
In order to be able to measure the fit between RPA and AI, we first need to establish the sets of task
characteristics (related to RPA tasks) and technology characteristics (related to AI capabilities). We will
do so in the next two sub sections. Two other constructs proposed by Goodhue and Thompson are
utilization and performance impact. Utilization refers to the behaviour of employing the technology incompleting tasks (Goodhue and Thompson, 1995; Davis et al., 1989) and usually measured by constructs
such as frequency of use. We particularly look at the level of automation that a reached by a certain fit,
so we call this construct automation level. We use two levels to indicate how much assistance is provided
by an RPA bot: human in the loop, where humans validate all data points resulting from the AI
augmented task and human for exceptions, where humans are only involved when the AI augmented
task results in an exception. Finally, we define performance impact as a mix of improved efficiency,
improved effectiveness, higher quality, etc.

3.3

Task complexity

Goodhue and Thompson (1995) do not prescribe specific task characteristics that should be used tomeasure TTF. Instead, they emphasize that this depends on the study at hand. The literature reported in
the previous section anticipated that augmenting RPA with AI will allow for the support of more
complex tasks. However, since task complexity is an umbrella term, we need to define it. Several highly
cited studies are published on task complexity—e.g., Campbell (Campbell, 1988) and Wood (1987). Liu
and Li (2012) reviewed and summarized this work and proposed a comprehensive framework on taskcomplexity which we use as basis for our task characteristics.
Liu and Li (2012) distinguish multiple elements that make tasks complex: 1) task components are
inherent parts of a task: input, process, output, and user interface; 2) complexity contributory factors(CCF) are factors or indicators that externally reflect the task complexity level; and 3) complexity
dimensions describe “the interior structure of task complexity” and are “composed of several related
CCFs” (Liu and Li, 2012). To clarify the difference between a complexity contributory factor and acomplexity dimension, we provide an example. Consider, for example, the complexity dimension ‘size’.
This dimension could be measured by several complexity contributory factors, such as ‘number of steps
used in the process, and ‘quantity of output results’.
Table 3 shows eight complexity dimensions Liu and Li (2012) distinguish. We drop two dimensions
from the original framework: Incongruity, which relates to visualization that is a mismatch to a task(e.g., text when tabular is desired), and Action complexity, which focuses on inherent cognitive (e.g.,
IQ and memory) and physical requirements (e.g., strength). Both are irrelevant for automation.
Dimension
Ambiguity
Size
Variety
Variability
Novelty
RelationshipTemporal demand
Unreliability

Table 3.

Explanation
Degree of clarity, structure, or ambiguity.
The number of inputs, steps, and output.
Number of dissimilar task components.
Unstable characteristics of task components.
Non-routine, random or irregular events.
Non-routine, random or irregular events.
Complexity factors caused by time.
Inaccurate or misleading information.

The eight adopted task complexity dimensions from Liu and Li (2012).

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3.4

AI capabilities

We adopt the framework on AI capabilities from Burgess (2018), as explained in Section 3.1. This
framework takes a holistic perspective, compared to a functional perspective (e.g., Strohmeier andPiazza, 2015), is very detailed, and makes connections to other concepts in AI. Burgess (2018) defines
two current main objectives of AI: 1) capturing information, and 2) finding out what is happening.
The objective capturing information contains the following AI capabilities (Burgess, 2018):
1. Speech Recognition: encoding speech (live or recorded) into words or sentences.
2. Image Recognition: processing and interpreting images (i.e., unstructured data).
3. Search: extracting structured data from unstructured or semi-structured text.
4. Data Analysis/clustering: identifying patterns or clusters in structured data.
The objective finding out what is happening contains the following AI capabilities (Burgess, 2018):
1. Natural Language Understanding: extracting meaning from text in order to act as a translator
between humans and machines.
2. Optimization: reaching a desired goal with a set of possible actions to get there. Characteristics
are that a goal needs to be achieved, a problem needs to be solved, or a plan needs to be made.
3. Prediction: using historical data to match new data to an identified group.

3.5

Conceptual framework

Figure 2 presents our conceptual framework for augmenting RPA with AI, based on the TTF model ofGoodhue and Thompson (1995). It combines the task components and complexity dimensions of Liu
and Li (2012) with the AI capabilities of Burgess (2018) to enable us to determine the fit between an AI
capability and the characteristics of a task that might be automated with RPA. The model iscomplemented with the Automation level, where humans are either in the loop, or only used in
exceptions.

Figure 2.

The intelligent RPA framework: a conceptual framework for augmenting RPA with AI

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4Intelligent RPA framework

Based on our data analysis, we grouped the data in the constructs of the conceptual model developed in
the previous phase. First, we established the fits between an RPA task requirement and the AI technology
capability. Table 4 presents the list of fits, including the supporting literature, cases, and interviews.
Fit

AI technology
capability

RPA task
characteristic

Ambiguity
Size

-

9

Size

Process
Anagnoste (2018)2

2, 3, 6-8, 12, 13

Relationship-22, 4, 12, 16

Novelty--

5, 7, 10, 11

2
3

Speech recognition Ambiguity
SearchVariety4

Natural language
understanding
Natural language
understanding
Natural language
understanding

7

Data analysis &Prediction
Data analysis &Prediction
Data analysis

89Supportinginterviews

1, 2, 5-7, 9, 1113, 15
1, 2, 4-7, 9, 1113, 15
3, 12, 13

Speech recognition Variety

6

Supporting
cases

Input component
Burgess (2018a); Lacity and Willcocks (2015); Schmitz et al. (2019)
Burgess (2018a)
Burgess (2018b); Lacity andWillcocks (2018a); Mohanty andVyas (2018); Schmitz et al. (2019)
Burgess (2018a); Lacity and Willcocks (2015); Schmitz et al. (2019)
Burgess (2018b); Gollapudi (2019)

1

5

Supporting literature

Variety5-

51-4, 6-8

-

9
-

Goal
10 Optimization
11 Optimization
12 Image recognition
13 Image recognition

Table 4.

Size
Relationship--1, 6-8, 15, 16, 18
1, 6-8, 15, 16, 18

Ambiguity
Variability

User Interface
Beerbaum (2020); Taulli (2019)
Beerbaum (2020); Taulli (2019)-

1, 2, 5, 6, 12
1, 4, 5, 6, 12

List of identified fits, accompanied by the supporting literature, cases, and interviews

After establishing the fits, we developed a typology in which we aggregated the fits into categories.
These categories are based on the specific task components (e.g., input or process). In the following
sections, we will discuss each category.

4.1

AI structuring for RPA (fit 1-6)

This use case resembles all types of deployments where AI structures input for RPA, such as extracting
data from documents or identifying damage on an image of a car. Figure 3 shows the following fits:•

Speech Recognition – Variety of input: Words are can be pronounced in different ways. In case
5 a voice bot was implemented to help users resetting their password. This bot asked several
security questions to identify the user. AI was used to recognize the answers given by the users.

•

Speech Recognition – Ambiguity of input: In spoken language, the same pronunciation can havemultiple spellings, e.g., ‘I went to the sea to see my friend’. This means that the context isessential to determine the spelling. The voice bot in case 5 had to deal with spoken language.

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Search – Variety of input: The search capability can extract structured data from unstructured
documents when the variety in document format is too high for RPA to handle. Our informantof case 3 explains: “… while the OCR engine scans the documents and extracts the data,
machine learning models have been designed to read this data, to retain this data, and to reapply
it [in the] right [way] for the next set of documents (…) this is where the learning part of themachine comes into picture.

•

Natural Language Understanding – Variety of input: there are many ways of phrasing andwording the same meaning. This was for example mentioned by interviewee 4: “there is a big
problem space... there are many ways of phrasing and wording the same thing”.

•

Natural Language Understanding – Ambiguity of input: The meaning of text can be ambiguousand might change in another context. For example, interviewee 7 explains: “if you take asentence ... you can give five different definitions to it” (depending on the context).

•

Natural Language Understanding – Size of input: the size or volume of the text also affects thecomplexity to capture meaning from it. According to the informant of case 9: “The documentsthat are input for the task can be 10 to 15 pages. If it was a one-pager, maybe it would have beeneasier... But the fact that it is hidden in the 10 to 15-page document makes it that you need NLP
for that”.

Figure 3.

Use case AI structuring for RPA.

In this type of use case, there are two possible automation levels: either a human is entirely in the loop
and checks all output (cases 4, 6-9), or a human is only handling exceptions (cases 1-3, 5). The
distinguishing factor here is trust in AI within the organization, but also the business critically of theprocess. In performance impact, we see that the performance impact of these types of use cases isincreased productivity (case 1, 2, 3, 5-7, 9) and data quality (case 6, 8).

4.2

AI capturing complex patterns for RPA (fit 7-8)

RPA often falls short, capturing complex patterns. Whereas RPA can only handle a pre-configured listof scenarios with business rules, AI can capture and analyse more complex patterns and predict for the
future. Figure 4 visualizes the following relevant fits:•

Data analysis & Prediction – Size: to the number of factors that are included to capture aphenomenon. This was used in case 2, where AI was used to predict future sales. As theinformant explained "it was a kind of task where you would just plug in a bunch of data that
may or may not be relevant". AI was used to analyse this and predict future sales, which
depended on a large quantity of factors in this deployment.

•

Data analysis & Prediction – Relationship: the relationship or interdependence between therelevant factors that influence a phenomenon. Also in case 2, the sales prediction during theChristmas season had to be handled differently than in other time periods: weather was notimportant (in contrast to other seasons), but the price levels of other vendors were.

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We identified one automation level, namely human in the loop, since a decision is never taken solely by
the AI. Performance impact in this use case relates to an increase in productivity, but also an increase inineffectiveness (case 2). Humans cannot capture complex patterns as AI can; therefore, this use casecreates value that was not available before, since it involved too much data, or was too complicated.

Figure 4.

4.3

Use case AI capturing complex patterns for RPA.

AI increasing RPA robustness (fit 12-13)

Environments that lack an underlying technology connection that allows RPA to extract meta on UI
elements have difficulty supporting RPA (Beerbaum, 2020). These environments are virtualized layers(e.g., Citrix) and most legacy applications (Beerbaum, 2020; Syed et al., 2020). Deploying RPA on theserver-side is often challenging due to political challenges, approval from information security, etc.
Therefore, RPA tools are often deployed on the client-side. The AI increasing RPA robustness use casecan be of high added value here because it makes RPA interact with these types of UI more robustly. It
makes RPA less susceptible to all sorts of changes in the UI. Figure 5 visualizes the following fits:•

Image recognition – ambiguity (of the UI): Ambiguity in the UI means that RPA lacks anunderlying technology connection that allows RPA to analyse metadata on UI elements. Asinterviewee 12 points out: “we definitely need the RPA tool itself to become smarter to know
how to recognize objects on screen”. Image recognition techniques can solve this.

•Image recognition – variability (of the UI): problems for RPA occur when cosmetic changes
happen in the UI. Interviewee 5 explains: “If a button moves from left to right, and if you
programmed your bot correctly, it would solve this automatically. But in a virtual environment(…) the bot cannot always identify the right buttons. Then, you need computer vision.”

Automation levels are not relevant for this use case. Performance impact relates to safeguarding thecontinuity of RPA deployments.

Figure 5.

AI increasing RPA robustness.

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4.4

AI optimizing for RPA (fit 10-11)

In this use case, AI optimizes a complex goal or problem, and RPA executes the relevant activities. Thisuse case has the least evidence to support it, and only the expert interviews show support for this use
case. Figure 6 visualizes the following relevant fits:•

Optimization – size: optimization applies to tasks with several goals at the same time. As
interviewee 6 provides a retail example: “Every store needs to balance its stock levels: too highincreases costs, too low might result in unsatisfied customers. RPA can place the actual order,
and AI optimizes the right volumes to order.”•Optimization – relationship: there has to be some kind of trade-off between two goals. Using
the previous example of interviewee 6: these two goals are in conflict: Every store needs tobalance its stock levels: too high increases costs, too low might result in unsatisfied customers.

Due to a lack of data from our cases and interviews, we did not include the possible automation levels
and the performance impact for this use case.

Figure 6.

4.5

AI optimizing for RPA

Figure 7.

AI capturing exception for RPA

AI capturing exceptions for RPA (fit 9)This use case differs from the other use cases because it is not related to an AI implementation in theprocess itself. Instead, in this use case, AI is used to analyse data on exceptional situations and builds
an RPA bot based on this data. Although this use case is different, we chose to include it because it was
mentioned as a high potential use case by many interview participants. Figure 7 shows the following fit:•

Data analysis – Novelty: Data analysis can be applied to resolve exceptions occurring within abusiness process. Data analysis constructs a process flow and builds an RPA bot based on this.
A human only needs to configure the last parts. Interviewee 7 explains: “You can monitor what
an employee does in certain exceptions (…) Using computer vision in combination with processmining could result in in the discovery of this process. (…) This can be added to the RPA flow.”

Due to this different nature, we do not consider automation levels relevant here. We do not have data on
the performance impact. However, we anticipate that it would strengthen the RPA performance impact.

5

Evaluation: focus group sessions

We performed two focus groups to evaluate the Intelligent RPA Framework. Each focus group consistedof two phases: a simulation and an expert evaluation. For the simulation, we developed a case consistingof a process model, a process description, and an input data specification. In this process model, we
identified six tasks that could be automated through RPA of which three also could be augmented with
AI. The process steps that could be automated through RPA and/or AI are listed in Table 5. For detailed
process model, we refer to Nieuwenhuijs (2020).

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Using AI to Augment RPA

During the first phase of both focus groups, we asked the participants individually to identify
opportunities for RPA, which we then discussed in the groups. Then, the participants individually
identified which RPA opportunities could be augmented by RPA, which we also discussed in the groups.
In general, the participants were able to apply the framework consistently. All RPA and AI opportunities
were identified by the groups and by the majority on an individual level (as shown in table 5).
Process step
Check credit history

Opportunity
RPA & AI

Assess loan risk
Prepare acceptance pack
Check if insurance
quote is requested
Verify repayment
agreement
Approve/cancel
application

RPA & AI
RPA
RPA

Table 5.

RPA & AI
RPA

Rationale
Use RPA and Search to extract from various
document formats
Use AI to predict a loan default
Let RPA prepare the acceptance pack
Let RPA perform check# participants
7 of 8Use RPA and Search to extract scanned image
from system and assess if there is a signature
Use RPA to update systems and send out emails

6 of 85 of 8
7 of 8
7 of 8

7 of 8

Tasks evaluated in the simulation.

In the second phase of the focus groups, we evaluated the experts’ opinions of the framework, througha questionnaire. Table 6 provides the results of the scores based on a 5-point Likert scale. Each of theevaluation criteria in Table 6 was tested by asking the respondents to rate two statements. Both thecriteria and the statements were based on the work of March and Smith (1995) and Aier and Fischer(2011). Level of detail scored lowest with a 2.9. Between the first and the second focus group, we added
further examples which improved the average score from 2.6 to 3.1. Overall, the framework helps tomake people understand in a structured way when to use RPA and when to integrate RPA with AI. No
participant had significant issues understanding the framework.

Session 1 average
Session 2 average
Total average

Table 6.

6Completeness
3/5
3/5
3/5

Fidelity toreal-world
3.3/5
3.8/5
3.5/5

Internal
consistency
3.9/5
4.4/5
4.1/5

Level ofdetail
2.6/5
3.1/5
2.9/5

Operationability
3.8/5
3.4/5
3.6/5

Understandability
4.1/5
3.8/5
3.9/5

Results of the expert evaluation.

Conclusion and Discussion

AI capabilities can augment RPA to allow for the support of tasks with a high complexity. In our study
we developed the intelligent RPA framework, a conceptual framework for augmenting RPA with AI. In
our empirical study, we identified 13 fits of specific AI capabilities that can be used to augment RPA.
Moreover, we constructed five Task-Technology Fit types, expressed in concrete use cases, that show
how AI can augment RPA.
Our study confirms the potential of AI to allow RPA to support more complex tasks (Aalst et al., 2018;
Gotthardt et al., 2019; Scheer, 2019). Comparing the identified use cases with literature, we see that theuse case AI structuring for RPA to convert unstructured data is reflected in other studies (e.g. Devarajan,
2018; Kobayashi et al., 2019; Van Belkum et al., 2018). Also, several studies indicate that AI can help
RPA to go beyond rule-based pre-configured scenarios (Schmider et al., 2019), or judgment decisions(Madakam, Holmukhe and Kumar Jaiswal, 2019; Met et al., 2020), which corresponds to our use caseAI capturing complex patterns for RPA. The use case AI increasing RPA robustness is mentioned by

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Using AI to Augment RPA

Beerbaum (2020) on a conceptual level. The interviews provided important extra information on theusefulness of this use case and showed the dilemma of a server-side implementation.
Not explicitly mentioned in literature is the use case AI optimizing for RPA. However, some authors
mention that future RPA capabilities will self-configure (e.g. Hofmann, Samp and Urbach, 2019;Jędrzejka, 2019). Furthermore, Hull and Motahari Nezhad (2016) pointed out that AI (specificallyCognitive Computing) will enable automatic learning about business processes. Also, more generally,using AI in optimization is not new (e.g., Huang et al., 2011; Schallner, 2019). The use case AI capturingexceptions for RPA has not been reported before in literature. Although some authors point out thepotential of process mining and RPA (Santos et al., 2019; Syed et al., 2020), or AI creating RPA bots
(Hofmann, Samp and Urbach, 2019), these studies look at this problem from a process discovery
perspective to identify RPA tasks, while we look into existing RPA implementations. The same
technique can be used in both cases.
We picture several practical implications of this research. First, the developed framework can be used
as a starting point for discussion and provide a basis of the type of use cases that AI enables in RPA.
Secondly, the results of this research can help organizations automate more processes end-to-end.
Organizations that tried to implement RPA but struggle with some tasks can use this model to identify
what complexity they are dealing with. Subsequently, these organizations can locate the AI capabilities
they need, based on the complexity they have in their process. Finally, organizations could map the usecase typology on a set of their processes. As a result, a broad and structured overview of the automation
potential in different departments, type of use cases, and required AI capabilities is provided. Therefore,
our findings can offer guidance from a strategic management point of view.
A limitation of this research concerns the consistent understanding of the used constructs in theinterviews. We tackled this threat by asking additional probing and specifying questions and byconsistently comparing the examples mentioned by the interviewees with the delineated task
characteristics. Also, some fit scenarios are solely based on expert interviews because they are observedin cases and not found in literature. Further research is necessary to further develop and evaluate theconstructs and fit scenarios.
Further work could focus on extending the framework towards a process assessment method to enableassessing organizational processes on their exact potential of AI. Another research direction is todevelop an instrument to quantitatively measure the relations between fit type, automation level, andperformance impact. This would enable us to make statements about what kind of performance gains
can be expected from certain AI and RPA implementations and the extent to which this should be
automated. Finally, more research needs to be done to how sustainable RPA implementations are andhow they affect the work needed to digitize (other) business processes.

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