Benefits and Challenges of Robotic Process Automation

Laura Lahtinen

, Tommi Mahlamäki

and Jussi Myllärniemi

Unit of Industrial Engineering and Management, Tampere University, Tampere, Finland

Unit of Information and Knowledge Management, Tampere University, Tampere, Finland

Keywords: Robotic Process Automation, Benefits, Challenges.

Abstract: Digitalization has been shaping the ways how we work and live for a considerable length of time. Businesses’

competitiveness is partially determined by their capability to adopt and leverage new technologies. One of

the latest trends in digitalization is the automation of repetitive, low-cognitive human tasks in white-collar

jobs. A tool that was created to automate low-cognitive human tasks, Robotic Process Automation utilizes

software robots to address this topic. RPA gains attraction because it is easily scalable, and implemented at a

rather low cost and the use of it doesn’t require prior programming skills. This research relies on existing

literature and identifies the benefits and challenges of Robotic Process Automation.

1 INTRODUCTION

Almost all aspects of our lives have become digital,

and the trend continues – not least, the way of doing

business is in constant change due to digital

development (Reis et al., 2018). The emergence of

new digital tools has enabled businesses to improve

their efficiency, and accuracy, and reduce costs

(Osman, 2019). Recently, the automation of repetitive

human tasks (Leshob et al., 2018) and non-value-

adding activities in a scalable manner has attracted

increasing interest from corporates (Hofmann et al.,

2020). A set of tools that meet these requirements are

Robotic Process Automation (RPA) tools which take

over the above-mentioned repetitive manual

processes by robots created for this purpose (Fantina

et al., 2021). RPA tools can be defined as “a business

process automation system that uses software tools to

interact with existing applications and re-place

humans” (Fernandez & Aman, 2021).

This study aims to understand what the benefits

and challenges are offered by RPA. We are especially

interested in studying Sales support work activities

can be digitalized with RPA. The potential of RPA in

enhancing operational excellence and fostering

digitalization in Sales support can be very important

aspect to study. This paper is written as a position

paper that paves the way for a comprehensive model

https://orcid.org/0000-0003-3329-4351

https://orcid.org/0000-0002-2848-0426

of RPA implementation in Sales support.

In the following chapter the aim is to show the

conceptual basis of RPA. Chapter 3 presents the

literature setting, benefits and challenges of RPA,

mainly in general but also from Sales support point of

view. Finally, in the chapter 4, some future thoughts

of the phenomenon.

2 ROBOTIC PROCESS

AUTOMATION

Automation of business processes is not a recent

phenomenon: starting at least from the 1990s

organizations have tried to figure out, what tasks

should be automated and what tasks to be performed

by humans (van der Aalst et al., 2018). The dominant

approach for automating business processes has been

Business Process Management (BPM) which is an

umbrella concept for techniques and methods aiming

to organize business processes in an efficient manner

(Mendling et al., 2018). BPM is a large portfolio of

practices used also for finding solutions for process

improvement and decision support (Osman, 2019).

BPM automation systems rely on the classical

“inside-out” approach to improve processes, meaning

that the new system is developed from scratch and

integrated into the existing IT infrastructure, often

Lahtinen, L., Mahlamäki, T. and Myllärniemi, J.

Beneﬁts and Challenges of Robotic Process Automation.

DOI: 10.5220/0012208700003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 249-255

ISBN: 978-989-758-671-2; ISSN: 2184-3228

249

requiring some tuning of the existing systems as well

(van der Aalst et al., 2018). This makes BPM projects

quite expensive, and the use of BPM tools needs

extensive programming expertise from users (van der

Aalst et al., 2018; Lacity & Willcocks, 2016b). Due

to the costly implementation, it is best to have many

cases with a similar structured process to make the

automation economical. This limits the applicable use

cases of BPM to only a few even though there is

usually a lot of repetitive work suitable for

automation in an office environment, but which is not

frequent enough to justify automation cost-wise. (van

der Aalst et al., 2018) That is where RPA comes in: a

tool with the primary focus on automating tasks

which can be deployed with little investment (Osman,

2019).

RPA aims to automate existing processes

performed by humans using existing applications

making it feasible for cases that wouldn’t work with

BPM (Lacity & Willcocks, 2016b; Osman, 2019).

The Financial Express (2016) defines RPA as a set of

automation software tools utilized by companies for

repeat processing and low-end tasks without human

involvement (Fernandez & Aman, 2021). Along with

other newer business process automation approaches

it has emerged due to the advancements in the field of

Artificial Intelligence, Machine Learning and

distributed systems which have provided the

foundation for new automation technologies

(Mendling et al., 2018). RPA technology is based on

software robots (Engel et al., 2022). Typically, robots

remind us of physical electromechanical machines,

but those can be also software-based as in this case; a

robot is just any kind of machine that replaces a

human resource (Lacity & Willcocks, 2016a).

Software robots will take over a big share of white-

collar jobs in the same way that physical robots have

replaced blue-collar jobs (Madakam et al., 2019).

Robots can have a different basis for action: RPA

works on rules-based robots but there are also

learning-based robots which improve by learning

from data. Automation that is implemented using

learning-based robots is called cognitive automation.

(Engel et al., 2022)

While BPM relies on an “inside-out” approach,

RPA uses the opposite “outside-in” approach where

the existing information systems remain untouched,

enabling implementation with little investment. RPA

requires only lightweight IT implementation,

meaning that it acts at the graphical user interface

(GUI) -level and is driven by non-IT employees

whereas tools requiring heavyweight IT the

implementation is driven by IT experts. (Engel et al.,

2022; Osman, 2019).

RPA must be consistent with the organization’s IT

governance and thus it cannot be totally outside the

control of the IT department. (Lacity & Willcocks,

2016b) RPA software is “non-invasive”, meaning that

there is no need to develop extensive platforms to

acquire RPA, but it just sits on top of existing systems

(Fernandez & Aman, 2021; Lacity & Willcocks,

2016b).

RPA works with structured data, which means

that the data is organized in a consistent structure that

allows running queries on it to retrieve information

for organizational use; the data has a definite format

and length, and it is easy to store (Eberendu, 2016).

The type of data is important as RPA cannot process

unstructured data, such as images and emotions

(Desai et al., 2021) but cognitive automation tools

can. Structured data can be processed and analysed

using statistical and mathematical methods (Rabin et

al., 2020), which fits the rules-based operating

principle of RPA. According to Osman (2019), the

quality of data is a vital aspect of RPA applications to

ensure the correct functionality of the robots. This

also sets a boundary condition for the tasks to be

automated as all data must support the same format

and be electronic (Osman, 2019). If data comes from

different sources and with different labels, it needs to

be standardized before RPA usage (Moffitt et al.,

2018). In general RPA implementation is less risky

with standardized and mature processes, meaning that

the process is stable, and results are predictable

(Leshob et al., 2018). Tasks that require human

judgement and have uncertain outcomes are better for

probabilistic approach-based automation ((Moffitt et

al., 2018).

So, feasible processes to be taken over by RPA are

rules-based, non-complex, standardized and executed

in high volumes (Moffitt et al., 2018; Rutschi &

Dibbern, 2020). It remains to be clarified, how RPA

works. Syed et al. (2020) state that RPA robots mimic

human behaviour, following the manual path taken by

the user through a range of computer systems to

perform a certain business process. The robots can be

seen as digital workers each of which is using its own

username and password to access systems, similar to

human employees (Kokina & Blanchette, 2019). RPA

robots work autonomously by interacting with

multiple systems and making easy, binary decisions

that don’t require intelligence unless RPA is enriched

with AI features which enable more complex

decision-making (Kokina & Blanchette, 2019; Syed

et al., 2020). Simple RPA mimics human behaviour

whilst cognitive automation mimics or augments

human judgement (Hegde et al., 2017). RPA and

cognitive automation tools are also highly synergetic

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250

when used together and when used in tandem the

automation possibilities are extended (Lacity &

Willcocks, 2018, pp. 57-58). In this study, we focus

on the traditional, non-AI enriched version of RPA as

it is where organizations often start automating their

processes (Lacity & Willcocks, 2016a).

RPA communicates with the other systems the

same way as humans do, so via the front end while

traditional software communicates with other

systems via the back end and data layers (Asatiani &

Penttinen, 2016; Kokina & Blanchette, 2019). RPA

works based on pre-defined rules which follow the

routine of a human employee performing the task

(Flechsig et al., 2022; Rutschi & Dibbern, 2020).

All processes have exceptions which must be

considered in the process design as the robot follows

the rules unwaveringly and in case of an exception, it

is unable to process if an exception handling is not

determined. Despite careful design, no application

will run smoothly all the time and that’s why the robot

must indicate somehow, e.g., by sending an email to

the responsible person that it has completed its task

(Fantina et al., 2021). The rule of thumb is that one

robot performs one process and once the process has

been fully implemented in the robot no changes will

be made unless an error occurs or the environment

changes (Lacity & Willcocks, 2016a).

An archetype of an RPA task is transferring data

from one source to another. Often the input is

processed – again, based on the rules – and the result

is entered into some other software system (Engel et

al., 2022). These kinds of processes are in many

sources (e.g., Engel et al., 2022; Lacity & Willcocks,

2016b; Syed et al., 2020) described as “swivel-chair”

-like tasks, which do not require human intervention,

so mechanical and repetitive work with little or no

need for human intervention. Clarity of the process

helps also in the development of automation, which

can be done by the employee whose tasks RPA will

take over. Lacity and Willcocks (2016b) describe

RPA development as a “drag and drop” -process since

the users don’t need to write code but only drag and

drop icons and the code is automatically generated in

the background. Some RPA software also allows

automation to be developed using a record function,

which records the user performing the task and based

on the recording generates the automation logic for a

robot (Moffitt et al., 2018).

Even though RPA development doesn’t require

specialized programming skills, it requires an

understanding of information system functionalities,

such as the structure of rule-based logics (loops,

conditions and so forth), the use of data and the

interfaces of the applications used in automation.

That’s why it is often beneficial that business

operations and IT functions cooperate in RPA

development. (Hofmann et al., 2020)

3 IMPACTS OF ROBOTIC

PROCESS AUTOMATION

Because RPA is non-invasive technology (Madakam

et al., 2019) which is implemented on top of the

existing IT infrastructure requiring no changes in the

existing systems, it is quite cost-effective to adopt

(Asatiani & Penttinen, 2016; Engel et al., 2022). In

comparison with other automation alternatives, RPA

has very competitive adoption costs and shorter

implementation time and maintaining costs are

relatively cheap enabling savings in an organization’s

total IT service spending (Asatiani & Penttinen, 2016;

Fung, 2014). After RPA implementation there will be

cost-savings also from human resource-related costs:

depending on the source, RPA is claimed to cut 20–

50 % (Syed et al., 2020) or even up to two-thirds

(Fung, 2014) of staff-related costs, compared to a

situation where all manual tasks are performed by a

human. The numbers are based on robots replacing

full-time equivalent employees (FTEs) and one FTE

is equal to one employee working full-time on a task

(Asatiani & Penttinen, 2016; Syed et al., 2020).

Asatiani and Penttinen (2016) suggest that RPA

might also possess an alternative to traditional

outsourcing of non-core and routine activities. Both

options help to reduce human resource-related costs

and focus on core operations, but whilst outsourcing

has some hidden costs of management and problems

with complex service level agreements, RPA enables

eliminating these challenges and keeping the benefits.

(Asatiani & Penttinen, 2016; Fersht & Slaby, 2012;

Madakam et al., 2019) Robots are also not limited by

working hours but are available around the clock with

lower costs than human workforce (Driscoll, 2018;

Fung, 2014; Syed et al., 2020) which has positive

impacts on productivity (Asatiani & Penttinen, 2016).

Cost-savings are part of the improved operational

efficiency achieved with RPA. Other metrics of

efficiency are a reduction in time and manual

workload and increased productivity. These factors

have a positive interdependence as the reduction of

manual tasks leads to better time efficiency in terms

of reduced waiting time, task handling time and so

forth. (Syed et al., 2020) Improved operational

efficiency together with all of its three cornerstones –

cost-savings, reduction of time and manual work – are

generally recognized benefits of RPA in the field of

Beneﬁts and Challenges of Robotic Process Automation

251

research and named one of the main reasons why

organizations should consider RPA adoption and also

why business managers see it as a very lucrative way

of improving key performance indicators (Fung,

2014; Gotthardt et al., 2020; Hofmann et al., 2020;

Januszewski et al., 2021; Leshob et al., 2018; Syed et

al., 2020). The reduced manual workload is also

considered to have positive impacts on the personnel

as they are freed from repetitive and tedious tasks to

more complex and value-adding activities (Hofmann

et al., 2020; Leshob et al., 2018; Syed et al., 2020)

which is believed to improve employee morale

(Madakam et al., 2019). Capable human resources

allocated to more engaging and interesting work

contributes also to improving efficiency (Madakam et

al., 2019; Syed et al., 2020).

Replacing humans with robots helps

organizations improve accuracy and quality (Driscoll,

2018; Rutschi & Dibbern, 2020). “Swivel-chair”

tasks including accessing multiple systems and

transferring data from one system to another make

good candidates for RPA and these kinds of tasks are

also prone to errors (Fung, 2014). According to Das

and Dey (2019), RPA can eliminate human errors

when the process and implementation are done

properly. Also Syed et al. (2020) claim that with RPA

deployment amount of human errors is decreased and

automated tasks are expected to achieve 100 %

accuracy. Also, Fung (2014) and Madakam et al.

(2019) recognize that better accuracy and fewer errors

can be achieved with RPA deployment, but they

refrain to give any precise numbers of improvement.

Robots can achieve better accuracy while working at

a much higher speed than humans and don’t get tired

like humans, meaning that robots are simply able to

outperform humans in certain types of tasks (Costa et

al., 2022; Rutschi & Dibbern, 2020). An advantage

compared to the human resource is also the fast

scalability of capacity based on the need, so the

workload of robots can be easily up- or downscaled

based on business demand (Das & Dey, 2019; Fersht

& Slaby, 2012; Hofmann et al., 2020; Syed et al.,

2020).

One more benefit of RPA is the ease of

configuring the automation which doesn’t require

programming knowledge (Lacity & Willcocks,

2016a; Madakam et al., 2019) but the RPA vendors

provide an intuitive user interface where the RPAs are

built by arranging a sequence of modules and control

flow operators to match the business process rules

and logic (Hofmann et al., 2020). This allows the

responsible business process people to design the

automation themselves. The automated processes are

also not limited to one business, but process owners

can re-use the execution logic created (Hofmann et

al., 2020). According to Lacity and Willcocks

(2016b), this non-IT staff can be trained to design

automation within just a few weeks which fosters

faster implementation (Osman, 2019). The control

over the process remains also within the business

function or unit and reduces the dependence on

central IT services (Fersht & Slaby, 2012). The

overall control over the business process also

improves when transferred from humans to robots

(Syed et al., 2020).

Several sources also raise the improved data

quality in terms of accuracy, consistency and

compliance and data security as one RPA benefit

(Fung, 2014; Januszewski et al., 2021; Leshob et al.,

2018; Siderska, 2021). To get a comprehensive

understanding of the positive impacts of RPA, the

above-listed benefits and respective sources are

gathered in below Table 1.

Table 1: Benefits of RPA.

A coin has two sides and RPA also has its risks

and challenges in addition to the benefits listed in

Table 1. One central challenge is that RPA currently

is only suitable for certain types of tasks and

processes (Asatiani & Penttinen, 2016; Fernandez &

Aman, 2021). Identifying appropriate processes

suitable for RPA requires skills and a correct

approach, which is not always so straightforward

(Fernandez & Aman, 2021; Siderska, 2020). Keeping

in mind the elements of a suitable task for RPA and

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avoiding choosing complex and subjective processes

for automation, at least at the beginning of the

organization’s RPA journey, it’s possible to mitigate

the risk (Fernandez & Aman, 2021; Rutaganda et al.,

2017). Being a recent technology, RPA lacks a

proven track record compared to traditional

outsourcing (Asatiani & Penttinen, 2016), for

instance, which makes it hard for organizations to

choose the best approach to evaluate the tasks in their

situation (Costa et al., 2022).

Interestingly, Fernandez and Aman (2021) name

data security and privacy as the main issue of RPA

while some research stated that RPA implementation

improves data security and privacy (e.g., Leshob et

al., 2018; Siderska, 2020). Fung (2014) claims that

RPA lowers the risk of unauthorized data access and

thus improves data security and governance. Higher

compliance to data regulatory requirements can be

achieved through process transparency and

traceability and reduced error-level. (Fung, 2014)

Also Moffitt et al. (2018) see that RPA can improve

security as human interaction with sensitive systems

is decreased and processes are better monitored. On

the other hand, robots handling data constitute risks

especially regarding hacker attacks according to

Flechsig et al. (2022). The robots will log into

systems using company credentials and thus have

access to passwords which has a potential risk for

unauthorized access if not properly secured. Also, if

mistakes are made during the configuration of robots,

it can cause serious errors throughout the systems it

has access to and a malicious robot may execute tasks

harming the organization. (Fernandez & Aman, 2021)

Companies that wish to automate processes handling

confidential client data (e.g., in the accounting

industry) might face customer reluctance to use RPA

software because they have concerns about data

security (Cooper et al., 2019). However, these risks

do not only concern RPA but any IT system and

countermeasures to mitigate the above-mentioned

risks are readily available and continuously

developed (Gotthardt et al., 2020).

The type of data poses an issue for non-AI

enriched RPA, as it requires data to be of a structured

type and stored digitally (Costa et al., 2022). RPA

cannot process unstructured data, such as scanned

documents, which make up to 90% of all data. As a

consequence, companies have to feed RPA with

process data in a correct form which maintains low-

value tasks for employees. (Gotthardt et al., 2020)

Cognitive automation tools are capable of handling

and processing unstructured data but in this study’s

context the technological constraints of RPA have to

be followed and tasks including the processing of

unstructured data are not suitable to be automated

with RPA as such. (Gotthardt et al., 2020; Hegde et

al., 2017)

Asatiani and Penttinen (2016) and Fernandez and

Aman (2021) see that RPA’s impact on the jobs and

current employees is a challenge. As with any new

technology, people might feel threatened by RPA

(Lacity & Willcocks, 2016b) and see robots as direct

competitors for a job (Asatiani & Penttinen, 2016) or

that their positions are weakened by robots (Gotthardt

et al., 2020). If not transparently communicated and

properly handled, this might have destructive impacts

on employee morale (Asatiani & Penttinen, 2016).

Siderska (2021) claims that there is no reason to fear

that robots would make people obsolete, but it will

surely impact jobs and require organizations to

rethink employee roles. Strategic initiatives to deploy

RPA should consider engaging employees with the

technology which is essential for RPA success

(Amaka & Nnenna, 2021). Table 2 gathers RPA

challenges recognized in current research.

Table 2: Challenges of RPA.

Despite the challenges listed above, research has

proven successful RPA implementations and positive

post-implementation feedback (Asatiani & Penttinen,

2016; Willcocks et al., 2017). According to Amaka

and Nnenna (2021) and Siderska (2021), the overall

impact of RPA is seen as positive as its strengths

outweigh its weaknesses and thus the technology is

regarded more as an opportunity than a threat. The

realization of both benefits and possible challenges

comes down to the success of RPA implementation

(Costa et al., 2022).

Beneﬁts and Challenges of Robotic Process Automation

253

4 CONCLUSION

Digitalization and automation of workflow processes,

e.g. RPA, are emerging in organizations as a solutions

to their constantly growing demands of

organizational processes. The utilization of RPA is

one of the ways to improve efficiency in the

organizations, by reducing human labor in routine

business processes, improving the quality of the

work, enhancing scalability, increasing productivity,

and reducing costs (Kirchmer 2017; Fernandez &

Aman 2018). This position paper offers a fairly novel

approach to the discussion of impacts of RPA,

especially benefits and challenges the literature

recognised. Even though there is no universal concept

or framework for RPA adoption but a stream of

research around this topic has recently emerged (e.g.,

Costa et al., 2022; Gotthardt et al., 2020; Januszewski

et al., 2021; Rutschi & Dibbern, 2020). And with the

above created understanding of the benefits and

challenges of RPA the aim of our ongoing research is

to continue towards a framework of RPA adoption in

Sales Support.

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