Intelligent_Process_Automation_The_Future_of_Digital_Transformation (2).pdf

Proceedings of the SMART–2021, IEEE Conference ID: 52563
10th
International Conference on System Modeling & Advancement in Research Trends, 10th
–11th
December, 2021
Faculty of Engineering & Computing Sciences, Teerthanker Mahaveer University, Moradabad, India
Copyright © IEEE–2021 ISBN: 978-1-6654-3970-1 185
Intelligent Process Automation: The Future of
Digital Transformation
Pankaj Singh Kholiya1
, Akshat Kapoor2
, Meghavi Rana3
, Megha Bhushan4*
1,2,3,4*
School of Computing, DIT University, Dehradun, India,
E-mail: 1
pankajkholiya005@gmail.com, 2
kapakshat@gmail.com,
3
ranameghavi16@gmail.com, 4*
mb.meghabhushan@gmail.com
Abstract—The technological evolution in the last decades
has led us to the forefront of the digitization of services at every
stratum of the processes and businesses. As the technology is
advancing, an era of automation is witnessed in each field.
Intelligent Process Automation (IPA) is amalgamation of
Robotic Process Automation (RPA) with Artificial Intelligence
with a view to create end-to-end processes which can think,
learn, and adapt on their own. This paper aims to present
a comprehensive study of the RPA tools associated to the
intelligent automation using Artificial Intelligence. It also
provides a brief overview of the current development in IPA
market which can contribute to the implementation of the IPA
in various organizations.
Keywords: Artificial Intelligence, Robotic Process
Automation, Natural Language Processing, Intelligent
Process Automation, Machine Learning
I. Introduction
The entire workplace is undergoing digital
transformation wherein Robotic Process Automation
(RPA) trend appears to be persistent [1]. In future every
factory/office would prefer to adapt Intelligent Process
Automation (IPA) as it will be at the forefront of RPA
revolution [4-7]. Table 1 depicts the difference between
IPA and RPA technologies [2-3]. IPA guides the businesses
to automate processes by making use of various types of
data i.e., structured, unstructured, and semi-structured. It is
a technology that allows to configure a so-called software
robot or bot, to compete with the work that is carried out
by a human, responsible for interacting with a system for
executing the process. In other words, it is a combination
of process enhancements and next generation tools with
an aim to reduce or eliminate repetitive as well as regular
tasks where it imitates human behavior and learn from it
with anaim to increase the efficiency in each iteration. Due
to the popularity of Deep Learning (DL) and cognitive
technologies, the automation levels can be supplemented
with decision making capability. The aim of IPA is to
give higher efficiency by utilizing the employees for
other complex tasks which will enhance job satisfaction,
productivity [8] and the output within time while reducing
the errors. The figure 1 represents IPA.
Fig. 1: IPA includes RPA and AI [9]
Table 1: Comparison of RPA and IPA.
RPAw IPA
It can be termed as a software
robot which helps businesses
to automate processes by
mimicking human action on
computers with little or no
human assistance.
It is a combination of RPA with
various AI technologies, or it can
be also termed as next generation
of RPA which has a capability to
handle far more complex processes
rather than just carrying out
automation of routine tasks.
It helps to automate high
volume processes which are
repetitive and mundane in
nature.
It brings a measure of decision
making to the processes or tasks
with an aim to meet challenging
demands. It makes the processes
quicker, reliable, and efficient.
It consists of primarily screen
scrapping and workflow
automation.
It increases the scope of RPA by
combining new technologies such as
Machine Learning (ML), Intelligent
workflows, Natural Language
Processing (NLP), Data extraction
and Artificial Intelligence (AI).
It utilizes structured data to
complete tasks.
It has the capability to handle both
structured and unstructured data and
the major advantage is that it also
supports decision making.
It does not have ability to learn
and is designed primarily to
mimic human actions.
It can understand context, learn, and
iterate and after some time also can
perform better than humans.
II. Related Work
This section will discuss various existing works
including prediction, technologies, benefits, and
applications in area of IPA.
2021
10th
International
Conference
on
System
Modeling
&
Advancement
in
Research
Trends
(SMART)
|
978-1-6654-3970-1/21/$31.00
©2021
IEEE
|
DOI:
10.1109/SMART52563.2021.9676222
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10th
–11th
December, 2021
186 Copyright © IEEE–2021 ISBN: 978-1-6654-3970-1
uses algorithms that can work efficiently in both
supervised data and unsupervised data with an
objectivetoidentifypatternsindata.TheSupervised
algorithms prior to making predictions of their own
create inputs and outputs and the unsupervised
algorithms observe structured data and develop
insights majorly from pattern recognition. In IPA,
the bots can evaluate the efficiency of processes
and adjust improve processes.
• DL [22] is a subset of ML as it provides the system
with advanced thinking capabilities which can
analyze data at an advanced level. It makes use
of artificial neural network like the ones which
has been go gifted to the humans. DL gives the
computer systems a more in-depth knowledge to
analyze and draw conclusions on the given data.
• Cognitive computing aims to provide improved
interaction between people and machines. It
is a technology where the primary focus is to
imitate human personality. The thought process,
personality, and sentiment help a system in the
decision-making process.
• Intelligent Character Recognition (ICR) aims to
convert handwritten text characters into a format
readable by machine. The difference between
Optical Character Recognition (OCR) and ICR is
that the capability of OCR is restricted to printed
data however, the ICR, is intelligent enough to
decipher data from non-standard documents, which
contain handwritten texts with varied formats.
• Optical mark recognition (OMR) is a technology
which helps in recognizing tick/check marks in a
form and has the capability to check underlined
text and shaded circles.
• Optical barcode reader (OBR) is a technology
which helps in reading barcoded data from a
document.
• Computer vision is a field of ML used in the
interpreting device and interpretation stage
which enables computers and systems to derive
meaningful information from digital images,
videos and other visual inputs and take actions or
make recommendations based on that information.
• NLP analyses human language with an aim
to provide machines with a capability to read,
understand and derive meaning out of the context.
It works through using techniques like ML and
AI. It uses chat bots to determine the meaning of
interactions with humans to provide users with
relevant responses.
D. Benefits of IPA
Various benefits of IPA are as follow [7-9]:
• The automation of processes and systems apart
from the increase in accuracy will also provide
A. IPA Predictions by Gartner
It includes the following [4]:
• By the year 2023, it is predicted that 40% of
complete enterprise workloads may be deployed
in cloud infrastructures.
• By the year 2024, customer centric organizations
with Internet Technology (IT) teams, which
accurately understand the customer requirements,
will outperform other organizations by
approximately 20%.
• By the year 2025, approximately 50% of the
world population will experience a minimum one
program on Internet of Behaviors (IoB).
• By the year 2025, it is predicted that approximately
40%ofthebusinessesbasedonphysicalexperience
will outperform its competitors by switching to
virtual experiences.
All the major organizations are focusing on the best
way to provide virtual experience to the customers, thereby,
creating new digital processes with an aim to provide the
best experience to the customer.
B. The Three Pillars of IPA
IPA majorly includes three technologies which will
accelerate business and technology transformation [5].
• The first and the most important component for
IPA will be AI where intelligent use of ML [10]
and algorithms can provide businesses with a
knowledge base and hence, meaningful predictions
can be made for an organization in advance. AI
can also be termed as decision engine of IPA.
• The second component will be Business Process
Management (BPM) which will provide agile
processes and automate the workflows thus,
improving interactions.
• The third component is RPA which can leverage
various domains of AI to automate and perform
more complex tasks with near 100% accuracy.
C. IPA Technologies
Various technologies of IPA are as follows [6-22]:
• RPA automates routine and mundane tasks such as
document processing, analysis of data, followed by
extraction. It is highly customizable and is based
on rules which mean that organizations can use it
for undertaking calculations, preparing reports and
file documentation.
• Smart workflow tools can be termed as process
management solutions that are used to control
RPAs and integrate the task hand offs performed
by employees and machines, along with real time
tracking of tasks.
• ML is a subset of AI, and it provides systems with
an ability to learn from the data without there
be any requirement of it being programmed. It

Intelligent Process Automation: The Future of Digital Transformation
flexibility as well as scalability to an organization.
Further, changes can be immediately implemented
without requirement of any training on new
processes to the human workforce.
• The human workforce can be utilized for more
complex and challenging tasks which will
enhance their job satisfaction as an employee
gets disinterested easily by performing repetitive
mundane tasks.
• It yields a better Return on Investment for large
organizations. IPA utilizes the power of AI to take
better decisions which in turn provides consistency
to tasks which are repetitive in nature.
Fig. 2: Benefits of IPA [24]
• It helps to provide an enriching experience to the
customer as the queries are processed faster with
accuracy.
• The issues of compliance and regulations can be
consistently addressed by a company due to the
automation as the human error is nullified in this
approach.
E. Applications of IPA
Various applications of IPA are as follows [14]:
• Processing and interpreting unstructured data:
ML can be used to interpret and route the customer
queries, emails as well as tickets which then triggers
RPA to provide an automated and streamline
response.
• Predictive analytics: The complex ML algorithms
can make predictions about a customer and change
in the customer’s behavior and can organize
retention response.
• Predictive maintenance: The fault risks can be
detected in complex machinery by using ML
thereby providing an alert for proactive repair.
• Anomaly detection: By using the power of AI, the
anomaly can be timely detected and accordingly,
the corrective action can be taken by the
organizations [15-21].
• Forecasting: Forecasting of demand is a common
challenge across various industries. Therefore,
RPA can be used to gather real time and local data.
Then time sensitive ML can be used to identify the
choke points and improve the accuracy.
• Real time optimization: As proactive identification
of issues in logistics can optimize routing of goods,
therefore IPA can provide real time alerts to the
companies so that corrective action can be taken
in advance.
F. Proposed methodology for IPA Implementation
in an Organisation
IPA aims to provide a competitive advantage to
companies only when companies create a clear roadmap to
success. The key consideration for organizations seeking
to pursue RPA and, more importantly, migrating towards
successful IPA deployments to drive a more efficient
workforce is as follows [24-26]:
Align IPA with the Goals to be Achieved:
To fully optimize the power of IPA, an organization must
create a journey map that is in sync with the business
strategy. The goal for implementation of IPA in an
organization should be concise and clear.
OptimizetheProcessesfirstbeforeIPAImplementation:
IPA can provide the greatest impact only once the when the
processes are fully optimized. From there, AI and ML along
with other technologies can help improve the processes
furthermore.
Tool Selection: It is important that right tool should
be selected to automate the process wherein the thorough
understandingofRPAandprocessesistakenintoconsideration
The techniques which are employed in Intelligent automation
can be further broken down into following.
Look: ICR, OCR & OBR are used for converting
the images of handwritten or printed text into the digital
format. The use cases include invoice scanning, passport
face detection and face verifications.
Read and Understand: Understanding of text after
reading is performed using Natural Language Processing
(NLP). It uses few predefined techniques to analyze the
natural language and speech. NLP technique is used to
understand the context of a message. After understanding
it further transforms the unstructured data into a structured
data which is used as an input for achieving the desired
automation. Its use cases comprise of chatbots for self-help
and chatbots for answering FAQs.
Learn: It is carried out by using ML algorithms which
enable a system to derive patterns out of structured, semi
structured, and unstructured data without any requirement
of programming the system to derive insights out of the data.

10th
–11th
December, 2021
Decide: Cognitive reasoning is a technology which
is used to make complex decisions based on the previous
knowledge and provides the explanations to the decision
made. The best use cases to enunciate use of cognitive
reasoning is carrying out risk assessment and enhancing
back-office employees while making judgements.
Communicate: As the NLP evolves the conversational
AI helps powering the Virtual agents which can hold well-
structured conversations between user and machine. The
best use of NPL include to have a human-like conversation
with a customer and seamlessly handover to human if
needed and to create an intent Figure 3: Ever and emotion
capturing virtual assistant that can determine the best course
of action during conversation.
Fig. 3: Everest Group RPA Tools PEAK Matrix Assessment 2021 [28]
The implementation of IPA is not a one-time process,
it is defined as an implementation of a roadmap in the
organization to keep automating each and every processes
which are currently done manually.
G. Challenges of using IPA in an Organization
The most common challenges of integrating IPA in an
organization include the following [27]:
• The inadequacy of skilled manpower in field of
IPA to implement process automation in large
scale organizations.
• The retraining and re skilling of staff is required to
use IPA technology in an organization.
• There is a requirement to integrate IPA technology
with the software solutions already present in the
industry.
• Managing the resistance from human workers
because of fear of rightsizing.
• The challenge of implementing robust
cybersecurity in dealing with threats from hackers.
III. Summary
This section includes various IPA Tools. All tools
are majorly categorized in three parts: leaders, major
contenders, and aspirants as shown in Fig 3.
A. RPA Tools with AI support
There are more than twenty companies globally which
are working on RPA [45] these days. Few companies which
have major impact in the market are analyzed as shown in
Table 2 [29-44].
B. Comparison of Leading IPA Solution Providers
Table 2 gives the description of all the top leaders in
IPA along with their tools. Various Market leaders in IPA
tools are as follow [46]:
Table 2: Comparison Between Different IPA Solution Providers in the World.
Function Nice RPA Blue Prism Automation Anywhere UiPath Microsoft
Year released 1986 2001 2003 2005 2021
Version/
Edition
Community edition not
available
The trial version is
not available
A community edition is
there
A community edition
is there
A community
edition is there
Reusability Yes Yes Yes Yes Yes
Base Technology VB scripting and C # C # Intermediate Microsoft
Intermediate C# and
Microsoft
Microsoft, Python
Architecture Client-server building Client-server building Client-server architecture
Cloud Based Web-
based orchestrator
Software
Recorders (Macro
Readers)
Smart recording (record
and play)
The Recorders do not
exist
It allows to record and
practice actions which can
be later modified
It allows to record and
practice actions which
can be later modified
Same as Ui Path
and Automation
Anywhere
User-friendliness Medium High Medium High High
Reliability Moderate Very high High Moderate Low
Accuracy
Great for those tasks
which need little or no
subjective judgment
Desktop PCs, web
and Citrix automation
can be used
Fair efficiency across
mediums
Best suited for BPO
automation and shines
in Citrix automation
High for web
automation and file
handling
Cognitive
Capability
Less Less Medium Less Medium
Operational
Scalability
Fast execution and
seamlessly scalable
Speed of Execution
is high
Limited deployment in
large-scale organizations
Average Performance
Easy to scalable
with multi-thread
architecture

Intelligent Process Automation: The Future of Digital Transformation
UiPath [28-30]: UiPath is a global leader company
in the field of RPA. It helps to discover opportunities,
build automation quickly, manage at enterprise level, run
automation through robots, and engage people as well as
robots as one team.
Blue Prism [31-33]: Blue Prism develops intelligent
software for the industry and business with more security
and intelligence. Also, developers work on making this
software for end-to-end operational activities.
Automation Anywhere [34-38]: Automation Anywhere
is a leading company in RPA. It includes Automation
Anywhere University, Bot Games, Imagine Conference,
Bot Lab, A Listers and A-People. It provides a unique cloud
based intelligent automation to the world which is also
linked with automation for the major Indian companies.
Nice [39-41]: Nice helps in pairing every service
agent in any organization with virtual attendant. The human
and robot bot’s duo combo will provide truly exceptional
customer experience. Nice Employee Virtual Attendant
(NEVA) works side-by-side with employees, guiding them
through complex time taking processes and automating their
repetitive work from any location in the world.
Microsoft [42-44]: Microsoft has introduced RPA with
AI in newly launched windows 11. It can help in repetitive
and time-consuming tasks so that a user can focus on high
value task. It has also implemented low code or no code
interface so that it should be easy to use, regardless of the
individual technical specialties.
IV. Conclusion and Future Challenges
The emergence of IPA is the next big game changer
for digital transformation of the companies in future.
This paper provides a comprehensive introduction to IPA
and all the related aspects. It has been observed that most
of the proprietary tools implement the algorithms based
on the objectives of AI which comprises of recognition,
classification, extraction, and optimization of knowledge
from the documents or processes. This work provides
descriptionofvarioustechnologies,benefits,andapplications
of IPA. Further, it summarizes various IPA tools which
can be used by organizations to automate their repetitive
mundane tasks. A comparison of leading IPA solution
providers has also been provided in tabular representation.
Therefore, all the repetitive tasks including the tasks which
do not require human intervention will be taken over by IPA
with more accuracy. Future challenge includes the lack of
strategy or clear vision to implement IPA.
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