What is Hyperautomation.?

Hyperautomation-
The next frontier
November 2020

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Brochure / report title goes here
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Table of Contents
Foreword from CII 01
Foreword from Deloitte 02
Hyperautomation – The next frontier 03
Introduction 04
Analysis of conventional RPA and its limitations 05
Emergence of hyperautomation 08
Hyperautomation ecosystem 11
Delivery approach for hyperautomation 14
Way forward 16
Connect with us 17
Acknowledgement 17
References 18

Hyperautomation-The next frontier
01
Foreword from CII
It is clear that Robotic Process
Automation (RPA) is one of the highly
discussed areas of automation right
now. RPA is a productivity tool that
allows users to configure one or more
scripts to activate specific keystrokes.
These scripts overlay on one or more
software applications and mimics
actions within the well-established
IT process. The actions could be
manipulating data, exchanging data among applications,
generating responses, etc.
There are many misconceptions around RPA like it will solve all
automation needs. We must understand that implementing
RPA alone will not yield results. Moreover, automating a bad
process does not make it better. Instead, it will speed up the
bad process.
Generally, RPA is implemented as a non-invasive integration
method to automate routine, repetitive, predictable tasks.
Also, before we decide on implementing RPA, we need to ask
ourselves whether the process is optimized for automation or
not. We have to perform process quality check before actually
evaluating it for automation readiness evaluation and actual
RPA implementation.
To summarize, we should only look into RPA if our IT process
is well established, time scheduled, and all applications
have structured data. Even though the RPA technology has
tremendous hype, its adoption is low in India especially due
to aforementioned challenges. However, the number of initial
success stories, both with large-scale adoption and smaller,
more targeted projects, is quite encouraging particularly in
financial and insurance industries.
Even though RPA may provide quick returns as a non-invasive
form of integration, it becomes challenging as processes are
not always simple, routine, repetitive and stable. The real
challenge will be to scale beyond few initial low-hanging fruits
of routine processes because complex processes require
multitude of tools.
Process hyper automation is an approach in which
organizations identify, evaluate, and automate as many
established processes as possible through standard approach.
Hyperautomation is orchestrated use of multiple technologies,
tools and platforms like artificial intelligence, machine learning,
event-driven architecture, RPA, intelligent BPM suits, etc., to
automate the business processes.
Going forward organizations who have successfully
implemented RPA will introduce Hyperautomation for
operational resiliency. Hyperautomation is inevitable.
Everything that can be automated will be automated. Market
pressure to improve efficiencies and agility are pushing
organizations to adopt such technologies.
In planning your automation journey, it is very important to
have purpose and desired business outcomes well defined. For
successful adoption of Hyperautomation, organizations should
1.
Define the purpose and Identify the use cases with
expected key business outcomes in terms of revenue,
expenditure, and business risks
2. Optimize the existing processes and standardizing data
inputs and decision logic
3. Identify relevant Hyperautomation tools
4. Implement process automation with AI as augmented
intelligence.
I hope this reports provides you foundation on
Hyperautomation technology and gives you direction while
navigating your organization towards digital transformation.
D Ramakrishna
Chairman, CII, Andhra Pradesh

02
Foreword
The year 2020 has been a
period full of socio-economic
disruptions for the human
race. We have witnessed a
major shift in perception
towards technology across
businesses and a major
segment of the consumer
fraternity globally. Steering
through the unprecedented
times, there has been a
significant need to streamline the ways of working, along
with interoperability of technological innovations across
industries and organisations.
One of the more interesting phenomena is that technology
has just not aided these efforts significantly but also
helped standalone platforms integrate and communicate
seamlessly, providing the much-needed distancing for
human users.
Each of these technologies is immensely self-sustaining and
provides a diligent framework for both B2B and B2C users,
revolutionising their user experiences. However, these
technologies also have basic limitations that has often been
a major question for service providers and product makers.
Hyperautomation, one in all and all in one, is a wise amalgam
of leading technologies, such as Artificial Intelligence (AI),
Machine Learning (ML), RPA, and advanced analytics.
This facilitates a wide segment of enterprise-level users
in harnessing the best of virtual workforce, which is both
immune and intelligent.
Hyperautomation offers a complete package to user groups by
enabling the discovery, design, build, enhancement, sand self-
learning features for a wide array of use cases across functions
and domains.
Powered by faster scalability, ROIs, implementation speed, and
interconnected platforms, this technology is the next frontier
to look up to as a one-stop solution for organisations globally.
Above all, the very essence of hyperautomation lies in the
humble conjugation of technologies, just as humans, to thrive,
evolve, and overcome individual limitations, and make the
world a better place.
I hope this report provides an enriching perspective to the less
explored technology.
Nitin Agrawal
Partner, Deloitte Touche Tohmatsu India LLP

03
Hyperautomation – The next
frontier
RPA had been launched in the late 1950s, with the
development of ML by Arthur Samuel (1959). In its nascent
stage, RPA was a standalone concept of virtual workforce
with specific application areas and pre-defined capabilities.
However, rules were just not enough to justify the raging
requirements of the real world. To sustain and grow in the
age of Industrial Revolution 4.0, the very concept needed an
innovative jolt.
‘Evolution before extinction’, a mantra that led to dynamic
integration of RPA, along with AI and ML, bringing together
one of the most disruptive technologies of current times
under a single umbrella. The strategic amalgam gave rise to
what we know as hyperautomation. Offering organisations
to digitally transform their ways of working and lay down
a strong foundation for areas of innovation of the future,
hyperautomation thrives to live by the saying – ‘Today's
Disruptive is Tomorrow's Stable.’

04
Introduction
What is hyperautomation
Hyperautomation refers to a combination of complementary
sets of tools that can integrate functional and process silos to
automate and augment business processes. Hyperautomation
brings together several components of process automation,
integrating tools and technologies that amplify the overall
ability to automate business processes.
It starts with RPA at its core, and expands the automation
horizon with AI, process mining, analytics, and other
advanced tools. The integration of these multiple
technologies enables end-to-end process redesign,
automation, and monitoring, delivering much greater value
and impact.
Hyperautomation provides several benefits over other
automation technologies. These include automating processes
at a quicker rate; using advanced analytics; offering increased
employee satisfaction and motivation; assigning a workforce
for value-added tasks; sharing accurate insights; ensuring
enhanced compliance and reduced risk; and enabling greater
productivity and increased team collaboration.
Simple and agile
What is in it
Hyperautomation does not just refer to implementing tools to manage tasks. It also requires collaboration amongst humans
who are decision-makers, and can use technology to interpret data and apply logic.
For example, imagine a case of social media and customer retention. A business can rely on tools that use RPA and ML to
produce reports and pull data from social platforms to understand customer sentiment. Reports will be generated, and
information will be readily available for the marketing team. However, it will then require the marketing team to use these
insights, and consider what types of campaigns, promotions, and incentives should be incorporated into a business plan to hold
onto satisfied customers and address the concerns of those who feel dissatisfied.
This report outlines the details around the concept of hyperautomation. It details the technology’s history and origin, its
standard definition, key components and fundamentals, comparisons, market value, and forecasts. The report helps readers in
intercepting the right set of information about the relatively new technology, and its advantages and limitations, along with an
overview of the peripheral technology spectrum it involves.
It also includes insights, quotations, statistics, and summaries from global platforms and distinguished members from socio-
economic and technological forums. These have helped in supporting various aspects of the technology from techno-functional
aspects.

05
As RPA gained traction and began to be viewed as a technology and the next phase of business process evolution,
the procedure to identify business processes for automation is not usually thorough. Due to which in many cases,
a few criticalities of the processes are missed during the evaluation phase. This later caused challenges to overall
automation. Such criticalities are difficult to determine due to high manual involvement, which can lead to numerous
flaws for the same process.
Most organisations looked at capacity creation as the key advantage and consideration to determine a business case.
This was mainly due to the high ROI expectation linked to workforce release, which is not right. With this mindset,
the majority of the automated processes led to failure as they were not amenable for automation but were still
automated as they were highly manual. Instead, ideally the ROI should be realised by making business processes
more reliable, quicker, and better. This will free up employees’ time spent on performing these mundane and
repetitive tasks, allowing them to focus more on critical, high-value deliverables directly linked to business objectives.
Detailed visibility to the business process is critical in determining the business case for its implementation. Hence,
the challenge for the enterprise executives sponsoring their RPA business cases is to determine processes that need
automation. This can drive ROI and carry value in automation, instead of just their amenability percentage.
Analysis of conventional RPA and
its limitations
RPA is one of the most highly discussed and adopted automation technologies across
industries and geographic markets. This disruptive technology has evolved the way
organisations work and operate. Although RPA’s advantages are widely known, some
of the key gains to be acquainted with are mentioned below:
• Decreased cycle times and improved throughput
• Flexibility and scalability
• Improved accuracy
• Effective utilisation of resources
To embrace more digitised ways of working, many organisations have adopted
robotics to automate repetitive processes. Now those organisations are seeking to
scale these solutions with AI to go beyond the routine to be innovative.i
Over the next three
years executives expect
automation to increase
their workforce
capacity by 27 percent−
equivalent to 2.4
million extra full time
employees.i
Limitation #1 - Identifying the right business processes to automate
“The market for automation technologies, such as RPA, is growing at 20 percent per
year and is likely to reach US$5 billion by 2024”.i
Although RPA offers many benefits and has become a must-have for organisations, a few limitations prevent the technology from
being at its finest. According to market research and the collective insights gained from the industry’s top analysts, vendors, and
customers, a few of the key automation limitations confronting enterprise executives in 2020 are mentioned below:

06
According to research and publications, the majority of enterprise automation initiatives have not scaled.
Although large organisations have abundantly invested in automation software (whether it has been process
discovery tools, RPA vendors, or a combination of both), scaling their automation programmes is still a challenge
due to the absence of an enterprise-wide charter and automation strategy. Most organisations do not have a well-
established centre of excellence for managing their automation programme, resulting in non-uniform information
flow and strategy adoption. Even organisations with a centre of excellence face challenges in socialising and
ensuring their guiding principles. These challenges make it difficult for organisations to scale programmes to
different functions and regions. For example, inconsistency in factors for evaluation of business case can lead to
acceptance and rejection in different geographies for the same process.
Hence, establishing an automation centre of excellence is critical for an organisation to standardise a framework
for deployment while ensuring that the technology initiatives drive value on the basis of their business case.
Limitation #2 - Scaling digital process automation programmes and operations
After implementing their digital workforce, large organisations often realise that their expectations are misaligned
with both enterprise constraints and visibility into critical business processes. Automated business processes tied
to evolving touchpoints, controls, or decisions have to be pulled from production, re-analysed, and then modified
before they can be operational again.
Parallel or conflicting digital initiatives have a major impact on digital transformation initiatives and related ROI.
With the accelerated advancement in technologies, organisations want to accelerate the implementation of their
digital programmes to use different capabilities. Hence, in some cases, a wide range of technological initiatives
are being worked out to achieve a desired goal. We have observed these initiatives often end up in a conflicting
stage wherein either of the technology initiative is barred to keep another. For example, assume there is a process
transformation initiative in progress using BPM capabilities. At the same time, there is another initiative targeted
to automate one of the processes of the same function using RPA. At some stage, the automated process will no
longer be of use due to the workflow or other changes brought in with BPM.
This does not only lead to re-work, but also limit scale. Executives need to find a way to better connect their
critical business processes with digital initiatives and evolving regulations.
Limitation #3 - Accounting for regulatory and enterprise constraints, i.e.,
conflicting initiatives

07
As already mentioned, the biggest challenge prohibiting large organisations to gain from their RPA efforts is their
inability or difficulty to apply automation at scale. A root cause for this is observed to be how the automation
projects are governed.
Different isolated teams and functions are taking ownership of separate business processes to automate. With
this distributed set-up, tracking their automation programme effectively is a challenge for organisations. Various
challenges lead to this difficulty that include no uniform technology stack utilised (i.e., different RPA tools for
different functions or initiatives, friction between functions, and their corresponding application environment).
Due to the lack of an effective governance to assess and analyse automation programme, organisations are
unable to target improvement areas. This in turn leads to low business user satisfaction. For example, assume a
business processes are automated for different regions of the same function and one of it is not being executed
per expectation. Lack of visibility to the process in other regions due to a difference in technology stack or
environment would lead to the shutdown of the inefficient process (which could have been easily avoided with a
proper governance platform).
This is creating significant dependencies and bottlenecks, leading to limited agility and effectiveness.
Limitation #4 - Effectively governing and monitoring automation
Straightforward automation of business processes eliminates the opportunity to make processes better by
identifying inefficiencies. This challenge is critical to the success of any RPA implementation.
We see several reasons why process mapping, analysis, and redesign work are essential to an effective RPA
implementation. The existing business process is often overly complex, with unnecessary steps that could be
eliminated before RPA is implemented. RPA involves the codification of business rules. However, in many cases,
business rules have not been examined for many years and do not make sense in the current environment. In
addition, existing business rules are sometimes described as requiring judgment, although they can be turned
into more-accurate and more-consistent algorithms for better, more-consistent decision-making.
With changes such as those mentioned above, a process optimised and automated using RPA can be efficient and
effective than that automated but otherwise unchanged process. Redesigning processes while implementing RPA
can have a negative impact on overall time and cost of the initiative. However, the ROI can be as great or greater
when compared with RPA implementations with no process change. Along with ROI benefits, holistic process
improvement enhances overall business user experience. It not only focuses on a single part of the process but
also on upstream and downstream activities.
Limitation #5 - Optimising business processes before automating them

08
Emergence of hyperautomation
Industries have witnessed some of the major fundamental
changes in the past that led them to reconsider and further
forced them to re-invent their ways of working to adapt
and evolve with the changing times and trends. Fueled by
innovation and taking steady steps towards technological
growth, these disruptive events have been instrumental in
shaping methodical changes across the world and developing
industries.
The first industrial revolution or the proto-industrialisation
period, powered industries and improved production
capacity by introducing mechanisation. The second one,
almost a century later, made way for massive technological
advancements in the industries that helped the emergence of
new sources of energy, electricity, gas, and oil.
After mastering the Mega, they were keen to explore the Micro.
Thus, the third revolution brought forth the rise of electronics,
telecommunications, and of course computers; devices that
accelerated industries’ growth like never before.
And Industry 4.0 is the latest age that has exponential
potentials. It is pushing new limits for industries to develop
and evolve with every passing second. Klaus Schwab, founder
and executive chairman at the World Economic Forum, in his
article, ‘The Fourth Industrial Revolution: what it means, how
to respond’, quotes –
“The Fourth Industrial Revolution, finally, will change not only
what we do but also who we are. It will affect our identity
and all the issues associated with it: our sense of privacy, our
notions of ownership, our consumption patterns, the time we
devote to work and leisure, and how we develop our careers,
cultivate our skills, meet people, and nurture relationships. It is
already changing our health and leading to a “quantified” self,
and sooner than we think it may lead to human augmentation.
The list is endless because it is bound only by our imagination.”
Industry 4.0, a global phenomenon triggered by a pool
of technologies of the modern world that have edged
their ways through the needs of users and evolved over
the decades as a result of consistent efforts and humble
collaborations. These technologies have thrived the long
list of requirements, while standalone, and are ready to
unite together to overcome the limitations poised by the
changing times.
AI – It enables
organisations to
become Insight-Driven
Organisations (IDO), which
rely on the fundamental
building blocks of
people, process, data,
and technology (using
an analytics strategy).
Strategic and tactical
options are assessed to
address key data and
analytics issues, risks,
and opportunities, as
well as define the AI
strategy in support of
new business models and
realise improved business
outcomes.
Intelligent automation–
It is used to increase
focus on high-value
business activities by
implementing automations
to emulate human actions,
engagement, interactions,
and judgments; using
robotic and cognitive
technologies; and
improving workforce
productivity and process
effectiveness.
Advanced analytics - The
power of data lies in the
way it is interpreted. We
work with organisations
across a wide range of
industries, in critical
parts of their business, to
empower data through
analytics. Analytics
supports decision-making
in the business domains
of customer, supply chain,
finance, workforce, and
risk, where we identify
actionable insights through
ML, predictive modelling,
and text mining.
Information management
– It involves a business-
driven approach to
designing and implementing
next-generation solutions
and processes that
support businesses
globally to better manage,
protect, share, and
innovate using their data.
Organisations can design,
develop, experiment,
and operationalise
effective Enterprise
Data Management
(EDM) solutions to drive
automation, advanced
analytics, and digital
capabilities.
Leading technologies in 2020 - Fundamentals of hyperautomation
Based on a report published by Deloitte in 2019, AI, ML, and intelligent automation are amongst the top 10 Industry 4.0
technologies that have the most profound impact on major organisations globally.iii
AI Intelligent automation
Advanced analytics Information management

09
However, each technology driver may have limitations as it
continues to evolve and improve with time.
With strategic goals to reap the potential of each technology,
revisit limitations, and deliver with best of the amalgam,
hyperautomation positions itself as a discreet enabler to the
wider spectrum of automation technologies.
Hyperautomation – Reimagine automation, redefine work
Hyperautomation is no more about mimicking rule-based
tasks performed with RPA. Conventional automation or RPA
was a foundation stone that has made way for users to explore
the broader meaning and greater abilities of automation. It
is about seamless interoperability of utilities and industry
applications.
One of the key differentiators of hyperautomation is its
ability to loop humans into the process. Using collaborative
intelligence, technology and humans work together.
Employees can begin to train automation tools and other
software. Through ML, they can get to a state of AI-enabled
decision-making. With hyperautomation, companies can
begin to reimagine work typically done by employees using
technology.
Some of the celebrated potentials of hyperautomation
01
Workforce enablement - Harnessed with the power of hyperautomation solutions, employees can
automate many processes within their roles and get more done faster with the resources available to them.
Minimising manual tasks enables them to focus more on impactful work, such as planning and strategy.
02
Employee upskilling - With automation no longer relies solely on IT, any business user can become an
automation leader within their own department, benefiting both tech- and non-tech minded employees.
03
Systems integration - With hyperautomation, a company’s clunky on-prem technology and disparate data
systems can communicate seamlessly.
04
Digital agility – When every form of automation works closely together, a company can move past the one-
off benefits of a single technology to a state of true digital agility and flexibility at scale.
05
ROI - Using key analytics, businesses can track the exact ROI realised (based on the processes automated,
departments optimised, and time and money saved every week, month, and year).

10
Hyperautomation – Roots to shoots
Hyperautomation is relatively new while the notion of
intelligent automation has been around for a while now. Given
its phenomenal growth and adoption, its growth and market
insights are impressive and promising.
Per Coherent Market Insights, the global hyperautomation
market is anticipated to grow at a CAGR of 18.9 percent
during 2020-2027 with extensive digitalisation of traditional
manufacturing plants being the primary contributor to growth.
Based on forecasts, hyperautomation is expected to pool a
global market cap of US$ 9.98 billion by 2022.iii
Per the survey conducted by Deloitte, executives estimate that
intelligent automation will provide an average cost reduction
of 22 percent and an increase in revenue of 11 percent over
the next three years. However, those organisations scaling
intelligent automation say they have already achieved a
27 percent reduction in costs on an average from their
implementations to date.i
Some of the sectors that are most likely to see a disruptive
impact of such technologies are healthcare, insurance,
travel, and tourism, and arguably the largest single employer
in most countries — the government. These sectors have
a preponderance of disparate legacy systems, myriad
intermediate players and processes, and some form of an
intelligent cognitive input required in decision-making and
delivery. These factors together make these sectors quite
attractive for hyperautomation.
This poses an interesting dilemma. On the one hand, the
speed of delivery for goods and services will significantly
improve with consistently reliable results (after adopting
such technologies). However, on the other hand, the
disintermediation of humans in routine tasks and non-critical
decision-making will have a real and sustainable negative
impact on employment numbers (given that these sectors
collectively represent a significant share of the total workforce).
Some jobs, and perhaps some supporting functions, will cease
to exist.

11
Hyperautomation ecosystem
Hyperautomation refers to a combination of automation tools
with multiple ML applications and packaged software used to
deliver work. RPA is just one subset of the key technologies
helping to drive hyperautomation. Other technologies include
intelligent Business Process Management Suites (iBPMSs),
integration Platform as a Service (iPaaS) platforms, and
decision management systems. Together, they provide a robust
toolbox of technologies that enables hyperautomation.
Hyperautomation Ecosystem
Machine Learning
The RPA market is facing market disruptions as heavy RD
investments continue to flow in to redefine services and
solution offerings. New offerings with a broader reach, new
vendors, and new commercial models are emerging rapidly to
redefine the market. This will lead to a revitalisation that is far
beyond simple task-based RPA.
Robotic Process
Automation
Artificial Intelligence
Process Mining
Business Process Management(iBPMs)
Analytics
Chatbot
Introduce machine learning
to augment the scope of
automation enabled by RIA
Improve core operations by using insight-driven business rules
to drive automation across functions, sub-functions
Enhance your user experience by
leveraging cognitive capabilities of
Conversational AI
Amplify your process streamlining goals and
benefit realization with in-depth understanding
Define key metrics for success and align insights from
the execution of an RPA deployment directly to the
impact on business outcomes.
Manage and streamline your enterprise-wide
automation initiative by leveraging workflow
capabilities using BPM tools
Measure and demonstrate the ROI of
automation and its impact based on business
outcomes that matter to your company.
Cutting-edge technologies have come together to complement RPA and build an
ecosystem for hyperautomation (i.e., creating a new way to work using a unified
approach beyond ‘simply RPA’).

12
RPA
Process mining
AI
•
AI is a brilliant way to augment automation of processes and a robot's abilities. The most common applications of AI are in
incident management, case management, contract management, legal processes, etc. The following different types of AI
can be used for process automation:
–
Optical Character Recognition (OCR) - It is used to extract text from images and documents via mechanical or
electronic means. OCR in RPA enables organisations to automate a greater volume of their operational business
processes, especially those that still highly depend on scanned paperwork, such as customer-completed forms.
–
Natural Language Processing (NLP) - In RPA, NLP analyses structured, unstructured, and “semi-structured”
documents to identify, extract, and structure data within them for further analysis. Applications of NLP include invoice
processing, insurance claim handling, contract analysis, voice of customers, and voice of employees. Integration of
NLP and RPA helps companies improve customer experience by measuring the sentiment in the text.
–
Chatbots - They apply NLP to interact with users, understand their intent, and respond to them based on the
assessment of their queries. This helps employees focus more on critical customer requests requiring personal
interaction, reduction in incoming traffic of queries, and faster resolution.
–
ML - It applies AI capabilities to lend business context to tasks executed by RPA systems, enabling the latter to make
better decisions and be more productive. It builds a knowledge base based on historical data and uses it for future
decision-making. The amalgamation of RPA and ML establishes a symbiotic relationship of continuous improvement
between execution and analysis.
• RPA is a non-invasive integration technology used to automate routine, repetitive, and predictable tasks. This technology
can help organisations with their digital transformation. Some of the top applications of RPA include customer service,
accounting, financial services, healthcare, human resources, and supply chain management.
• Highlights
– Enabling better customer service
– Ensuring business operations and processes comply with regulations and standards
– Allowing processes to be completed much more rapidly
– Providing improved efficiency by digitising and auditing process data
– Creating cost savings for manual and repetitive tasks
– Enabling employees to be more productive
•
Process mining is designed to discover, monitor, and improve real processes by extracting knowledge from event logs
readily available in application systems. It includes automated process discovery, conformance checking, and other
advanced analytics features by integrating the BPM and RPA platforms.
• Highlights
– Intelligent support for process model enhancement by deep data-driven insights
– Identify process inefficiencies at a granular level for focused automations and quick wins
– Auto generation of simulation models and process flows for faster documentation
– Conformance checking to rapidly conduct a root cause analysis

13
iBPMS
Intelligent BPM Suites (iBPMS) have a solid foundation of tools for orchestrating processes and automating tasks within those
processes. They (iBPMS) consolidate integration services, decision management, process orchestration, ad hoc processes,
and advanced analytics into a single platform. The right orchestration is needed to unify bots, applications, and people to
ensure that the automation results are as planned.
Advanced analytics
Applying advance analytics to RPA to generate data can assist organisations to unlock operational and business insights, gain
unprecendented knowledge of the RPA programme performance, and build a roadmap for the future.

14
Identify right opportunities
for automation; redesign
processes to reap maximum
benefits from execution; and
follow in-depth assessment
to finalise a pipeline for
automation.
Imagine Deliver Run
In this phase, an
automation solution is
devised and developed.
Further, the solution is
deployed on the production
environment.
This phase determines
how well an organisation
realises the benefits
mentioned in the business
case and plan further
steps for the automation
programme
Delivery approach for
hyperautomation
COVID-19 had a significant impact on the way we deliver technology solutions, leading us to re-think and design an evolved
approach to delivering automations. Despite having hyperautomation capabilities, thriving in this time has never been easy.
Three major phases of the delivery approach are mentioned below.
We have articulated a high-level delivery journey enabled by hyperautomation mentioned below.
The new normal with intelligent automation | Deloitte’s point of view
9
Review
Comments
Reference
documents
Satisfied
workforce
Constant
improvement
Real-time
monitoring
Virtual and
hassle-free
automation
delivery
experience for all
stakeholders
Real-time monitoring of
automation performance
to ensure continued
optimisation
We have developed a codified and efficient automation lifecycle enabled by our virtual automation delivery playbook
Deloitte’s Automation Delivery Playbook -
Automation lifecycle
Start
End
Meetings to conduct initial
opportunity assessment
through virtual
collaboration tools
Details of shortlisted
processes are captured
through process
discovery tools
Processes are analysed
through process mining
tools to identify and capture
automation benefits
Imagine Deliver Run
Create and track
‘Book of work’
within pipeline
management tools
Client provides sign off
to initiate production
deployment
Automated process
is deployed on
cloud-based
infrastructure
with scalable
configuration
The development team initiates
and completes development over
cloud-based tools enabled with
virtual collaboration
along with testing
Solution quality
is ensured using
automated review
tools and User
Acceptance Testing
(UAT) is conducted
Shortlisted
Process List
Process
Documentation

15
Some of the key benefits of this redefined approach are mentioned below:
01
03
02
04
In-depth assessment of processes with data-based process assessment
Effective prioritisation for process automation determined by key metrics
Ease of tracking initiatives with cloud-based and platform agnostic tracking tools
Central and real-time reporting, leading to one-place monitoring of the entire automation programme

16
Way forward
The strength of intelligent automation comes to the fore when RPA combines with AI to enable applications that go beyond the
routine to the innovative: from collecting and processing data to analysing and making contextual decisions. However, based on a
survey by Harvard Business Review, a significant number of survey respondents (48 percent) admit to neither thinking about nor
implementing an intelligent automation strategy that includes AI. Another 36 percent include AI in their strategies but not at scale.
Only 11 percent companies are currently scaling solutions that include AI.ii
The potential of hyperautomation is huge. Problems that could have been solved yesterday using human intelligence, can be
solved tomorrow using digital twins. Just as the complexity of electronic circuits had doubled in a given period of time (Moore’s
Law), automation technologies will also grow exponentially. As advancements in different technologies ended up complementing
each other and forming hyperautomation, we look forward to a similar amalgamation in the future.
Nevertheless, with the technological capabilities currently available with hyperautomation, organisations have the capability to
entirely transform their core business operations and be a part of Industrial Revolution 4.0. Considering the evolving nature of
the business environment and the ongoing COVID-19 situation, adopting automation has become critical. Hyperautomation is a
collection of various complimenting technologies and it often leads to the creation of a digital twin of the organisation that can
support business operations in parallel to an organisation’s physical workforce.

17
Connect with us
Acknowledgement
Jaidev Dutta
Sudarshan Cheripalli
Tushar Gupta
Shreya Gupta
Avish Sharma
Shivam Satyam
Deloitte Touche Tohmatsu India LLP
Nitin Agrawal
Partner, Consulting
nitinagrawal@deloitte.com
Ravi Mehta
Partner, Consulting
ravmehta@deloitte.com
Confederation of Indian Industry (CII)
D Ramakrishna
Chairman, CII, Andhra Pradesh

18
i
How Companies Are Using Intelligent Automation to Be More Innovative - SPONSOR CONTENT FROM DELOITTE (hbr.org)
ii
https://hbr.org/2018/06/before-automating-your-companys-processes-find-ways-to-improve-them
iii
https://www.coherentmarketinsights.com/market-insight/hyper-automation-market-3754

https://www2.deloitte.com/content/dam/Deloitte/in/Documents/technology-media-telecommunications/in-tmt-IoT_
Theriseoftheconnectedworld-28aug-noexp.pdf

https://www.lexalytics.com/lexablog/text-analytics-nlp-rpa-use-cases

Deloitte Report: “The Fourth Industrial Revolution – At the intersection of readiness and responsibility”
References

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