Intelligent automation exploring enterprise opportunities for systems that do think and learn cognizant

Intelligent Automation: Exploring
Enterprise Opportunities for
Systems that Do, Think and Learn
To compete in an era of globalization and fast-moving
business change, organizations need to apply smart
technologies, which can reduce costs, increase scalability,
improve accuracy, boost speed and make better use of
human efforts.

Executive Summary
Businesses today exist in an era of globalization, hyper-competitiveness and ever-accelerating
business cycles. To succeed in this environment, many enterprises are focusing on the top-level
objectives of reducing operational complexity, increasing agility and speeding innovation while
lowering the associated costs. Of all the various levers organizations can pull to achieve these
goals, smart technologies — which we call intelligent automation — are among the most versatile
and effective.
Opportunities abound for applying intelligent automation (see Figure 1). In many organizations,
multiple processes continue to require manual workarounds, which results in smart people
spending their time on limited-value, rote and repetitive manual process steps. These processes
can be found in back offices, processing centers and anywhere else that an interface has been
built between legacy and digital front-end applications.
Compared with many other approaches — such as wholesale system migrations, new ERP
solutions or even many process re-engineering efforts — intelligent automation can be imple-
mented quickly, with limited cost. The value of intelligent automation includes reduced cost,
increased scalability, improved accuracy and consistency, faster processing, better use of human
efforts, improved compliance and audit trails, and faster ROI.
Exploring the Possibilities of Intelligent Automation
Deriving insight from mountains of data,
text, images, video and audio to improve
pricing, diagnostics, sourcing, trading, etc.
Personalizing real-time
customer interactions to align
with time, place and language.
Dynamically adjusting outcomes for
multiple variables (such as location,
weather, time of day, relationship
history, spending limits, past
preferences, group trends, etc.).
Eliminating errors, mistakes and
miscalculations from rote,
keystroke-dependent processes.
Building always-on borderless
operations, delivery and
production capabilities.
Accelerating innovation and
agility by untethering human
resources from mindless and
repetitive tasks and pairing
them with smart technologies.
Figure 1
2 KEEP CHALLENGING September 2016

INTELLIGENT AUTOMATION: EXPLORING ENTERPRISE OPPORTUNITIES FOR SYSTEMS THAT DO, THINK AND LEARN 3

Introducing Systems that Do, Think and Learn
Our point of view on the future of enterprise automation is represented by our
“Do-Think-Learn” Intelligent Automation Continuum. Simply put, Do-Think-Learn
categorizes automation technologies into three horizons (see Figure 2).
Systems that Do
Organizations today are investing much time and effort in the first part
of the automation continuum: systems that “do” (see Figure 3). This
type of automation is best represented by robotic process automation
(RPA), which is the on-ramp for many just getting started with intel-
ligent automation. Automating processes with most RPA tools is more
like creating traditional flowcharts than writing code, especially when
screen and keystroke recorders are employed to make it even easier.
Once built and tested, object libraries of automated tasks can easily be
reused or quickly customized to make future automations go faster.
Meanwhile, teams of “virtual RPA workers” can be scaled up or down
instantaneously or, even better, autonomously, as task volumes ebb
and flow.
RPA is far more flexible and secure than macros, scales quickly and
is relatively low-cost compared with traditional business process
management systems or process re-engineering studies. Business
users with very modest developer skills can automate many types of
work processes in just weeks or a couple of months at most. Getting
the same thing done using traditional automation technologies could
take overloaded IT teams multiple quarters to complete — assuming
they could even get the project funded and scheduled.
Systems that Think
Whereas today’s RPA systems can work only with structured inputs
and hard-coded business rules, the next level of automation – systems
that think — are able to execute processes much more dynamically than
the first horizon of automation technologies (see Figure 4). The big
advantage with automation technologies that think is the introduction
of logic, which allows these programs to make decisions autonomously,
or on their own, when they encounter exceptions or other variances in
the processes they execute.
If you look at IT service automation as an example, these systems
are able to analyze a user-generated request or trouble ticket for key
Replicate repetitive
human actions
Handle
judgment-oriented tasks
Learn to understand context,
and adapt to users and systems
Systems that
DO
Systems that
THINK
Systems that
LEARN
Intelligent Automation Continuum
Figure 2
Robotic
Process
Automation
Speech to
Text
Conversion
Data
Collection &
Preparation
Systems that
DO
Figure 3
Autonomic
Automation
Natural
Language
Processing
Smart
APIs
Systems that
THINK
Figure 4

words or other triggers; then, based on embedded algorithms and logic, they can
then make decisions as to how to best prioritize and address each case. Even better,
over time their performance improves as they develop comprehensive histories of
resolution data, which they are able to access to improve future decision making.
These thinking systems deal far more effectively with less defined processes and
unstructured data, and in this way, they differ from RPA or other systems that “do,”
which operate best in defined, rules-based processes.
Natural language processing (NLP) is another example of an automation techno-
logy that thinks. Natural language processing is a fast evolving form of software
automation that can interpret spoken or written communication and translate it
into executable actions that can be made by the computer system. Smartphones
increasingly rely on NLP for hands-free use, and call centers increasingly deploy
NLP-based automated agents to help them handle more calls with greater efficiency,
scale and consistency.
Systems that Learn
Looking at the third horizon in our Intelligent Automation Continuum,
systems that learn, we see a range of fast evolving technologies char-
acterized by their ability to analyze vast amounts of dynamic and
unstructured input, and execute processes that are highly dynamic
and non-rules-based (see Figure 5). As an example, machine learning
is improving the diagnostic capabilities of medical imaging systems;
allowing online retailers to create highly individualized catalogs; and
improving the way software companies test for security vulnerabilities
in future application releases.
These learning systems are also adaptive, in the sense that they can
apply one set of rules in one situation, but when variables change —
such as location, resource availability or suspicious activity — they can
make adjustments optimized for the new situation. In the enterprise
world, imagine these systems that learn running in tandem with your
research and development teams, sales organizations, manufacturing
and logistics operations or customer service departments.
Data-intensive processes and decisions predicated on understanding several
complex variables and large volumes of information could move at machine
speed and produce far more accurate, reliable and timely results. The impact —
from financial trading systems, to real-time pricing engines, to patient care, to
completely individualized insurance programs — is enormous and is just beginning
to be recognized by early adopters.
What is important to understand in terms of the Intelligent Automation Continuum
of today, is that every organization has vast opportunities to apply all the
technologies of systems that do, think and learn to improve business processes,
accelerate outcomes, increase data quality and enable powerful and predictive
analytics (see Figure 6, next page). Yet, even more compelling than this digitization
of work that these technologies provide is their ability to improve the human role of
organizational operations. People are now more empowered than ever to do what
humans do well — which is to think creatively, solve problems, prioritize and interact
with clients, partners and coworkers in smarter, more productive ways than ever
before possible.
Sentiment
Analysis
Artificial
Intelligence
Machine
Learning
Systems that
LEARN
Figure 5

Looking Beyond Do-Think-Learn
By 2017, here’s where we believe organizations will be, in terms of their adoption of
intelligent automation:
• Embedded intelligence becomes table stakes. Imagine your set top box without
a DVR or your smartphone without a voice-activated personal assistant. Expect
the same transition to occur in automation, as today’s “systems that do” vendors
build or buy their way to smarter technologies. This change will make imple-
mentations faster and easier, extend applicability to more dynamic processes
and improve outcomes by creating fewer exceptions, improving output data and
further compressing cycle times.
• Do-Think-Learn becomes Think-Learn-Adapt. As the technologies that enable
intelligence become more pervasive across the ecosystem, the “systems that
do” horizon will become narrower and less useful.
The “systems that think” category then becomes the
entry tier as learning systems become mainstream.
By mid-2017, the new horizon three will be “systems
that adapt,” meaning systems with the self-awareness
to not only learn but to also decide autonomously
how to apply that learning to provide smarter, more
effective outcomes.
In truth, business leaders today have no choice but
to acknowledge that automation in all its forms is
already playing a role in their organization’s future
(see Figure 7, next page). Taking a wait-and-see stance is not an option when
systems that do, think and learn are already fast at work differentiating com-
petitors, as markets and whole industries reposition themselves for the Fourth
Industrial Revolution now upon us.1
Opportunities across the Continuum
Figure 6
Applications for
Systems that Do
• Claims Processing
• Accounts Payables /
Receivables
• Record or Account Data
Reconciliations
• Data Consolidation /
Validation
Applications for
Systems that Think
• Service Desk Incident Resolution
• Complaint Management and
Resolution
• Network Security Management
• Customer Service and Support
Applications for
Systems that Learn
• Prescriptive Pricing Engines
• Virtual Service Agents
• Portfolio and Investment Services
• Retail Engagement Systems
• Clinical Diagnostics
• Optimized Trading
Business leaders today have
no choice but to acknowledge
that automation in all its forms
is already playing a role in their
organization’s future.

Moving Forward With Intelligent Automation
To help chart their path forward, organizations should consider three approaches
to move toward adopting intelligent automation.
1. Think Big; Scale Fast
With this approach, organizations identify automation as a top strategic initiative
across their entire enterprise. They also appoint an experienced executive to
assume the role of automation leader, with the responsibility of accelerating the
adoption of intelligent automation simultaneously across both IT and business
operations. A life sciences organization we work with has taken this approach; the
company has established a joint internal/external team of automation experts but
plans to ultimately formalize its own internal automation practice that is capable of
moving at the speed and coverage the business believes will be necessary.
The team first prioritized just three unique processes that allowed the organization
to begin developing experience in several different “do, think and learn” technology
categories. Based on those early learnings, the team has now developed an
automation roadmap for the next 12 months that will encompass more than three
dozen process areas across five different functions.
2. A Winning Partnership
No one organization has the corner on all things automation. Businesses will need
to turn to a partner to help them quickly take advantage of potentially game-
changing intelligent automation technology offered by small, emerging vendors.
We worked with a multinational financial services company to deploy an intelligent
automation product across large volumes of back-office transactions. We bench-
marked the vendor’s technology, validated its capabilities and then supported the
Rethinking Industries for Intelligent Automation
Banking and Financial Services Healthcare and Life Sciences Retail and Consumer Services
“Given the sheer amount of
data generated in the financial
industry, both by established banks
and disruptive fintech start-ups,
automation is becoming a crucial cog
in this global machine.
“A recent Citi report estimates that
bots now manage approximately
$20 billion of assets globally. Going
by current trends, this could one day
increase to as much as $13.5 trillion.
Additionally, in the increasingly
influential area of cryptocurrency and
blockchain ledgers, ‘smart contracts’
are continuously written and signed
from one AI bot to another, to
instantly articulate and verify every
financial transaction.”
Silicon Republic, April 18, 2016
“‘What if a system could draw a
connection between this particular
patient and to some previous cases
dealt with by other physicians at other
hospitals or other historic cases in the
system. That could be made possible
by an artificial intelligence system,’
said Eric Xing, a professor at Carnegie
Mellon University.”
ZDNet, April 27, 2016
“‘Machine learning has the potential
to become a powerful tool in plastic
surgery, allowing surgeons to harness
complex clinical data to help guide
key clinical decision-making,’ writes
Dr. Jonathan Kanevsky of McGill
University, Montreal.”
Wolters Kluwer, April 30, 2016
“As retailers and brands predict and
plan for the way consumers will shop
in the future, artificial intelligence is
high on the business development
strategy for 2016 and beyond.
“Within retail, intelligent assistance
is a rapidly developing area of AI
that has the potential to change the
consumer experience, as it uses big
data combined with natural language
interfaces and machine learning to
provide human-like interactions. The
assistants are quicker than their
human counterparts and can analyze
vast amounts of data in seconds.”
IT Pro Portal,
March 20, 2016
Figure 7

effort of designing, testing and deploying the automation solution into the client’s
operations.
By leveraging a partner’s industry expertise and specific knowledge of the
company’s operations, processes and systems, businesses can reduce the risk of
integrating leading-edge automation technolo-
gies into their environment.
3. Automation On-Demand
Some organizations have no intention of
becoming automation experts themselves but
still want the benefits of these technologies. We
worked with a healthcare payer to implement an
as-a-service approach to quickly and accurately
process out-of-network claims. In just weeks, the
automated claims system was in place, and the
intelligent agents eliminated a backlog of 8,000 claims in just five days, at 99%
first-pass accuracy. Today, the solution handles every out-of-network claim for this
provider. The always-available automated agents not only determine who should
be reimbursed but also complete all necessary documentation to ensure the health
plan pays the right party for each and every claim.
Getting Started
As suggested earlier, the promise of intelligent automation is real, and it’s here now.
Business leaders should begin developing plans now to understand the present and
Business leaders should begin
developing plans now to understand
the present and future opportunities
and begin charting the path forward
for their organizations.
Cognizant’s Rapid Intelligent Automation Framework
Understand Assess Design Plan Execute Transition Support
Interview key
stakeholders.
Assess pain
points.
Capture use
cases or
requirements.
Understand
short-term and
long-term goals.
Conduct
automation
ideation.
Review/
document
as-is processes/
systems.
Identify
opportunities
for automation.
Perform gap
analysis.
Scope the
initiative.
Develop scoring
approaches and
prioritization.
Complete a
dependency
assessment.
Design a
solution/
architecture.
Assess
document
requirements.
Plan for the
pilot.
Develop the
resource/
asset plan.
Create a budget.
Develop the
governance and
charter.
Plan the
communications
and cadence.
Create the
project/
work plan.
Determine
change request
management.
Create a
responsibility
assignment
matrix.
Execute the pilot
according to the
plan.
Leverage
best practices.
Conduct
reporting.
Ensure clear
communication.
Adhere to strict
change control
process.
Conduct
contingency
planning.
Follow change
management
plan.
Ensure
knowledge
transfer.
Create
supporting
documentation.
Develop a
knowledge
repository
(Wiki).
Prepare for
initiative
handoff.
Ensure
maintenance/
warranty
support.
Conduct regular
checkpoint(s).
Complete
customer
satisfaction
survey.
Figure 8

future opportunities and begin charting the path forward for their organizations
(see Figure 8, previous page). Keep in mind that the full continuum of “do, think
and learn” technologies will have a role in your organization’s new digital delivery
and operational models, and be prepared to adjust and evolve as this automation
ecosystem is sure to continue doing so as well. Most important, now is the time to
start your organization on the automation journey so you too can begin experienc-
ing the benefits of process acceleration, greater efficiency, quality gains and the
unleashing of people and work teams from rote rule books to begin collaborating,
creating and improving results like never before.
To get started, businesses should abide by the following guidelines:
• Automate first. Rather than looking at wholesale system changes, process engi-
neering or complex studies, companies will realize that intelligent automation can
be tried, tested and either fail or scale in very short cycles. So why not automate
first and start capturing the benefits right away? In parallel, you can take the
time to consider the costlier and more complex approaches to create efficiency.
• Automate ambitiously. Intelligent automation will span many technology
approaches and address all sorts of process challenges, from high-volume to
low, complex to simple, structured data to unstructured, and rules-based to
dynamic. Companies will take a multidimensional approach to applying intelli-
gent automation, and will apply it ambitiously and in parallel with back- middle-
and front-office processes.
• Automate with purpose. The ability for intelligent automation to drive new
types of outcomes will be well understood by mid-2017. That awareness and
insight will form the goals by which implementation success will be benchmarked.
The incremental successes delivered today by rudimentary levels of intelligent
automation — systems that do — won’t be enough anymore.
Footnotes
1 http://www.weforum.org/pages/the-fourth-industrial-revolution-by-klaus-
schwab.
To read more about intelligent automation and other advanced technologies, such
as IoT, cloud, mobility and security, see our Digitally Cognizant blog http://digitally.
cognizant.com/digital-technologies/automation/.
About the Author
Matthew Smith leads the Automation Venture for Cognizant’s Emerging Business
Accelerator organization. His responsibilities include automation strategy,
enablement and market communications. In this role, Matt works closely with
Cognizant internal automation and related technology practices, client-facing
teams and leading external providers of automation, AI and other cognitive technol-
ogies. Matt has a bachelor’s in business administration from Stetson University. He
can be reached at matt.smith@cognizant.com. | Twitter: https://twitter.com/_Mat-
thewJSmith | LinkedIn: https://www.linkedin.com/in/matthewjsmith01/ | Flipboard:
Automation, AI and the Digital Enterprise | Blog: http://www.matthew-j-smith.com/
category/robotic-process-automation/.

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