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Infosys Insights | External Document © 2017 Infosys Limited
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CONTENTS
04 THE CORNER OFFICE
-Towards a Future with Purposeful AI
06 ON THE COVER
- Cracking the AI Conundrum
12 IDEAS AND BEYOND
- Taking AI Where the Human Brain Goes
- Better at Being Human, Thanks to AI
26 TECH TALK
- AI Tames the Complexity of Regulation in Financial Services
- Moving the Goalposts: AI and Logistics
- You can Focus on Care, not Costs, Thanks to AI
46 RENEW-NEW
- Where’s the Safety Net for Digital Refugees?
- Cautious Optimism Marks the Adoption of AI at Proximus
- Prediction Puts the Smart in Smart Retail
66 RESEARCH UNRAVELED
- On the Trail of Machina Economicus

4
While debates about job automation and
ethos of artificial intelligence (AI) continue,
technology stalwarts — individuals and
organizations — pursue AI advancement,
collectively giving it a purpose that can
eventually make our lives better and solve
some grand challenges facing the planet.
Google just launched a massive open AI
division, making available on cloud its Tensor
Processing Units, which can accelerate both
training and running machine-learning
(ML) models, for developers. An American
software firm, Cylance Inc., leverages AI,
algorithmic science, and ML to prevent
cyberattacks. Tesla has achieved considerable
success in building capabilities to prevent
collisions for automated vehicles. At Infosys,
we continue to build on the capabilities of
Infosys Nia, our next-generation integrated
AI platform. Besides helping our clients in
faster revenue reconciliation (CPG), better
forecasting (pharma), and dynamic fraud
analysis (banking) among others, Infosys
Nia is also bringing significant automation-
led productivity improvements to delivery
efficiencies across our own service lines. In the
last 12 months, it has helped us eliminate the
efforts of around 11,000 full-time employees,
thereby helping us repurpose those people
into more valuable and meaningful tasks.
Organizations across the board are
acknowledging the potential of AI. In a recent
study commissioned by Infosys,‘Amplifying
Human Potential: Towards Purposeful Artificial
Intelligence’, out of the 1,600 IT and business
TOWARDSA
FUTUREWITH
PURPOSEFULAI
The Corner Office

decision-makers interviewed, 76 percent
cited AI as fundamental to the success of
their organizations’strategy and 64 percent
believed organizational growth is dependent
on large-scale AI adoption. A majority (84
percent) plan to train employees on the
benefits and use of AI, and 80 percent plan
to retrain or redeploy impacted employees.
In this issue of Infosys Insights, we present
various perspectives on the larger purpose of
AI to help you trace meaning across massive
technology-driven changes, which is beyond
complete comprehension for most of us.
On the cover, Abdul Razack tells you why
adopting AI should not be about choosing
one or two from a list of
AI techniques, but using a wide range.
Because, today, the true potential of AI
is unfathomable.
In the Ideas and Beyond section, the spotlight
is on ideas, inspirations, and pursuits that
could inform the future. Kris Gopalakrishnan,
cofounder of Infosys and Chairman of Axilor
Ventures, elucidates the second AI wave — on
why the confluence of human intelligence
and AI will amplify growth and ensure
sustainable progress. In the other article,
Abhijit Shroff, Senior Principal, TechArch,
focusing on next-gen cloud and
AI platforms, knocks over the fear and
paranoia surrounding AI — about robots
controlling humans.
In the Tech Talk section, we get down to brass
tacks across three industries. Mohit Joshi,
President and Head of BFSI, Healthcare, and
Life Sciences, Infosys, explains why and how
ML can eliminate all regulatory compliance
woes tangled in complexity for decades.
Madhu Janardhan, Head of Retail, CPG,
and Logistics (Americas), Infosys, writes
about the potential transformation of the
logistics industry through big data and AI.
Venky Ananth, Vice President, Healthcare,
Infosys, captures how the industry is coming
face to face with automation and AI and what
could be a road map to start the journey.
In the Renew-New section, we focus on the
duality of renewing the past and exploring
the new in the time of being digital.
Is there a safety net for‘digital refugees’?
Holly Benson, VP and Managing Partner at
Infosys Consulting, ponders on this question
with some insightful data. In the next
article, Dominique Leroy, CEO of telecom
major, Proximus, converses with Rajesh
Krishnamurthy, President and Head of
Energy, Utilities, Telco, and Services, Infosys,
explaining why the company is cautiously
optimistic about adopting AI. Then, Aniket
Maindarkar, tells you what constitutes‘smart’
in smart retail.
In the final section, Research Unraveled,
Dr. Martin Prause, Professor of Computational
Economics (CE) and Business Analytics at Otto
Beisheim School of Management, Germany,
highlights the applications of CE in today’s
business landscape and how AI can improve
business simulations.
I hope this issue provides you with a rich
outlook on AI and helps you make informed
decisions for your business.
Towards a Future with Purposeful AI
Ravi Kumar S.
President and Deputy COO, Infosys

6
On the Cover
6

Cracking the AI Conundrum
In his seminal book‘The Society of Mind’,
cognitive scientist and artificial intelligence
(AI) guru Marvin Minsky constructs a model
of human intelligence step by step, built
up from interactions of simple parts called
‘agents’that are themselves mindless. These
agents are akin to the various AI techniques
which have matured today, such as machine
learning, deep learning, natural language
processing (NLP), and artificial neural
networks. But collectively, these agents hold
the potential to create great outcomes, but
when taken individually, they are pretty
‘mindless’and not a patch on the whole.
So when enterprises adopt AI, it is essential
to have a sense of purpose – to use all
the AI techniques needed – not just
individual agents.
Let’s look at a business scenario that illustrates
this idea. Let’s say, a manufacturing business
is under pressure to increase yield. So, it has
identified in its 15-step supply chain process,
one step that is currently consuming four
hours. The organization is excited about
applying a certain AI technique to this step
and improving the process significantly.
Cause for celebration yet? Not really, because
the company could instead, create a high-
impact solution by taking a comprehensive
approach towards increasing yield – by
applying a bunch of AI techniques.
These could help them identify the known
and the unknown problems surrounding
the issue of inadequate yield, make
interconnections among them, read hidden
patterns, and gather insights. The true
potential of AI is unfathomable. The key is to
continually embrace AI by experimenting,
learning, and building on successes.
Today, two important developments have
precipitated an environment for AI techniques
to rapidly develop and thrive, empowering
organizations to adopt AI:
ABUNDANCE AND AVAILABILITY
OF DATA: With IoT creating colossal
amounts of structured and unstructured data,
organizations have unprecedented access
to data. The more the data that is fed into a
machine learning algorithm, the sooner it
‘stabilizes’and improves accuracy.
According to Ovum Research, machine
learning will be the biggest disrupter for
big data analytics in 2017.
SUPERCOMPUTING POWER AT A
REASONABLE COST: Neural networks have
existed for decades, but it is only now that
CRACKINGTHE
AICONUNDRUM
The various techniques of AI, or what Minsky calls‘agents’are by
themselves of little use. But when used together, with a clear
purpose, they have the potential to create great outcomes.This is
what enterprises have to aim for, as they undertake the AI journey.

8
On the Cover
massive computing power is available at a
reasonable cost, which in turn has helped
increase the number of‘layers’in these
networks. Each‘layer’adds more intelligence
but also consumes enormous computing
power, which earlier used to be
prohibitively expensive.
More layers mean better outcomes.
This is what is commonly referred to as
‘deep learning’.
Beyond automation: AI is about
problem finding and problem
solving
A piecemeal approach to AI amounts to
turning the clock back by underutilizing its
true power. Kvelling upon the automation-
driven scale and cost efficiencies is
appreciating only a fraction of the picture
− being myopic really. To tap into the true
potential of AI, it is important for businesses
to change their approach to problem finding
and problem solving.
There are essentially two kinds of problems
that a business needs to solve:
Known problems – These have been known
to us for years but it is only now that we
have the technology to tackle them and
achieve tangible results. Some examples
are fighting money laundering and financial
fraud, ensuring better regulatory compliance,
medical diagnostics, analyzing customer
sentiment, and so on.
Unknown problems – These are problems
that have not manifested themselves yet.
Problems that we don’t even know exist.
But these are‘problems’nevertheless.
The pervasive technology revolution today
compels us to look beyond and push the
boundaries, as technology-led innovations
and disruptions are upending business
models and creating new paradigms. Often,
going after the familiar problems may not
lead to the best solutions. On the upside, AI-
driven techniques such as predictive analytics
and forecasting − of revenues, possible snags,
behavior of suppliers, partners, and clients
in a certain context, cost of products yet to
be built, and so on – are enabling deeper
understanding, discovering problems, and
creating better solutions for them.
At Infosys, we believe that a comprehensive
approach to adopting AI – spanning both the
known and the unknown problems – albeit
in a step-by-step manner, is the way forward.
We have adopted Design Thinking across
the board to address these two categories of
problems and create sustainable solutions for
our client ecosystem. True to the principles of
Design Thinking that pivots on the end-user,
our clients are integral to this journey of
problem finding and problem solving.
Our next-generation AI platform, Infosys
Nia, leverages open source and consolidates
various AI tools and techniques to create a
‘society of mind’, if you will. Infosys Nia brings
onto a single platform, big data / analytics,
machine learning, knowledge management,
cognitive automation capabilities, end-to-end
RPA capabilities, optical character recognition
(OCR), natural language processing (NLP)
capabilities, and infrastructure management
services. It enables a wide set of industry-
and function-specific solutions and allows
customers to build bespoke experiences to
suit their business needs.
It is important to deal with the known problems first and build credibility, especially
among the skeptics in an organization, through early results.
AI is never about a piece of technology. Rather it is a new paradigm altogether that
needs to be treated thus.
There should be dedicated focus and investment in problem finding, an area that is
inextricably related to realizing the true potential of AI.
A JOURNEY INTOTHE UNKNOWN

While the power of AI lies in problem solving, it will allow humans to focus on problem finding!

10
On the Cover
Addressing larger issues,
amplifying human potential
While automation and AI are transforming
bottom-line-focused, data-rich industries
such as retail, manufacturing, and telecom,
as well as touching others such as healthcare,
insurance, and financial services, these
technologies are also enabling massive
collaborations across organizations and
industries. Some of these are the result of new
business models and disruptions, and others
are driven by the need for the greater good.
Many technology leaders and visionaries
believe that AI has the power to address
some of the biggest problems facing us – as
humans, on planet Earth.
Google DeepMind focuses on trailblazing
work in machine learning with the aim
of bringing about an‘artificial general
intelligence’, to harness AI for grand
challenges. CEO of DeepMind, Demis
Hassabis, says:“If we can solve intelligence
in a general enough way, then we can apply
it to all sorts of things to make the world a
better place.”
OpenAI, a non-profit AI research company,
backed by Tesla and SpaceX founder,
Elon Musk, is aimed at‘advancing digital
intelligence in a way that is most likely to
benefit humanity as a whole.’
Although concerns around machines
overtaking humans may exist, human
creativity and imagination will triumph.
AI will relieve us from the mundane and
the cumbersome, liberating us to focus on
finding the most relevant problems to
solve. To achieve our true human potential.
We live in extraordinary times, where
technology empowers us to envision a future
where we can sync our brains with artificial
neural networks, yet at the same time ponder
upon the possibility of job loss and AI systems
going rogue. It is a time when all enterprises
will embark upon the AI journey and learn
along the way. The more we iterate and the
more we learn, the better the results will be.
So as we take the initial steps, it is important
to acknowledge the potential of AI and build
a purposeful platform for it.
It is important to deal with the known problems first and build credibility, especially among the
skeptics in an organization, through early results.

Abdul Razack
In a career that spans over two decades, Abdul has been involved in
several engineering and consulting roles at Commerce One, Sybase,
KPMG Peat Marwick, and SAP. Abdul holds a Master’s Degree in
Electrical Engineering from Southern Illinois University, and a
Bachelor’s Degree in Electronics and Communication Engineering
from the University of Mysore, India.
About the Author
If you wish to share your thoughts on this article or seek more information, write to us at Insights@infosys.com

12
IDEAS
AND
BEYOND

14
The human brain has advanced over time in
responding to survival instincts, harnessing
intellectual curiosity, and managing demands
of nature. When humans got an inkling about
the dynamics of the environment, we began
our quest to replicate nature.
Our success in imitating nature has
been related to advances in science and
technology. Take for example, our aspiration
for flight. We replicated wings to achieve safe
and long-haul air travel. However, we are
aware that inflexible aircraft wings are not
an exact replacement, and a likely solution
may lie in the Self-Assembly Laboratory at
the Massachusetts Institute of Technology
(MIT). It is developing a 4D printing technique
to create aircraft wings that adapt to
aerodynamic conditions.
While the human brain finds ways to exceed
our physical capabilities, the combination
of mathematics, algorithms, computational
methods, and statistical models is accelerating
our scientific pursuit. Artificial intelligence
(AI) gathered momentum after Alan Mathison
Turing developed a mathematical model for
biological morphogenesis, and authored a
TAKINGAIWHERETHE
HUMANBRAINGOES
Ideas and Beyond
The human brain — intelligent and unique — is challenging scientists, who
are determined to decode its complexity and unlock possibilities to enhance
human lives. By harnessing artificial intelligence (AI), they have already made
breakthroughs in man-machine interactions throughWatson, Siri, and more.
But for AI to have a truly transformational impact, artificial neural networks
need to be further reinforced by human native intelligence.

Taking AIWhere the Human Brain Goes

16
Ideas and Beyond
seminal paper on computing intelligence.
Today, AI has grown from data models for
problem solving to artificial neural networks
– a computational model based on the
structure and functions of human biological
neural networks.
Teaching the machine
The first generation of
AI created machine-
learning systems. Machine
learning focuses on the
development of computer
programs that can change,
or learn, when exposed
to new data. Algorithms
from the first generation
of AI‘taught’machines to
identify images and objects,
see obstructions, correlate, and discover
relationships between variables. It resulted in
intelligent applications that managed single
tasks at a time.
AI makes industrial machinery accurate,
reliable, and self-healing; and paves the way
for calibrated performance resembling human
action. Modeling techniques locate indecisive
voters, identify crops that are most suitable
for a specific topography, and verify clinical
diagnosis and treatment. AI integrates with
robotic controls, vision-based sensing, and
geospatial systems to automate advanced
systems. It enhances disease prevention and
treatment, boosts engineering systems, and
drives self-organizing supply chains. As of
today, AI provides near-human customer care
at the Royal Bank of Scotland, and assesses
insurance claims at Fukoku Mutual
Life Insurance.
In fact, we now rely on machines for decision-
making across processes — underwriting,
recruitment, fraud detection, maintenance,
and more. Real Core Energy uses machine-
learning algorithms that evaluate production
and performance parameters to guide
oil-drilling operations as well as investment
decisions. Gift concierge service 1800-Flowers.
com uses AI to recommend gifts. It combines
customer interaction with macro buying
trends and consumer behavior to recommend
personalized gifting ideas. Philips has
developed a deep-learning-based, automatic
screening solution to detect tuberculosis, a
disease that affects 2.5 million people in India.
The human race conceded to artificial
intelligence in move #37 of the game
between Lee Sedol, the world champion
of Go, and AlphaGo in Seoul, South Korea.
Experts took weeks to understand the
‘wisdom’of the AlphaGo machine.
The structure of artificial neural networks
is inspired by the human
nervous system. It helps‘train’
machines to make sense of
speech, images, and patterns.
DeepFace, the Facebook
facial recognition system, was
trained to recognize human
faces in digital images by
using millions of uploaded
images. Researchers at MIT
have developed a model
for facial recognition that
duplicates the neurological functions
of the human brain.
Machines learn to think
Computational neuroscience bridges the
gap between human intelligence and AI by
creating theoretical models of the human
brain for inter-disciplinary studies on its
functions, including vision, motion, sensory
control, and learning.
Research in human cognition is revealing a
deeper understanding of our nervous system
and its complex processing capabilities.
Models that offer rich insights into memory,
information processing, and speech / object
recognition are simultaneously reshaping AI.
A nuanced understanding of the structure
of the human brain can help restructure
hierarchical deep learning models. Deep
learning, a branch of machine learning,
is based on a set of algorithms that attempt
to model high-level abstractions in data.
It will enhance speech / image recognition
programs and language processing tools by
understanding facial expressions, gestures,
tone of voice, and other abstracts. We are
on the threshold of experiencing advances
in speech technology that will lead to more
practical digital assistants and accurate facial
recognition that will take security systems to
the next level.
However, contemporary deep-neural
networks do not process information the way
Today, AI has grown from
data models for problem
solving to artificial neural
networks based on
human biology.

the human brain does. These networks are
highly data-dependent and should be trained
to accomplish even simple tasks. Complex
processes require large volumes of data to be
annotated with rich descriptors and tagged
accurately for the machine to‘learn.’ Further,
deep-learning systems consume far more
power than the human brain (20 watts) for the
same amount of work.
We need to discover less intensive machine-
learning approaches to augment artificial
intelligence with native intelligence.
Our world is awash with data from Internet
of Things (IoT) applications. Deep-neural
networks capable of consuming big data for
self-learning will be immensely useful. But just
as children identify trees despite variations
in size, shape, and orientation, augmented
intelligence systems should learn with less
data or independently harness knowledge
from the ecosystem to accelerate learning.
Such self-learning algorithms are necessary
for truly personalized products and services.
The interface imperative
The merger of human intelligence and AI
will turn computers into super-humans or
humanoids that far exceed human abilities.
However, it requires computing models
that integrate visual and natural-language
processing, just as the human brain does,
for comprehensive communication.
Language-learning skill is one of the defining
traits of human intelligence. Since the
meanings of words change with context,
‘learning’human language is difficult for
computers. AI-embedded virtual assistants
can address complex requests and engage in
meaningful dialogue only when they‘think
and speak’the human language. Machines
should learn to understand richer context for
human-like communication skills. They should
be endowed with richer cognitive capabilities
to interpret voice and images correctly.
AI systems such as IBM’s Watson, Amazon’s
Alexa, Apple’s Siri, and Google Assistant will
become more useful, if enhancements to the
quality of language and sensory processing,
reasoning, and contextualization are achieved.
Voice-activated devices and smart machines
will create a centralized, artificial-intelligence
network or‘intelligent Internet,’which will
redefine man-machine and machine-
machine collaboration.
In the near future, drones with built-in
navigation systems will deliver goods in
crowded cities, and smart home appliances
Just as children identify trees despite variations in size and shape, augmented intelligence systems
should learn with less data or independently harness knowledge from the ecosystem.

18
Ideas and Beyond
Experts took weeks to understand the ‘wisdom’ of the AlphaGo machine.
will translate recipes, assemble ingredients
in response to voice commands, and serve
gourmet meals.
Of course, as computers become more
powerful, more networked, and more human,
they become capable of independent
interaction with stakeholders. However,
creativity and strategic thinking differentiate
the human race from artificially intelligent
entities. Today, we do not fully understand
concepts that make our intelligence unique.
We need to know more deeply how the
human mind operates, as a means to
incorporate emotional and social intelligence
into machines. Human beings will continue
to control everything until machines become
self-referential systems. Till then, we must
revisit our ecosystem, which spans education
systems, skill-development processes, and
social welfare models to make way for more
efficient methods.
AI systems will be a force multiplier for every
industry and human activity. AI can transform
billions of lives via myriad applications, and
solve fundamental issues: clean the air we
breathe, purify the water we drink, enrich the
food we consume, and ensure our wellness.
All it needs is that the person-to-machine user
interface mimics the brain-to-brain interface.
The world will be a better place for successive
generations when technology works in
transformative and invisible ways. While the
native intelligence of the human race has
produced inventions that are ubiquitous,
the confluence of human intelligence and
artificial intelligence will amplify growth
and deliver sustainable progress.

Kris Gopalakrishnan
Chairman, Axilor Ventures; Chairman, Itihaasa Research and Digital;
and Cofounder, Infosys
Senapathy ‘Kris’ Gopalakrishnan served as the Vice-Chairman
of Infosys from 2011 to 2014, and the Chief Executive Officer and
Managing Director of Infosys from 2007 to 2011. Kris is one of the
cofounders of Infosys.
Recognized as a global business and technology thought leader,
he was voted the top CEO (IT Services category) in Institutional
Investor’s inaugural ranking of Asia’s Top Executives, and selected
as one of the winners of the second Asian Corporate Director
Recognition Awards by Corporate Governance Asia in 2011. He was
also selected to Thinkers 50, an elite list of global business thinkers,
in 2009. He was elected president of India’s apex industry chamber,
the Confederation of Indian Industry (CII), for 2013–14, and served
as one of the co-chairs of the World Economic Forum in Davos in
January 2014.
In January 2011, the Government of India awarded Kris the Padma
Bhushan, the country’s third-highest civilian honor.
He now serves on the Board of Governors of the Indian Institute
of Technology Madras, and the Indian Institute of Management
Bangalore, is the Chairman of the Board of Governors of IIIT
- Bangalore, and is on the Board of Trustees of the Chennai
Mathematical Institute.
Kris holds master’s degrees in physics and computer science from
the Indian Institute of Technology Madras.
About the Author

20
Over the past few years, a growing tribe of
AI doomsayers has made several predictions
on the threat of AI. The prophesies include,
among others, an impending war by killer
robots and the need for humans to become
cyborgs to stay relevant in the age of artificial
intelligence (AI). Statements such as these
are not limited to technology leaders.
For instance, a prominent theoretical physicist
has warned that“AI could spell the end of the
human race.”Additionally, a British inventor
publicly voiced his concerns about how the
human race will be unable to survive against
more intelligent machines.
This may seem far-fetched to many of us.
As humans, we are all programmed to be
aware and cautious as an in-built safety
mechanism – but not to the point of being
paranoid. Paranoia merely breeds panic and
serves no purpose other than to impair the
human condition. Humans are curious and
positive creatures. This ability – to feel and
find happiness in the simplest of things –
without proven and accurate data is a human
privilege. It gives us a unique advantage
against any AI tool, robot or cyborg.
To be fair, some of the concerns voiced by
the global community are certainly well-
founded. For instance, the CEO of a high-tech
automaker raised valid concerns that“AI will
follow the will of the people that establish its
utility function.”In other words, he worries
that AI can be dangerous in the wrong
hands. To counter this, the industry needs
BETTERATBEINGHUMAN,
THANKSTOAI
Ideas and Beyond
Fear and paranoia about where AI is headed helps no one.
A positive outlook, combined with a strong foundation to make
innovation safe – through cooperative governance, will
help us amplify human potential – not destroy it with AI.

Better at Being Human,Thanks to AI

22
Ideas and Beyond
robust regulatory frameworks and stringent
governance that ensure innovations in AI
are not hijacked for unethical or criminal
purposes. Microsoft’s AI bot, Tay, and its
series of racist comments on Twitter is a
classic example of how AI software may
repeat user behavior gleaned from public
data, particularly when there are no filters.
Thankfully, Tay has been taken offline now
and Microsoft is working on upgrades.
A Case for Stronger Controls
However, incidents such as the corruption
of Tay may be giving AI
an undeserved ticket
to hell. All innovations
have teething troubles.
In the early days of cloud
computing, security was the
key concern. The question
in everybody’s mind was:
Is my data safe on the
cloud? Certainly, phishers
and hackers were lurking in the shadows,
waiting to steal sensitive data. But, this fear
did not stop businesses from leveraging
the transformational benefits of cloud. Very
soon there were strong regulations making
it mandatory for cloud service providers to
adhere to certain standards. Today, security is
no longer a major issue in cloud transitions.
The same can be said of AI. With the right
controls, why fear the power of big data and
the value of deep AI? With proper regulations,
big data and deep learning can only serve
to amplify human potential, propel the
global economy forward, and improve the
overall human condition. Interestingly, many
organizations are making committed strides
in this direction – to ensure that AI innovation
is safe. OpenAI, a non-profit AI research
company, has been established to understand
AI-centric governance and ensure that AI
is used safely. Recently, a group called the
Partnership on Artificial Intelligence to Benefit
People and Society – comprising Google,
Facebook, IBM, Amazon, and Microsoft has
been formed to establish best practices, and
conduct and publish research about all
AI-related topics.
AI can Make Us More Human
When touting the benefits of technology,
it is important to have a well-rounded
and unbiased view. Consider for example
that today, after decades of technology
revolutions, 9.6% of the world’s population
continues to live in dire poverty. Despite all
the technological advances that the world has
seen in these past decades, a large proportion
of the global population is still struggling
to survive. Technology, largely, has thus far
served the haves. AI has the potential to take
the benefits of technology and learning and
transform the lives of those who have been
left behind – by the sheer scale, speed, and
accuracy it can bring to global and large-scale
initiatives. AI offers a great opportunity to
provide a civilizational upgrade to the masses
by allowing us to apply our innate qualities
of‘empathy’and‘compassion’
to improve the lives of have-
nots. Freed from repetitive and
mundane tasks, humans will
be able to harness the power
of knowledge on-demand to
address daily job problems.
AI can help them discover
opportunities to truly advance
their unique traits of imagination, creativity,
and a quest for purpose. In doing so, we will
be able to solve bigger problems such as
hunger, disease, ignorance, and poverty.
AI, in fact, holds the promise of helping us
become more human.
The Power of AI
The current definition of a‘job’is a flawed one.
Most of today’s jobs are nothing but mundane
economic tasks that need to be fulfilled.
The dynamic human mind is capable of much
more. If anything, it needs to feed on creative,
imaginative, and innovative challenges such
as the socio-philanthropic condition. This is
where the power of AI comes in.
Here’s an example: It’s 2017. Tom and Dave
are operations engineers with traditional
‘jobs’. Tom is a subject matter expert (L2
function) while Dave is a dispatch / routing
agent (L1). This two-person team is trained to
understand insights generated through big
data and ensure smooth operations. In future,
this whole process can be replaced by a single
person who can not only leverage analyzed
data but also predict issues and recommend
corrective actions. This will be enabled by a
combination of cloud (for real-time actionable
insights) and digital natives (to execute the
recommended action). The end result: Higher
operational productivity and better efficiency
for the business. What about Tom and Dave
then? Tom becomes a knowledge worker
who finds ways to enhance the underlying
Many organizations
are making committed
strides in this direction
– to ensure that AI
innovation is safe.

Paranoiamerelybreedspanicandservesnopurposeotherthantoimpairthehumancondition.

24
Ideas and Beyond
IT operations, optimize robotic execution,
and enable intelligent decision-making using
digital natives. And, instead
of simply relaying and
executing actions, Dave is
now free to find better ways
to add value to the company
and realize his true potential.
Dave could be creating the
next‘uber’marketplace for
his enterprise.
What about the robots?
With deep learning, the robots can learn to
think and act based on data knowledge trees
and human action knowledge trees. They can
be taught to understand how human and
business events influence economic activities,
thereby empowering them to generate
better output.
Consider how AI can deliver value to BPO
employees who handle customer complaint
calls, bank tellers who dispense cash, or toll
booth operators who issue tickets. Imagine a
future where healthcare providers leverage
their digital avatars to provide trusted real-
time care when required, without having to
visit ERs. AI and automation will bring about
significant and positive disruption in the
lives of such people by encouraging them to
adopt value-generating roles. In fields such
as oilfield drilling and space exploration,
where calculations are massive and extreme
precision is critical, AI and
Deep AI can offer advanced
predictions, helping engineers
improve drilling processes and
enabling astrophysicists to make
ground-breaking discoveries.
The possibilities for digital
natives and AI are endless:
They can positively transform
banking, finance, agriculture,
government operations,
healthcare, drug research, and clinical trials,
to name just a few areas.
Bottom Line
In conclusion, it is evident that AI can
help machines serve humanity better by
creating a harmony between digital and
biological intelligences.
We can safely put away our fears of a
dystopian world or a post-apocalyptic future
where humans serve robots. As we empower
robots to take on some‘jobs’, we will dedicate
ourselves more emphatically to improving
and enhancing the human condition.
That is where the true power of AI will be
clearly seen.
We can safely put
away our fears of a
dystopian world or
a post-apocalyptic
future where humans
serve robots.
Human and artificial intelligence have to work together, as a team, complementing their abilities
to realize human potential.

Abhijit Shroff
AVP and Senior Principal Technology Architect, Infosys
Abhijit is an Associate Vice President and Senior Principal
Technology Architect with Infosys, having around 19 years
of experience in the industry. He has led emerging and niche
technology business practices, built products, and led complex
technology programs in the space of HPC, Big Data, Operations
Autonomics, and Hybrid Cloud Computing.
He is currently focusing on building and delivering the next-
generation AI platform to help clients reimagine their business
and IT operations landscape. His interests include adoption of
technology for impacting social and economic transformation.
About the Author

26
TECH
TALK

27
External Document © 2017 Infosys Limited | Infosys Insights

28
This is the scale of regulatory complexity
that banks face today. It arises primarily
from three sources:
DATA QUALITY: Compliance teams are
presented with enormous quantities of
internal and external data emanating from
customer records, financial transactions,
phone calls, emails, and activity logs.
But ironically the information doesn’t always
serve their purpose. When surveyed during
a webinar on regulatory trade reporting,
33 percent of participating firms said their
biggest compliance challenge was getting
hold of the right kind of data, while
24 percent said it was the quality of data
itself. Under such circumstances, the
ecosystem becomes vulnerable to fraud
and policy violations.
REGULATORY OVERLOAD: 2015 alone saw the
addition of 20,000 new banking regulatory
requirements. In an ever-tightening
regulatory milieu, banks have to deal with
onerous and copious legislations in all the
geographies they are present in. This not only
entails huge effort, but at times also creates a
conflict between jurisdictions.
PRODUCT DATA SILOS: Some cases of fraud
come to light only upon examining additional
data outside the transaction itself or in a
cross-product view. For instance, a credit
card transaction that looks aboveboard
might not be so when seen in conjunction
with the customer’s account information.
Unfortunately, compliance managers rarely
get the opportunity to access a cross-product
view – that is, a comprehensive picture of risks
AITAMESTHECOMPLEXITY
OFREGULATIONIN
FINANCIALSERVICES
TechTalk
By 2020, financial institutions will be staring at more than 300 million pages
of regulations, way beyond what employees will be able to handle.
Currently, banks spend approximately US$270 billion each year – which is
10 percent of the total operational spending of big banks – on regulatory
compliance. Even the average credit union spends the equivalent of one
employee’s time for every four employees, on meeting regulatory obligations.

AITames the Complexity of Regulation in Financial Services

30
and potential violations – because product
data in banks typically resides in silos.
Managing this complexity takes a fine
balancing act. Set the reporting threshold too
low (that is, widen the scope of documents
under regulatory purview) and end up with
an exponential increase in false positives;
lift the threshold too high and risk a breach.
Through experience, most banks seem to
have settled on a workload that occupies
about 15 percent of their workforce.
If that seems excessive, imagine the situation
in three years, when the 300 million pages of
regulations prediction comes true. With the
reins slipping away from humans, the only
option before banks is to leverage automation
technologies, from machine learning and
natural language processing to deep learning,
and manage the burgeoning scale
and complexity.
Of course, banking
compliance is no
stranger to technology.
With improvement in
capability and reduction in
cost, biometric technology
has been instrumental
in fraud prevention. I expect fingerprints
and retinal scans to be soon widely applied
in authenticating card transactions. And
although biometric technology is largely
independent of artificial intelligence today,
we can expect to see visual recognition
systems working in the background to
identify users in the near future.
Most financial institutions have also been
using some levels of automation to perform
reporting and compliance-related tasks for a
few years now. To cite an example, banks are
leveraging readymade platforms and toolsets
to automate the entire life cycle, from data
gathering to report submission.
Now it is the turn of Regtech to introduce
some of the disruptive technologies in
financial services to the realm of compliance;
technologies such as big data analytics,
APIs, and machine learning, which are
modeling risks, tracking capital compliance,
and cracking down on money laundering
and other malpractices. Of these, I consider
machine learning a significant marker in the
progress of technology adoption from simple
automation to evolved forms of artificial
intelligence. Machine learning – the ability
of computers or smart devices to learn from
data without being programmed – is making
its way into banks, identifying deviant activity
patterns from big data across products and
asset classes, and developing and testing
algorithms to proactively manage risk
and fraud.
Machine learning is far better equipped
than human beings for this role because
not only can it handle massive amounts of
data and a very large number of variables
for analysis, it can also spot correlations
that are not apparent to us. For instance, a
risk manager on finding a large drawdown
might investigate a few indicators such as the
location or the time of transaction, and other
similar parameters that are known to us. The
machine, on the other hand, is not limited by
the confines of human understanding − it will
look further, even at hundreds
of apparently unconnected
variables for its analysis, and
garner insights.
Machine learning, by its sheer
power of speed and scale, has
many everyday uses such as
reviewing documents. Last year,
JPMorgan Chase employed machine learning
to review commercial loan agreements, which
used to occupy staff for 360,000 hours every
year. The bank is not only saving enormous
manual effort and time, but is also reporting
lower error rates. JPMorgan Chase is now
planning to deploy machine learning in more
complex areas, such as credit default swaps
and custody agreements.
The next frontier for machine learning, in
conjunction with other AI technologies,
especially natural language processing and
robotics, is to comprehend regulations,
decide if a certain action is in violation,
and answer queries. Today, two bankers in
different parts of the world could interpret
the same text differently based on one’s
individual experience and regulatory context.
With natural language processing making
significant strides, there is the possibility
of using AI to read and codify regulatory
information into a single source of truth.
In such a scenario, the two bankers based
in different parts of the world only need to
query the AI system, through a chatbot for
example, and they would both receive an
identical interpretation of the text.
Imagine the situation
in three years, where
by some estimates, we
may have 300 million
pages of regulations.
TechTalk

Today, two bankers in different parts of the world could interpret the same text differently, based on
experience and context. But with a natural language processing-powered chatbot, they can soon
receive an identical interpretation of the text.

32
TechTalk
Financial institutions should make sure that the compliance teams don’t lose their grip on
things once machines start taking decisions.
While this may take three to five years to
unfold into the mainstream, Infosys is already
working with a client to digitize their global
standards and compliance handbooks on
our knowledge-based AI platform. Users
can query the platform about a particular
transaction in English and a chatbot will tell
them if it is permitted or not.
So what will AI’s impact on banks and
financial institutions in the near future be?
As stated earlier, approximately 15 percent
of a bank’s workforce is engaged in risk and
compliance activities. At the top are the policy
makers, liaising with regulators and industry
bodies, and working closely with the bank’s
top leadership. These essentially‘human’
roles will be irreplaceable by machines.
In the middle are those responsible for
evaluating risks and managing exceptions,
and so on. Most of them will continue in their
jobs, but might need to upgrade some skills.
The bottom tier of the compliance staff
performs voluminous and repetitive day-
to-day tasks, which will be automated.
The average compliance workforce will shrink
by half in the next three to five years.
Understandably, this will spark job security
concerns. My belief is that while jobs will be
lost to automation, some staff can be trained
and upgraded to fill vacant middle-tier
roles, instead of the organization recruiting
afresh. Others can be deployed in new roles,
where they can use their knowledge to solve
compliance problems, train new hires, or
participate in Regtech innovation.
Research corroborates this approach.
When Infosys recently surveyed 1,600 senior
leaders in IT and business functions for their
views on AI, 84 percent said they plan to train
employees on the benefits and use of AI,
and 80 percent of respondents plan to retrain
or redeploy impacted employees. AI is an
opportunity for enterprises to conduct
more purposeful pursuits, starting by
amplifying the ability and achievements
of their human resources.
There is a flipside to AI adoption in regulatory
compliance. Banks have to be careful not to
allow machine learning and other AI systems
to develop biases, which could negatively
impact the quality of decisions. They should
also make sure that the compliance team
doesn’t lose its grip on things once machines
start taking decisions. Last but not the least,
they would have to convince regulators that
their intelligent machines are doing at least
as much due diligence in compliance
screening, if not more, as the human beings
before them.

Mohit Joshi
President, Head – Banking, Financial Services, & Insurance (BFSI), Healthcare
and Life Sciences, Infosys, and Head – Infosys Brazil and Infosys Mexico.
Mohit is responsible for firm-wide sales operations and reporting
processes, including large deal pursuits and top account growth.
He has over 18 years of professional experience working across
the US, India, Mexico, and Europe. His area of expertise lies at the
intersection of financial services and technology. He was also
selected as a Young Global Leader (YGL) by the World Economic
Forum, Davos, earlier this year.
He joined Infosys in 2000 and has since worked in various
capacities. In 2007, Mohit was appointed CEO of Infosys Mexico and
was instrumental in setting up the first subsidiary in Latin America.
He has previously worked in India with ABN AMRO and ANZ
Grindlays in their corporate and investment banks.
Mohit holds an MBA from the Faculty of Management Studies,
Delhi University, and a bachelor’s degree in history from
St. Stephen’s College, Delhi.
About the Author

34
Logistics accelerates the movement of goods
and provides a fillip to global trade. Logistics
enterprises can ensure the smooth flow of
commodities and finished products over land,
sea, and air. These enterprises function in a
borderless world, connect the dots between
the central hub and spokes, streamline
processes, and ensure that business
users are empowered to make timely and
informed decisions for logistics to function
with clockwork precision. The catalyst for
this synchronicity is the voluminous data
harnessed during the journey of goods from
origin to destination. However, enterprises
face situations such as shipments that are
misdirected, stuck in transit, or that cannot
be traced. Machine learning can convert this
logistical challenge into an opportunity.
In fact, the compounding effect of big data
and AI can create hyper-efficient logistics
enterprises operating in a smart ecosystem.
Remote sensing, the Internet of Things (IoT),
telematics, and geospatial mapping embed
information into products and the vehicles
transporting them – the origin, destination,
journey, and the recipients of goods. So the
physical flow of materials across the supply
chain leaves a trail of data – which is usually
in an unstructured format and scattered
MOVINGTHEGOALPOSTS:
AIANDLOGISTICS
TechTalk
Artificial intelligence (AI) is going mainstream because of two
parallel events – unprecedented availability of computing power
and business outcomes from analyzing big data.This article
explores how AI models capitalize on big data to reinvent logistics.

Moving the Goalposts: AI and Logistics

36
across the ecosystem. This is a sweet spot
for AI, which depends on large volumes
of data to extract knowledge and learn
through self-analysis.
The logistics industry can use AI tools to
integrate data from diverse sources, devices,
and systems, and distill business insights,
letting them evaluate how road, air, rail, and
ocean carriers, freight forwarders, third-party
logistics (3PL) firms, and enterprises
make informed decisions and operate
more profitably.
Last-mile efficiency
Route optimization rationalizes the cost of last
-mile delivery, which is a significant overhead
in logistics expenditure. AI algorithms can
leverage historical trip sheets and real-time
statistics to estimate the delivery time for
each shipment. Continuous analysis improves
the accuracy of projected delivery. Data-
driven operating models are already helping
food distributors such
as Sysco, retailers such
as Walmart and Tesco,
and logistics providers
like UPS, DHL, and FedEx
provide same-day delivery
– the Holy Grail in B2C
logistics service.
AI platforms optimize the
route of every delivery
vehicle in real time.
Streams of geographical, environmental,
traffic, and shipment data are correlated
with designated delivery time windows and
vehicle information to sequence deliveries
and generate the best route for each
shipment. The next best point of delivery or
a modified route can be calculated based on
constraints / events, and shown on a live map.
The most optimal delivery route is shared
with the driver via the on-board navigation
system of the vehicle – during the journey.
Data-laden dashboards help logistics
facility managers take informed decisions
by monitoring the performance of drivers,
specific facilities, and the enterprise networks.
Real-time visibility into key performance
indicators such as units moved per hour for
each category of product / parcel / pallet,
average vehicle speed, and total travel
time, help benchmark and improve
service planning.
Network optimization
Since omnichannel marketing is a business
imperative for enterprises, the location and
layout of warehouses need to be reoriented
for anytime, anywhere delivery. Big data
helps enterprises, government agencies,
and lending and economic development
institutions determine the location of the
distribution infrastructure. The World Bank
is using big data optimization methods to
develop a multi-modal transport network in
India. Open spatial information helped create
and validate a pilot model for identifying
locations of multi-modal ports.
AI-driven analytical tools help logistics
providers aggregate customer demand
and simplify distribution networks, while
managing inventory. Intuitive systems
optimize the distribution network and
ensure smooth warehouse operations by
instantly mapping capacity and availability
of equipment as well as manpower with
workload, and providing
visibility across warehouse
and transportation
processes. An analytical
approach improves stow
accuracy and maximizes
the usage of assets,
including conveyors and
rack systems.
Logistics companies can
improve productivity as
well as resource utilization at warehouses
and distribution centers by capitalizing
on predictive AI algorithms and analytics.
Predictive maintenance of trucks, conveyors,
forklifts, and trailers rationalizes warehousing
and distribution costs.
Real-time data from automated materials
handling systems and equipment in the
facility enhances operations of large
warehouses. Optimizing the route for clamp
trucks and forklifts handling inbound and
outbound cargo expedites movement, saves
fuel, and ensures safety.
Amazon uses big data to manage 1.5 million
items sold through a complex network of
fulfillment centers, redistribution centers,
regional sortation centers, delivery stations,
and Prime Now Hubs.
Schmitz Cargobull, a German
trailer and truck body
manufacturer, monitors
maintenance requirements,
cargo transported, and
delivery routes of trailers to
minimize vehicle breakdown.
TechTalk

Route optimization rationalizes the cost of last mile delivery, which is a significant overhead in
logistics expenditure.

38
TechTalk
Algorithms can help maximize capacity utilization despite variability in demand for B2B
and B2C shipments.
Freight consolidation
AI models offer insights into products, and
volume and number of shipments – by
location, customer, season, mode of freight,
preferred delivery time frames, and transport
prerequisites such as ambient temperature
or humidity. It helps logistics enterprises
consolidate shipments to reduce transit time,
control costs, and improve customer service.
Significantly, it maximizes capacity utilization
despite variability in demand for B2B and B2C
shipments. Small parcels can be converted
into Less-Than-Truckload (LTL) shipments, and
LTL freight into minimal stop truckloads.
Advanced logistics applications integrate
simulation and AI to help logistics service
providers implement cost optimization
strategies. Damage claims can be analyzed
across delivery routes and modes of transport.
It supports rate negotiations for high-risk
cargo, and enhances damage mitigation
approaches. Rule-based AI solutions detect
fraud and errors by tracking supply chain
events and documents.
Avnet, a small-parcel distributor of electronic
components, leverages more than 250 million
data values from 5 million annual shipping
transactions to identify carriers presenting
invoices with errors, and analyze spend to
defer delivery and save costs.
Resource utilization
Automated systems track pickup and delivery
orders, job schedules, and crew availability to
assign work, manage the fleet, and streamline
the logistics network. Machine learning
systems deliver long-term value by predicting
constraints in the ecosystem and mitigating
process bottlenecks in real time.
AI rules evaluate job priority, cargo type,
weather, traffic, and resource capabilities to
make business decisions about movement
of freight. Load pooling rationalizes pricing
as well as the cost of operations. In addition,
it helps air, land, and ocean logistics service
providers maximize resource utilization,
including manpower, cargo handling
equipment, transport vehicles, and space.
Self-learning systems and AI frameworks
will be an integral part of logistics solutions
even as autonomous vehicles and drones are
adopted for last mile delivery, and alter the
distribution landscape.
An AI resource allocation engine optimizes
daily schedules and manages engineering
as well as maintenance activities for rail lines
in Hong Kong. In addition, the Hong Kong
Airport Authority deploys an AI scheduling
system to allocate parking slots to aircraft
based on flight schedules and shifts in
operational dynamics.

Madhu Janardan
Senior Vice President and Region Head, Americas – Retail, CPG,
and Logistics, Infosys
Madhu started his career with Infosys in 1996. He has over 26
years of experience in defining strategic business solutions and
IT consulting for several companies in Japan, Europe, and North
America. Madhu oversees several retail and CPG accounts in the
US and has expertise in developing and maintaining strong client
relationships. With his collaborative approach to innovation, he
has led teams to consistently deliver client delight, in challenging
business–technology environments.
Madhu has a master’s degree in computer applications from
the Regional Engineering College, Trichy, India (now a National
Institute of Technology). He enjoys cooking and hiking, and is active
in the community, teaching at the local university. He also runs
a private trust that focuses on education and healthcare for the
underprivileged in India and some parts of Africa.
About the Author

40
Although Americans spend far more on
healthcare than any other high-income
country, this spend is incommensurate to
the health outcomes they experience.
Life expectancy is shorter than desired and
chronic conditions are still widely prevalent,
among other things. Analyzing this high
healthcare spend reveals several underlying
reasons: an ageing population, rising use of
new expensive technologies in radiology,
new procedures, increase in prescription
drug costs (e.g., for Hepatitis C), high
pharma marketing spend, and a growing
number of specialties.
In addition, digital and AI-led disruption,
which could bring about significant cost
and process efficiencies have been relatively
slow in this industry, which has also been
traditionally slow to adopt new technologies
owing to complexities arising out of multiple
ecosystem players and intricate regulations.
However, consumers are now demanding
more from healthcare. As digitization sweeps
across industries, consumer technologies
have been growing in popularity, especially
in retail and banking. These consumers are
also increasingly demanding better pricing
transparency, connected experience across
payors, physicians, hospitals, pharmacies,
and other supporting institutions, all of which
play an integral role in the care continuum.
And the healthcare industry is beginning
to respond.
YOUCANFOCUSON
CARE,NOTCOSTS,
THANKSTOAI
TechTalk
AI in healthcare can complement the workforce and amplify their capabilities.
Human capital in healthcare needs to be diverted to care at the intersection
of caregivers and patients rather than be involved in low-value, back-office
operations to support front-line caregivers. AI-led automation is ushering in
this great opportunity today.

You can Focus on Care, Not Costs,Thanks to AI

42
In 2014, the market for AI in healthcare was
worth over US$600 million, and this figure
is expected to rise ten times by 2021.
Today, healthcare is widely believed to be
one of the industries destined for AI-led
transformation, which will be an antidote
for the high costs, as well as an enabler for
better health outcomes and experiences.
For some years now, a chatbot at Aetna
named Ann has been providing round-
the-clock assistance to new members in
using its website, guiding them through
the registration process, or helping them to
recover user names and passwords. Similarly,
at Credit Agricole, chatbot Marc responds
to product queries in the company’s health
insurance space and makes relevant offers
to customers after analyzing their needs.
The potential of robots in superseding
humans at the front office is enormous.
Deploying Robotic Process Automation (RPA)
and AI in health technology
can help deliver a rich
and seamless experience
for all participants in the
care continuum. Such a
model will pivot around
the member, enabling her
to navigate the healthcare
ecosystem to derive the
best possible care. In this
article, we share our view
on the impact of these
technologies on business.
Choosing wisely, but
acting quickly
Enthusiasm over RPA and AI has caused a
proliferation of solutions in the healthcare
space. While the perfect solution might be
elusive, the best way forward is to choose
a solution that is most effective for the
organization. This however, invariably leads
to the bigger challenge – to secure support,
technical and otherwise, for RPA and AI
initiatives, especially at the grassroots level,
and ensure robust governance around them.
It is important to identify the processes best
suited for automation, conduct pilots to
demonstrate quick wins, and then trigger a
virtuous cycle where success breeds success.
Automating broken processes, however, can
not only be inefficient but also dangerous.
Processes must be rationalized, optimized,
and simplified before automation.
The good news is that unlike traditional IT
projects, which run for several years, an RPA /
automation project life cycle lasts for six to
ten weeks – from ideation to implementation.
In our view, RPA is the right place to
commence an automation journey, laying
the foundation for more sophisticated
AI deployment.
Doing more than just paring
down costs
RPA and AI will have a far-reaching impact
on healthcare, well beyond their potential
to save costs or reduce labor, although
these two benefits are currently paramount.
By eliminating duplicate processes and
automating member support processes,
RPA will make it much simpler for members
to avail of services of healthcare companies,
even as it enables them to complete
transactions faster, benefiting all stakeholders.
For example, leveraging automation through
a combination of RPA and
AI, a healthcare company
could disseminate
communications on
procedure authorization
or claims status faster
to patients and doctors.
Another important
benefit is improvement
in quality of compliance,
because with RPA,
processes become fully
documented, traceable, and transparent.
AI can ensure accuracy of provider data,
which will help healthcare companies avoid
steep regulatory penalties imposed in the
absence of accurate provider data.
In addition, an intelligent system can turn
its vast data resources into insights and use
that to propose personalized offerings to
prospects, or simply offer the most relevant
additional product to an existing member.
Last but not the least, AI helps healthcare
companies take better care of their members.
In this business, it is not uncommon to
encounter emotional or agitated callers.
Companies can train their service staff to
deal with them with empathy. There are AI
software solutions, which analyze the speech
of company associates during calls, and
prompt them to soften their tone or slow
down, whenever required.
AI-led transformation
will be an antidote for
high costs, as well as an
enabler for better health
outcomes and more
rewarding experiences.
TechTalk

Human capital in healthcare needs to be diverted to the intersection of caregivers and patients.
Enabling people to accomplish
purposeful goals
It is estimated that a claim processed with
manual intervention costs about US$4,
which when auto-adjudicated, costs about
US$1 with legacy technology, and even
less with automation. A single robot, which
can accomplish the work of 2 to 5 claims
processors, can deliver over 20X returns on
labor costs alone over a 5-year term.
The savings for healthcare companies,
which have very large service operations
with several thousand employees, is clearly
enormous. Beyond this cost factor, there
is an enticing opportunity to deliver a
human-centered design for healthcare;
by redeploying freed up staff on work of a
higher order, such as member intimacy and
member-care. Automating most routine tasks
in service operations will make staff available
to engage members in proactive, contextual,
and meaningful conversations, which are also
AI-enabled, and help members improve their
health, or enjoy more healthy days.
An illustration might be useful here: If an AI
system alerts an associate about a member
who has missed renewing a prescription for
diabetes medication and provides a predictive
insight into the member’s health disposition
based on health history and related attributes,
the healthcare associate can promptly
arrange a review with the member’s
healthcare provider.
Like in several other industries, AI in
healthcare can complement the workforce
and amplify their capabilities. Human capital
in healthcare needs to be diverted to care at
the intersection of caregivers and patients
rather than be involved in low-value, back-
office operations to support front-line
caregivers. AI-led automation is ushering in
this great opportunity today.

44
TechTalk
New positions like the Chief Robotics Officer (CRO) will emerge within companies, along with
new business models in the market.
Challenges and concerns
Employees are anxious about losing their
jobs to automation, and their employers
about managing the change. These concerns
can be addressed to a great extent by
communicating plans for redeployment and
retraining as early as possible. Here, it may
be useful to cite the example of the banking
industry, which is about a decade ahead of
healthcare in terms of technology adoption.
Although the teller function in banking was
automated decades ago, the people who
were serving in those roles continue in the
industry, albeit in new roles.
An expanding technology landscape around
robotics and intelligent operational systems
(RIOS) gives rise to several challenges.
The proliferation of solutions and vendors in
healthcare complicates the decisions around
AI adoption. Maintenance of AI solutions, as
they scale from a single process to several
thousands, is another important concern,
followed by their governance and ROI.
Welcoming the Chief
Robotics Officer
RIOS and AI in general will also precipitate
new positions within companies and new
business models in the market. The Chief
Robotics Officer (CRO) will emerge in the
next few years, especially in industries
such as healthcare where automation is
beginning to be embraced rapidly. The CRO
will assume a comprehensive role with many
responsibilities – from choosing the right
technologies, and managing change and
effective staff communications, to managing
costs, governance, and ensuring ROI.
She will eventually become what the CIO is
today across businesses, and may even earn
a place in the boardroom.

About the Author
Venky Ananth
Vice President, Healthcare Practice, Infosys
Venky is responsible for business profitability and growth of
healthcare clients for Infosys. He manages critical relationships
with senior client executives and industry analysts, and anchors
talent development of key Infosys personnel. He is responsible
for crafting and delivering business and technology solutions
to client business problems with his deep understanding of the
healthcare business and technology.
Previously, he was the global delivery head for the Infosys
Digital Transformation Practice focused on the retail and
consumer industries, and was responsible for Practice P&L,
project / program delivery, competency development, and
talent management. He has spent over 21 years with Infosys
and has won many excellence and Gold standard awards for
outstanding achievement ranging from thought leadership to
client management.
He holds a bachelor’s degree in mechanical engineering and
has completed an executive leadership program at the Stanford
Graduate School of Business.

46
RENEW
NEW
46

48
Watching the digitization of work and life over
the last couple of decades, some clever pundit
coined the terms‘digital natives’for millennials
who seem to have technology in their DNA,
and‘digital immigrants’for the boomers and
others who have had to embrace technology.
These have proven apt descriptors of
segments in our workforces and communities,
and when we plan recruitment, training,
and deployment.
Today, as technology-driven disruption
renders entire companies obsolete
with artificial intelligence and pervasive
automation, we see the emergence of a
new segment:‘digital refugees’. Like political
refugees, digital refugees too are displaced
and in many cases struggling and adrift.
Social and governmental forces are starting to
wrestle with the unique challenges they pose,
leading corporations are starting to assess
the impact they have on the economy, and
communities are starting to wonder if – and
how – this wave of disruption will reach their
own doorsteps.
The concept of a workforce refugee is not
new; thousands of workers have been
WHERE’STHESAFETYNET
FORDIGITALREFUGEES?
Renew-New
The concept of a workplace refugee is not new. But the idea of‘digital
refugees’takes it a step further, as it has become a global concern, and
a pervasive one. Here we look at how the issue has evolved, and what we
can do to tackle it.

Where’s the Safety Net for Digital Refugees?

50
Renew-New
displaced by automation or globalization
over the past several decades, particularly
in manufacturing. Some have retooled and
entered other careers, largely in the service
sector. But many have languished, losing
hope in their nation’s ability to offer them a
future. The‘rust belt’is filled with stories of
shattered dreams.
The case of the digital refugees may be even
graver, however, for three significant reasons.
First is scale; our best estimates suggest
that digital refugees will not be created in
isolated pockets of displacement, but rather
in massive waves. The‘intellectual rust belt’
is likely to be broad and borderless. Second
is permanence; just as many manufacturing
workers were unable to retrain themselves
into knowledge-workers, many digital
refugees will be unable to step up to the even
higher-order technology jobs that are likely
to be created as part of the
digital / AI boom. Yes, jobs
will be created – but they
are unlikely to be jobs that
displaced workers can
hope to fill.
Third, and perhaps most
significant, is societal and
governmental readiness
to take on the challenge.
There is a bit of a“We have seen this before
and we are not going to let it happen again”
sentiment. This bodes well for workers – but
it may be problematic for corporations.
If we are ready to provide a social safety net,
whether in terms of retraining, guaranteed
wages, or economic stimuli in other sectors,
the question is,“Who pays?” Some argue that
corporations will need to foot the bill.
Preliminary dialogs last fall in a World
Economic Forum council on the Future
of Education, Gender, and Work indicated
support for the idea of a global‘cap and
trade’policy for AI-driven job loss, similar to
current‘cap and trade’provisions for carbon
generation. In other words, if a corporation
leverages AI and automation to disrupt its
business model, radically improve efficiency,
or enhance service or operations, and if
worker displacement is one outcome, then
the corporation should pay to provide the
safety net. This topic will be taken up at the
spring dialogs on the Future of Education,
Gender, and Work.
In February 2017, Bill Gates took a similar
position, suggesting that robots should
pay taxes. While a robot tax could replace
revenues previously generated by employee
income taxes, the approach clearly shifts
responsibility for filling the tax coffers from
the individual to the corporation. On the
positive side, this approach could fund the
creation of a broader social safety net for
displaced workers. In an interview with
Quartz, Gates noted,“A tax on robots could
help fund the training needed for jobs such
as caring for the elderly, teaching in schools,
and helping kids with special needs – roles
that robots cannot fill.”But it will have to be at
corporations’expense.
Corporations that are politically savvy, as well
as those that subscribe to
compassionate capitalism,
are trying to get in front of
this wave. They recognize
that they have to manage
the inevitable disruptions.
They are starting to consider
ways of managing massive
displacements, whether or
not to proactively retrain
displaced employees, and
what their new, post-AI organizations need
to look like. Some are finding that AI may not
so much target the blue-collar layer (which is
already pared to the bone) as the lower white-
collar layer – lower and middle management.
This creates secondary challenges – not only
“What do we do with these workers?”but also
“If we eliminate these positions, where is the
training ground for the next generation
of executives?”
Technology companies are finding the
retraining challenge somewhat easier,
and are aggressively moving to retrain
tech workers with new digital and AI skills.
Business Standard (March 7, 2017) reported
that Wipro plans to reskill 10,000 employees
in cloud, digital, analytics, and DevOps,
among other high-demand skills. Tata is
reportedly targeting another 100,000 in
digital technologies. Infosys is not only
reskilling employees in newer technical skills,
but has also completed training over 70,000
Firstly, the intellectual
‘rust belt’ is likely to be
borderless. Secondly,
it might become
pervasive, as workers
find it hard to retrain
into knowledge workers.

Thousandshavebeendisplacedbyautomationorglobalizationoverthepastseveraldecades.
technical employees in Design Thinking, to go
along with its Zero Distance initiative, driving
technology improvement and innovation.
The challenge will be greater for traditional
brick-and-mortar organizations with
employees who are less tech-savvy, whether
at factory floor or line manager levels.
Reskilling weighs heavily on executives
we have been speaking to, those who are
driving AI, automation, and digital disruption
initiatives. Organizational redesign is also
on their minds, including rethinking career
paths and competency models for leadership
development. Governance is top priority; how
will businesses and IT govern pervasive AI and
take accountability for its benefits – and risks?
As a leader in corporate learning at scale, and
as a business partner deeply experienced
in helping clients lead technology-driven
transformations, Infosys is developing new
offerings to help clients wrestle with the
human side of AI, digital, and automation.
We are combining human-centric design
with technological innovation to find better
answers – whether it is for reskilling displaced
workers at a leading CPG firm, designing a
more empowering culture and physical work
environment for a financial services leader,
or rethinking career paths for a technology
leader. If firms don’t want regulatory intrusion
(think cap-and-trade for digital refugees), they
need to find innovative solutions for human
challenges on their own, and lead workers to
new roles and help them accommodate into
an AI-driven business environment.
Looking at this shift against a backdrop of
rising nationalism, we realize that there is a lot
more at stake. Many of the countries that are
witnessing a rise in nationalism – including

52
Renew-New
Theintellectual‘rustbelt’whichcanleavepeoplebehindislikelytobepervasiveandborderless.
the US – actually do not have sufficient skilled
IT workers to meet domestic demand.
So could a technology company, renowned
for technical training at scale, provide training
and development to help create a skilled
domestic workforce? Instead of being merely
a purveyor of AI and automation, could
Infosys provide solutions for massive reskilling
of digital refugees? We think the answer is
yes, and we are actively pursuing this agenda,
starting with our recent announcement that
we will set up a new development center in
Indiana and create 10,000 new jobs in the US.
Responsible employers need to think not
only about reskilling digital refugees, but
also about developing the skills of future
generations. If basic technical proficiency
increasingly divides the haves from the
have-nots, one answer may be making
digital literacy as much a part of primary
and secondary education as reading,
writing, and‘rithmetic’.
Leading technology companies are
collaborating with the Code.org Advocacy
Coalition to reshape education and make
computer science an integral part of the
K-12 curriculum. Supporters of the coalition
are working toward fundamental changes
in classroom content as well as professional
development of educators. It is heartening
to see academic transformation taking place
ahead of government intervention.
While some detractors argue that early
exposure to programming might make
children more machine-like, research says
otherwise. Introducing children to the
fundamentals of computation actually
enables them to better understand patterns,
recognize underlying relationships, and solve
problems. It teaches them to deconstruct
complex problems into smaller parts and to
address issues with logic – skills which will
help them both professionally and personally,
throughout life.
“Computational thinking is thinking in terms
of prevention, protection, and recovery from
worst-case scenarios through redundancy,
damage containment, and error correction,”
says Jeannette M. Wing, Corporate Vice
President at Microsoft Research and former

Holly Benson
Vice President and Managing Partner, Infosys Consulting
Holly brings the scientist’s curiosity and observational skills to the
world around her. As founder and head of Infosys Consulting’s
Enterprise Talent and Change Practice, she uses her interactions
with some of the world’s leading corporations — and their
workforces — to form fresh and intriguing insights on education,
skills, and the future of work.
After 25 years of work in the consulting domain, she remains a
hands-on practitioner who helps clients deal with people and
organizational implications of agile enterprises, intelligent
automation, and changing business models. Holly is now
bringing this perspective to the World Economic Forum, as a
steering committee member of their Global Future Council
System Initiative on Education, Gender, and Work.
About the Author
President’s Professor of Computer Science
at Carnegie Mellon University. She sees
it as a fundamental skill.“We should add
computational thinking to every child’s
analytical ability,”she recommended in an
article published in the Communications
of the ACM.
At the Eliot-Pearson Children’s School in the
Tufts University Department of Child Study
and Human Development, children program
robots using building blocks with barcodes.
When kindergarteners adopt the learning
style of machines as they play with toys, they
cultivate a habit of lifelong learning. This will
create a wealth of talent for the workplace
and help insulate tomorrow’s workers against
whatever displacements characterize the
next generation.
At the end of the day, the challenge for
business leaders is not only how to help
displaced workers support themselves, but
also how to help them maintain their dignity.
When AI and automation take away not
only their ability to provide for themselves
and their families, but also their productive
self-worth, the answer needs to address both
the financial and the emotional aftermath.
Our solutions to these problems need to
address emotional needs as well as physical –
which may in the end be the harder problem
to solve. Mitigating this risk for future
generations, by embedding computational
skills and computer literacy at all levels of
education, may be one of the greatest gifts
we can offer our children and grandchildren.
Digital Refugee

54
Q: Automation and artificial intelligence
are seen as the next game-changers for
organizations. They are already reshaping
industries such as manufacturing, healthcare,
and transportation, among others. How do you
envision AI transforming telecom – an industry
that is already in the midst of transformation?
A: Automation, both of processes and
decision-making, has always been a way to
optimize our operations. Whether we are
talking about the correlation of incident-
tickets to detect general outages, the
automation of our provisioning flow,
or the zero-touch configuration of a modem
at the moment it is connected to our network.
So automation is not a new topic.
However, we see today that the ecosystem
enabling artificial intelligence is evolving
rapidly based on the availability of cheap
and massive computing power, IoT gathering
massive amounts of data, often real-time,
and new possibilities in analytics.
Like with all new technologies, it is important
to keep the objective in mind. We don’t
CAUTIOUSOPTIMISM
MARKSTHEADOPTION
OFAIATPROXIMUS
Renew-New
Dominique Leroy, CEO and Member of the Board, Proximus, in
conversation with Rajesh Krishnamurthy, President and Head
of Energy, Utilities,Telecommunications, and Services, Infosys.
Q A
Rajesh
Krishnamurthy
Dominique
Leroy

Cautious Optimism Marks the Adoption of AI at Proximus

56
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have the ambition to implement artificial
intelligence in all of our processes, just
for the sake of doing so. Nonetheless, I’m
convinced that we will need the capabilities
of automation and AI to further increase our
efficiency and agility, as well as to offer a
very high degree of personalized customer
interaction. Next to that, AI will need to help
us deal with the growing complexity coming
from exactly the transformations
you mentioned.
So, I look at technological innovation
in general as an opportunity.
The telecommunications industry should
embrace the future, and by being early, we
could play an important role. Proximus is well
placed to be a key enabler of
AI itself for other industries
as well – offering virtualized
infrastructure with our LoRa
IoT network, and anonymized
data sets through our
brokering platform, EnCo
(Enabling Company).
Q: Accuracy, speed, process and cost
efficiencies, and scalability are some of the
immediate benefits of AI (automation, RPA,
machine-learning techniques) that industries
are beginning to experience. What are the new
kinds of AI-driven services in telecom that you
see emerging?
A: There are three major application areas
where we believe artificial intelligence could
play a beneficial role.
First, AI can help solve business challenges
by reducing complexity and giving people
better information and insights to take better
decisions faster. We see a lot of opportunities
on internal optimization and operational
efficiency, like you mention. This concerns
the maintenance and optimization of our
networks and platforms or IT help desk
automation. In our customer operations
division, we’re evaluating ML and prescriptive
analytics for field interventions with a‘First
Time Right’objective.
Secondly, we also look at AI as an enabler of
a better and more personalized customer
experience. Concepts like voice-driven IVR
and virtual customer assistants are being
analyzed for simple customer interactions.
It’s essential here that AI is fully integrated
into enterprise systems, such as CRM tools,
knowledge management, and billing and
ordering systems, in order to offer a single
customer experience through all channels.
And finally, we look at AI as an enabler for
new revenue streams. As said before, we
want to build on our assets, such as our EnCo
platform, to support our corporate customers
in their own transformation and to leverage
new business.
Q: One principle of Proximus’ Fit for Growth
(Good to Gold) strategy is “simplifying at all
levels.” What has been the role of automation
and AI in this organizational
renewal?
A: One of the pillars of our
Fit for Growth strategy is to
become a fitter organization
and bring down our operating
costs while increasing
customer and employee
satisfaction. To this end, we
are driving simplification and efficiency at
all levels in the organization. We have been
simplifying our network and platforms
through virtualization; our product offerings
for our customers and our internal processes.
AI can support us in all these domains.
Typically we have been automating simple,
repetitive tasks, freeing up time for people to
invest in more value-added interactions.
We will certainly continue to do so, now that
AI expands the scope of what is‘simple’for
a computer.
This is not only done in the typical domain
of operations or back-offices, but also in
customer-facing departments such as sales,
marketing, customer services, and supporting
functions where we are investigating various
use-cases.
Besides reviewing our existing processes,
we will also introduce new processes,
technologies, or infrastructure. We must then
consider automation and AI from the start.
For example, making sure that the
architecture allows the extraction of the
needed data, as well as challenging the
Automation is not just
for repetitive tasks but
for customer-facing
roles too, like sales
and customer service.

needed human intervention present in new
processes versus the cost of automation.
But simplification through AI is certainly not
a given, as the technology behind AI is quite
advanced. We must be cautious that AI does
not become a complexity layer on top of all
the other ongoing technology evolutions.
There is a careful trade-off to be made – on
the one hand, we want to remove complexity
facing our employees by means of AI, but on
the other hand, we want to remain in control
and not blindly depend on a chain of black
boxes. This trade-off will vary depending on
the application, and we will have to learn the
optimal balance on the go.
Q: Proximus is looking to transform itself into
a ‘digital services provider’. AI will be obviously
playing a significant role in every phase of this
journey. Could you share some developments,
insights, and even learning around this?
A: The journey of becoming a Digital Service
Provider is threefold.
First, it is about digitizing our current business
and delivering a superior digital-first customer
experience. Secondly, we are transforming our
core networks and IT systems to deliver more
and more services from the cloud through
virtual network functions and automating
what can be. Lastly, we will develop new
digital services, for both our residential and
professional customers. In particular, for our
enterprise customers, we want to create a
new digital ecosystem, open to partnerships
and collaboration with new emerging players,
and offer our customers new application-led
and service-oriented solutions, like a smart
home, smart cities, smart mobility, new ways
of working, and smart retail.
This transformation to become a digital
services provider brings about a number
of challenges. I’d like to highlight just two
major ones.
At the technological level, we are investing
in strict data-architecture and governance
throughout the company in order to become
a data-driven organization. But the biggest
challenge is adoption. Adoption of new digital
tools by our customers, where it is our job to
ensure superior customer experience through
intuitive design, as well as adoption by our
employees – they must be convinced about
the capabilities of AI and how it will improve
the quality of their work.
We should definitely not underestimate the
change management journey, involving
We want to create a new digital ecosystem, open to partnerships and collaboration.

58
Rajesh Krishnamurthy: Thank you, Dominique, for your insights. Automation and AI are
reshaping the telecommunications industry, as organizations look to achieve greater efficiency,
even as they deliver new services. It is also helping companies like Proximus lead the way with
innovative, insights-driven offerings, and personalized experiences.
Renew-New
We should not underestimate the change management journey – changes in our ways of
working, thinking, and doing.
changes in our ways of working, thinking, and
doing. The success of becoming a data-driven
company will depend on change managers
just as much as it will on data scientists.
Q: There is widespread angst about loss of jobs
due to automation. Adoption of robotics and AI
itself will create new kinds of roles, especially in
the area of robotic maintenance. On the other
hand, with resources freed up from mundane
jobs, there will be opportunities to enhance
human talent, to improve services. What is your
view on repurposing talent through retraining
and upskilling vis-à-vis the telecom industry?
A: Automation and AI will mainly impact
people that today do repetitive jobs that
are typically prone to automation. Just
remember that a little more than 20 years
ago, most of our revenues came from fixed
voice. In a sector that is evolving so fast,
retraining and upskilling is a constant.
On the other hand, AI will augment what
humans are doing. Machines excel in speed,
cost, and consistency. But humans have
capabilities that machines do not have: to
handle complex tasks in a very broad area,
and superior social ability. Embracing the full
capabilities of working with AI will certainly
bring a competitive advantage as it will
allow all our employees to focus on
human interactions.
I strongly believe that human-to-human
interactions are key in our lives and it will
remain so – and no robot or machine can
compete with human creativity, social
ability, and empathy. This means we need
to keep building strong emotional links
with our customers – enabling great human
interactions that bring value to them – if
we want Proximus to remain a strong and
relevant brand in people’s lives.

Dominique Leroy
CEO and Member of the Board, Proximus
Dominique Leroy has been CEO of Proximus since January 2014.
She joined Proximus (formerly Belgacom) as Vice President of
Sales for the Consumer Business Unit in 2011 and was appointed
Executive Vice President of the Consumer Business Unit in 2012.
Prior to Proximus, Dominique worked for 24 years at Unilever.
She was Managing Director of Unilever Belux and member of
Unilever’s Benelux Management Committee.
She is also Chairwoman of the Boards of BICS and Be-Mobile,
Chairwoman of the International Advisory Board of the Solvay
Business School, and an independent board member at Lotus
Bakeries and Ahold Delhaize.
Rajesh Krishnamurthy
President, Head – Energy, Utilities, Telecommunications, and Services, Head
– Infosys Consulting, Head – Infosys Europe
Rajesh Krishnamurthy is responsible for the global Energy, Utilities,
Telecommunications, and Services business unit, and also works
with Infosys Consulting, the global advisory division. He also leads
the Infosys operations in Europe, and in a career spanning more
than 24 years with the company, he has held senior leadership
positions across the business and in all major markets. He is also
the Global SAP Practice leader for Infosys across industry groups
spanning 20,000+ employees.
Previously, Rajesh was the Head of the Financial Services and
Insurance business in Europe. He managed the global Consulting
& Systems Integration (C&SI) Practice for the Retail, CPG, and Life
Sciences (RCL) industry group, spanning management consulting
services, packaged application services (SAP, Oracle), and advanced
technologies. He is based in Paris.
About the Participants

60
This need hinges on three basic capabilities
delivered by technology:
IMMEDIACY: If a truck route is closed, we
need to know now! In real time, so we can
deliver the required inventory using another
truck, via an alternative route. If a plant has to
shut down, we need to know immediately, so
we can plan accordingly. Data that is even a
few days old is worthless.
INSIGHTS: The adoption of smart devices has
generated large volumes of unstructured
data. Using this data to generate insights
and drive business decisions is imperative.
On top of this, the convergence of retail and
consumer packaged goods (CPG) industries
demands the reorientation of processes and
business models.
INNOVATION: AI models are reinventing retail
across product cataloging, merchandising,
customer service, replenishment, demand
forecasting, pricing, and logistics.‘Fast fashion’
retailers such as Zara and Mango integrate
AI, machine learning, and personalization
technologies to better understand consumer
behavior. It helps recommend products
intuitively, empowering them to influence
demand and better manage pricing as well
as inventory.
PREDICTION
PUTSTHESMART
INSMARTRETAIL
Renew-New
The outlook for Retail is sobering.The industry is facing some of its
toughest challenges.What was just a means to increase margins or
revenues has become a matter of life or death – innovation through
digital disruption.

Prediction Puts the Smart in Smart Retail

62
The science behind predictive
commerce
An AI ecosystem helps brick-and-mortar and
e-commerce companies realize compelling
value before, during, and after a purchase.
Retailers can capitalize on the widespread
adoption of smartphones and the potential
of AI to improve the supply chain and create
value for their customers. They can shift from
responsive to predictive commerce, and
from micro-segmentation to personalization.
Besides, an AI backbone connects
stakeholders and devices in the ecosystem
to blur the line between online and offline
shopping experiences.
Real-life example 1: Shoppers
swipe interactive windows at
the Westfield San Francisco
Centre (a shopping mall) to
learn about various premium
brands and order the
products on mobile devices.
Real-life example 2:
The‘store mode’of cosmetics
brand Sephora’s mobile
app integrates the online shopping cart
with their Beauty Insider loyalty program,
simplifying notifications, real-time updates,
and redemption of reward points.
It is common knowledge that personalized
content engages shoppers at a deeper level.
AI-powered retailing creates immersive
shopping experiences by connecting data
from diverse sources and matching potential
demand with product availability in real
time. AI models integrate the browsing
histories, Facebook conversations, Pinterest
searches, and Instagram followers to provide
granular insights into shopping behavior.
It creates user profiles, assesses requirements,
understands consumers, all of which help
recommend complementary products and
drive impulse purchases. This is the secret
sauce of contextual commerce for retailers.
Influencing purchase
Recommendation systems powered by
machine-learning algorithms generate
substantial revenue for Amazon and Netflix.
The success of recommended purchases has
encouraged Amazon to pilot an AI-based
‘predictive delivery’program. Delivery trucks
are stocked with items that are likely to be
ordered by shoppers while the trucks are en
route for delivery in the neighborhood!
The Sears Auto Center has launched Digital
Tire Journey, a service to enhance auto tire
shopping. The AI-enabled app uses a set of
questions to create a driver profile based
on preferences and driving behavior. It
recommends tires that match the vehicle
and driver’s performance as well as the most
appropriate marketing channel for purchase.
Cognitive technologies provide computing
devices with the ability to recognize
handwriting and symbols, extract text from
images and files, transcribe human speech,
identify objects and faces, and
understand user intent. An AI
ecosystem processes queries
in natural language, generates
content from partial data, and
sequences actions based on
set parameters. Further,
deep learning systems
automatically validate
machine-generated action,
which ensures accuracy.
AI-based processes predict transaction
opportunities by getting to know shoppers
and recommending personalized offers along
the purchase path, without programmed
instructions. It prompts shoppers to search
for products even before they perceive the
need for it.
Driving product discovery
Technologies that make sense of visual
environments and engage consumers with
two-way conversations elevate the shopping
experience. The Pinterest Lens helps shoppers
who are undecided about their shopping
needs or have a limited vocabulary of a
product to find it online. Pinterest’s visual
search algorithms evaluate similarity scores
tagged with images to facilitate a user’s
search for home décor or fashion wear.
Machine-to-machine interfaces can be
combined with simulation, augmented
reality, and other virtual tools to automate
complex tasks such as product design,
selection of sizes for clothing and footwear,
and consultations for skin care. Consumer
interactions via the L’Oreal Diagnose My
Hair app mimic in-store hair consultations.
Adobe’s AI image-editing tool adopts
AI is not just for
e-commerce. It can
help brick-and-mortar
companies by allowing
them to shift from
micro-segmentation
to personalization.
Renew-New

photorealism to help handbag manufacturers
convert rudimentary sketches into images.
A detailed taxonomy of products is critical for
online discovery since product imagery alone
cannot define the content, promotions, and
marketing in retail. Inconsistent product tags,
incorrect metadata, or incomplete attributes
adversely affect product recommendations.
Deep learning algorithms create a product
catalog by extracting attributes, and classify
products for intuitive search. Accurate
classification of products enables AI entities
to transform retail operations.
Providing personal assistance
Speech and facial recognition capabilities
provide virtual shopping assistants with
a personal touch. It converts apps into
experienced concierge teams that can
interact with shoppers, take orders, and
share product information. Virtual assistants
incorporate insights from diverse sources to
make relevant recommendations, and deliver
an omnichannel experience.
The Macy’s On Call app combines cognitive
computing and natural language processing
with location-based software to guide
shoppers at stores. The AI system responds
to queries such as the location of products
within the store and details of a product.
AI is being embedded into products to
minimize human intervention. While
Amazon’s Dash button automates purchases,
Sharp is incorporating AI into home
appliances for hands-free operations.
Sharp’s vacuum cleaners and microwave
ovens can be controlled through voice
commands. Nestlé SA has deployed robots
as sales assistants at retail stores in Japan.
The fleet of humanoids engages customers
with product information and sells Nescafé
products and vending machines.
Cognitive computing and automated
systems enhance retail operations –
from stock management and pricing to
planogramming and promotions.
They provide insights to optimize marketing
spend and boost productivity at the store.
However, technological progress must deliver
compelling benefits to all stakeholders.
Each retail enterprise should replace or
augment human effort with AI to transform
business processes. The success of AI-driven
retailing lies in navigating a personalized
shopping journey for every shopper.
AI systems understand consumers and their real-time requirements, which helps recommend
complementary products and drive impulse purchases.

64
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RPA: Velocity meets accuracy
Robotic Process Automation (RPA) provides retailers with opportunities to optimize
costs and enhance efficiency across in-store systems and the supply chain.
It eliminates human intervention in the retail-specific as well as administrative and
generic back-office processes. Rule-based systems for order processing, inventory
planning, customer service, merchandising, product returns, and refunds can be
processed faster and with better accuracy by RPA solutions.
Software robots integrate with enterprise systems such as finance and human
resources to transform execution. It unifies data sources to simplify reconciliation.
In the absence of manual data entry or repetition, databases are always auditable.
Moreover, automated processes support reporting and compliance. Most importantly,
RPA streamlines workflows and establishes a robust foundation for advanced analytics.
Retail enterprises should replace or augment human effort with AI to transform business processes.

Aniket Maindarkar
Aniket has extensive expertise in Retail and CPG industries. He
also has a deep understanding of the shared services and global
business services (GBS) industry, and over the last 22 years has
assisted several Fortune 1000 organizations architect and execute
their GBS strategy.
He has a bachelor’s degree in computer science and an MBA
in marketing.
His interests include sailing, running, triathlons, and
adventure sports.
About the Author

66
RESEARCH
UNRAVELED
66

68
Q: Could you connect the dots for us – artificial
intelligence (AI), computational economics
(CE), and building ‘machina economicus’?
A: The quick and dirty answer is: CE is the
application of AI methods to economics.
Computational economics (CE) resides at the
intersection of economics and computation.
To understand how CE and AI connect with
machina economicus, we must first know that
the present economic theory is based on a set
of assumptions, which are:
• People have rational preferences
among outcomes
• Individuals maximize utility while firms
maximize profits
• People act independently on full and
relevant information
These assumptions about human behavior
create the construct of a species known
as homo economicus, the ‘economic man’.
While these assumptions do not accurately
represent how humans behave in the real
world, they are necessary to define an
analytical model to work with.
AI researchers aim to construct a synthetic
homo economicus known as machina
economicus (also described as the perfectly
rational machine). A recent article by Parkes
and Wellman explains how such an AI
approach can mimic homo economicus
if it can align perceptions, outcome
preferences, and actions to come to a
decision under uncertainty.
ONTHETRAILOFMACHINA
ECONOMICUS
Research Unraveled
Computational Economics (CE) is a discipline that uses computer-based models
to solve analytical and statistical problems. In this insightful interview,
Dr. Prause uses real-world examples to explain how CE and AI are finding
practical application in business landscapes. He also reveals how AI can play
an important role in improving business simulations.
Q A
Infosys Martin
Prause
Dr.MartinPrauseonhowComputationalEconomicsand
ArtificialIntelligencearechangingbusiness.

On theTrail of Machina Economicus

70
Now, computational economics has
two primary economic applications
for businesses today:
Example: You wouldn’t care if the GPS
navigation system in your car calculates a
route that takes just one minute longer
than the optimal one.
SOFT COMPUTING TO SOLVE ECONOMIC
PROBLEMS: Soft computing refers to a
set of nature-inspired computational
methodologies such as evolutionary
algorithms, swarm algorithms, and artificial
neural networks that solve real-world
problems where traditional approaches are
not efficient. This is because in many cases, it
takes an exponentially long time to compute
an optimal solution and the margin of benefit
between the second and the optimal solution
is, quite often, minor. Therefore, we can
safely make do with approximations. In the
business world, soft computing is used in the
iterative process for high-frequency trading
markets where trades are
done within milliseconds.
Here, an optimal solution
to determine the best
portfolio or to forecast the
financial markets cannot
be calculated efficiently,
hence, an approximation is
the next best option.
COMPLEX SYSTEM
MODELING TO
UNDERSTAND BEHAVIOR:
A complex adaptive system (CAS) is a system
where‘agents’autonomously interact with
each other. Simply put, an agent is a unit that
senses its environment, follows process rules
to react to the environment and its internal
state, and propagates its result to other
agents for interaction. The main advantage of
CAS over traditional analytical systems is the
study of how specific phenomena emerge.
As CAS is self-organizing, it allows non-linear
behavior to emerge depending upon
internal system changes as well as
environmental changes.
Agent-based modeling (ABM) is a specific
CAS model to study the economic dynamics,
i.e., how agents behave, providing a better
understanding of the system. It does not
focus only on outcomes – rather it focuses
on how the outcome materializes. In other
words, it is a methodology to study behavior.
ABM can be used in social networks to
simulate interactions, consumer behavior,
word-of-mouth advertising, innovation
diffusion, etc. Generally, ABM is used to
generate what-if scenarios for companies
and governments seeking to establish
policies, regulations, and forecasts.
In the words of Arthur Samuel (1959), AI
is the“field of study that gives computers
the ability to learn without being explicitly
programmed.”Taking a helicopter perspective,
AI consists broadly of three fields:
knowledge representation and optimization,
automated analysis of data, and learning (i.e.,
machine learning).
What is the link between CE and AI?
First, from a theoretical view, CE and AI use
the same methods to solve problems.
While one is tailored to economic applications
(CE), the other is not
tailored to any application
(AI). Second, from an
application perspective,
AI can enrich agents in
complex adaptive system
modeling. Thus, agents
gain cognitive abilities to
match or increase real-
world representation.
In summary, thanks to
CAS, we can study not
only equilibriums or specific outcomes, but
also how they are formed. Additionally, if the
agent’s behavior mimics human behavior
closely, the micro and macro dynamics can
be better understood.
Example: There were many publications that
tried to assess the impact of Brexit and the
recently discontinued Transatlantic Trade
and Investment Partnership (TTIP) on foreign
direct investment (FDI).
Q: How can AI be incorporated in business
simulations and how can this help companies
deal with complexity and uncertainty?
A: Business simulations are computational
simulations that mimic companies and their
strategic environments such as internal views,
A recent article by Parkes
and Wellman explains
how AI can mimic homo
economicus if it can align
perceptions, outcome
preferences, and actions
to come to a decision
under uncertainty.
Research Unraveled

competitors, customers, suppliers, and PEST
– political, economic, social, and technology
aspects. In education, such simulations are
used to teach how all business elements
are connected. In industry, they are used to
conduct what-if analyses using appropriate
assumptions and simplified models of the
real world. There are many systems in place
that give companies different views: ERP
systems give an internal view, CRM and
digital marketing offer an external view, and
competitive intelligence systems provide
perspectives on the strategic environment.
If the information provided by these three
systems is aggregated and fed into an
appropriate model, it can be used for
scenario analyses and market forecasts to
align strategies across all business units.
So the key question for business leaders
is: Can my company define and execute a
strategy consistently and coherently in
this environment?
A book called‘The Second Machine Age’
(2014) by Andrew McAfee and
The cognitive and heuristic shortcuts humans use to cope with constraints that are
prone to discrepancies with objective reality:
• Myopic problem representation – Where a problem is oversimplified and
important variables are overlooked
• Group think – A cognitive bias that occurs when the demand for harmony within
the group outweighs the need for critical thinking
• Conjunction fallacy – Based on misjudgment, where the more detailed
information an individual has on a specific event, the more plausible this event is
perceived to be, although it might be less probable
• Confirmation bias – Information that confirms existing beliefs and mental
models, leading one to neglect contradictory evidence
ABM modeling focuses not on outcomes, but on how it materializes.

72
Erik Brynjolfsson talks about how data
generation and usage will increase
exponentially in the near future, particularly
if machines can train themselves to get better
instead of just learning from the past. Digital
systems move at a faster pace than other
systems in society, adding to the complexity
and uncertainty.
Here, AI comes into play when it leverages
the agents of the machina economicus
paradigm in a business simulation. First,
one can study the dynamics based on more
advanced models. For example, instead of
using the analytical supply and demand
model, consumers and suppliers can be
represented as agents with desires, objectives,
and cognitive capabilities. This could help to
demystify the complexity and uncertainty
of the company’s environment. Second, to
conduct a sound what-if study, hundreds
of assumptions have to be tested.
Therefore, thousands of simulations have to
be tested. We must then identify patterns and
study the outcomes. This is where machine
learning comes in to identify patterns of
dynamics and correlate them with outcomes.
The next step is to relax some of our earlier
assumptions in the homo economicus model,
thereby making the outcomes closer to
reality, where human beings are subject to
cognitive biases. Human decision making in
daily life or professional business is subject to
lack of information, processing time, and
limited resources.
Once models can also account for this
systematical error, businesses can achieve a
better understanding on how they should
approach their suppliers or how they should
plan their marketing campaigns, etc.
Example: Daniel Kahneman and Amos
Tversky (1973) demonstrated that humans
use shortcuts to cope with these constraints
and that these cognitive representations
and heuristics are prone to a systematical
discrepancy to objective reality.
Q: What are the ways in which AI can be used
for a company’s business model and decision-
making process?
A: Let’s move away, at least temporarily,
from the idea that AI is a cognitive,
super-intelligent, artificial processing unit
and instead focus on the AI methods,
i.e., knowledge representation, learning,
and optimization. Today, the market for
AI applications is very fragmented and
there is a lot of buzz around this approach.
However, in most cases, AI refers to some
form of machine learning or soft computing
tailored to a particular application. In fact,
many companies / start-ups in the European
market are promoting AI methods across the
value chain, and they primarily use either
optimization techniques or machine learning.
In contrast to the tailored use of AI methods,
there are also leading players who are already
working on a machine with the capacity to
learn‘the way a baby or an animal does’.
This is interesting because this machine is
actually learning by‘observing the world’,
not by being trained. This approach closes
the loop and aggregates the elements of
knowledge representation, learning, and
optimization to support a wider range
of applications.
Some examples:
• Marketing: Wunder.ai is matching people
and products using algorithms.
• Inventory: Cargonexx has optimized the
utility of cargo space on its trucks, thereby
matching demand with supply.
• Operations: MicroPsi uses AI to control
industrial processes and systems.
• Development: EyeQuant uses AI to provide
automatic A/B testing for mobile app /
website design.
For each element, there are multiple solutions.
Q: AI has also precipitated concerns for
businesses such as job automation, fooled
AI, etc. What are the pitfalls and how can
businesses avoid these?
A: Let me elaborate on some of the basic
concerns that hinder the acceptance of AI:
FEAR OF LOSING CONTROL ON DECISION-
MAKING: Humans are subject to many
cognitive biases and machines can easily
exploit these. Do you think that we have free
will when we navigate a website? No; the
components in a well-designed website are
placed to achieve a certain goal.
Research Unraveled

Computational economics is about identifying patterns and studying the outcomes. This is where
machine learning can play a huge role.

74
There are other fears: Machines can easily
use framing or anchoring techniques to
influence our behavior, or machine-learning
systems may use people’s digital trails and
incorporate undisclosed traits into their own
decision-making.
NON-TRANSPARENT USE OF DATA:
Eric Horvitz and Deirdre Mulligan highlight
that social network posts can be used to
determine if a person has depression.
While this is good to help us initiate treatment
for that person, groups with vested interest
can secretly use such data against this person.
UNCERTAINTY OF THE DECISION-MAKING
PROCESS: The use of machine learning
is increasing, yet organizations lack
understanding of how computers arrive at
decisions. Is the machine programmed to be
biased towards a specific company goal?
How does the machine resolve ethical
dilemmas such as the much debated
‘trolley problem’?
UNANSWERED LEGAL QUESTIONS:
Who becomes responsible when the outcome
of a machine is not aligned with the law or
cultural standards? What if an AI-controlled
traffic signal learns that it is more efficient to
change the light one second earlier than
was previously done? While this may be
more efficient, perhaps it can lead to
more accidents.
OPENNESS TO MISLEADING INPUT: AI
methods can be fooled. It took less than 24
hours for Twitter users to corrupt Microsoft’s
AI chatbot, Tay, into making racist comments.
Nguyen, Yosinski, and Clune demonstrated
how an artificial neural network (ANN) for
image recognition was fooled into believing
that images which were unrecognizable to
human eyes were actually familiar objects.
Q: How do we avoid those pitfalls?
A: One approach is to establish standards
that are accepted by society. Just as car
manufacturers have to adhere to specific
norms to sell their cars, AI designers
and developers should also adhere to
specifications and follow norms on how
decisions are taken, although this is a difficult
approach as there is no one definition of AI.
Nevertheless, some organizations such as
OpenAI, are already heading in this direction.
Even the legal and governmental system
should adapt to the rise of AI in daily private
and business life to purposefully regulate the
use of data.
Soon, machines may be able to learn by observing the world, and not simply by being trained.
Research Unraveled

Dr. Martin Prause
Assistant Professor of Computational Economics and Business Analytics,
WHU – Otto Beisheim School of Management, Germany.
Martin received his Ph.D. in economics from WHU in 2014 and
was granted two research scholarships: in India (Infosys, 2013)
and Japan (University of Tokyo, 2015). From 2012 to 2014 he has
been involved in joint research projects with the London School of
Economics and Google Inc. in the Google Summer of Code Program.
Before he started his doctoral studies, he received his MBA degree,
worked as the Head of IT for three years in a small- and medium-
size company, and graduated from the Technische Universität at
Dortmund with a diploma in computer science.
About the Author

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