Intuition Engineered

July 2021
Intuition
Engineered
The Theory and Practice Behind
Engineering Intuition into Business

How does Serena Williams know where to move before her
opponent has hit the ball?
How does Lionel Messi know to cut left just as the defender
lunges in?
How does Sebastian Vettel know to take the outside line, when
the inside line seems clearer?
Intuition.
Williams had an “immediate insight”1
that the ball was going deep in the deuce court; Messi,
that the fullback had moved a fraction to his left; and Vettel, that debris close to the edge of
the Formula One™ racetrack could puncture a tire and send him careening out of control.
All had an intuition, a sense of what they should do next. More often than not, their intuition
is right. From such intuitions, legends are born.
Intuition is a mysterious thing. Some call it a hunch, others a guess. But as we better
understand the inner workings of the brain, through neuroimaging,2
positron emission
tomography and other sophisticated medical tools, we are learning that intuition is not
an ethereal spirit roaming free deep in our unconscious, but a mechanistic process that
analyzes information captured through our senses from the second we are alive. To
paraphrase Herbert A. Simon, the psychologist and Nobel laureate in economic studies:
Most of what we call intuition, to respond to situations and just act, is based on an enormous
accumulation of experience.3
Introduction
2 / Intuition engineered

In short, intuition isn’t a mystery at all. It is a biochemical feat of engineering.
And what is true of people is increasingly true of businesses, too. The best modern
enterprises act with the intuition of the great sport stars — faster, more precisely, more
confident in their understanding of what to do, where to go, how to act. To invest, or not. To
raise its prices, or not. To hire this person, or not. Like great masters, they build mechanistic
processes that capture information, accumulate and synthesize data, refine theories based
on what works and what doesn’t. The best organizations engineer the speed and insight of
intuition into their organizations.
How do leading enterprises act intuitively? How do they anticipate and act instantaneously?
By engineering systems and processes based on machine-learning software, layered on
top of hyperscale cloud processing power, manipulating brontobytes of data, aligned to
business processes that create hyper-personalized, next-generation, consumer-grade
experiences. By using modern tools and techniques that deliver outcomes fit for modern
times. By decommissioning infrastructures and systems and interfaces that are no more
than “legacy debt.” By hiring talent that is truly digital.
“Intuition engineered” is the new goal for every business that wants to outperform in
the Fourth Industrial Revolution, and beyond. As Gartner puts it: “The ability to predict
outcomes, quickly assess innumerable alternatives, and take action … must become a core
competency embedded throughout the organization.”4
In this report, we examine in more detail the technology and process components your
organization should be building, or extending, and how it should apply them with modern
technology optimization methodologies, to ensure you can act with the insight, precision
and speed needed in a world moving faster than ever, and in a business world more
unforgiving than ever.

Four key elements of engineering intuition
Modern businesses harness four key dynamics that are reshaping
competition in every industry in every part of the world:
Software that learns: For the first time in human history,
we have a tool that can make itself. With machine-learning
software, systems improve on their own over time. The system
learns how to recognize patterns and how to find hidden
insights in data, all without being explicitly programmed on
what to do or where to look. This is how, for example, Uber
knows how to match the right driver with the right passenger,
or how TikTok curates the For You feed. There are few people
at these companies figuring these things out. That would be
impossible, as in the case of TikTok there are about 100 million
monthly active users.5
Instead, the machine learns about each
and every one of those sessions, continually getting smarter,
and improving the user’s experience. According to our
research, over the next three years twice as many businesses
expect to be in advanced stages of AI maturity versus today,
and annual spending increases on AI will nearly double from
4.6% to 8.3%.6
A decisive majority — 64% — of executives in
our study believe AI is considerably or very important for the
future of their business.
Massive processing power unlocked by the cloud: Moore’s Law (i.e., the number of transistors
on a chip, and therefore processing power, doubles approximately every two years) persists, in
spite of it being more than 50 years old. Lately, it has been turbocharged by the cloud, which
enables hyper-powerful computers to be tied together. By way of comparison, a muscle car may
have impressive horsepower, such as the 435 “horses” under the hood of a Ford Mustang GT,
but you can’t glue two Mustangs together to go twice the speed. With cloud computing, however,
you can — by tapping into multiple servers to achieve blazing-fast performance. Google’s
Tensor Processing Unit, Amazon’s AWS custom web servers and software-defined networks,
Microsoft’s Azure offerings (based on the Open Compute Project) and Nvidia’s Titan V are among
a new wave of hardware and software architectures offering speed and performance levels
unimaginable a mere three years ago.
64%of executives
in a recent Cognizant
study believe AI
is considerably or
very important for
the future of their
business.

Combined, these four dynamics form the technological underpinnings that turn an organizational operating
model from one that runs things to one that anticipates and acts, one that sees the “next best action,” one that
acts as if on intuition.
Huge amounts of data: Data is the fuel of a modern business. The volumes of it are exploding
in our hyper-connected, IoT-sensor-infused world. Every day, 2.5 quintillion bytes of it are created
around the world. Each of us generates 1.7 megabytes each second, and the majority of data has
been created in the last two years.7
Truly we are in a new world, where a new resource is becoming
the most valuable resource there is. Every like, swipe, comment, tap, movement of a mouse,
every typed or spoken word, even every heartbeat recorded by a health sensor, creates data that
contains a story — a story of what somebody wants or needs or likes or is doing or thinking about
or dreaming of. Every piece of data is a key to a lock that can be unlocked — if you know how.
Design thinking in every experience: The embrace of the concept of “design thinking” has
changed the nature of technology development in recent years. With the rise of Web 2.0,
“consumerization” and the prominence of Stanford University’s d.school, modern business
systems and processes — for customers and employees — have reached new levels of ease of use,
elegance and beauty. These approaches, underpinned by new methods of software development
(e.g., Agile, scrums, pods, etc.), are about much more than simple aesthetics, however. Rather,
modern software-driven, hyper-personalized experiences generate higher sales and higher Net
Promoter Scores. Forrester Research puts the ROI of design thinking at up to 107%.8

What good looks like
This is exactly what leading
enterprises are doing right now:
unlocking business opportunity
by injecting AI into enterprise-
wide work processes, and thereby
accelerating their operational
speed and their ability to derive
real-time insight and foresight
into all aspects of their business in
material ways.
Respondents in a recent Cognizant survey believe
AI will improve decision-making by 17% during the
next three years, through the use of data to generate
beyond-human-speed insights and decisions,9
and
these enterprises will spend about 35% of their
AI budgets on data modernization toward this
goal.10
Generating intuition at the pace required of
a modern business is impossible without the right
technology and process foundation.
These realities have been made even more real by
the pandemic.“We have a heavier emphasis on
interpreting data using artificial intelligence metrics
to guide decision-making at the corporate level,” said
the Chief Information Officer of a retail company
in a recent Cognizant study.11
In this same study,
roughly two-thirds of companies said that since the
emergence of COVID-19, they have been developing
new analytics/models, evaluating and refreshing their
existing analytics/models, refreshing databases and
integrating new data streams such as geo-location,
social media and cell phone data.
Another three-quarters said that since the crisis
they had started relying more heavily on scenario
planning to assess the potential impact of extreme
events, while 71% said they were making greater use
of real-time data.
As a result of these initiatives, almost 70% of
companies that have made major or significant
changes to their data management or analytics/
models are now more confident in their business
decisions, compared with 45% among less active
companies. They are also more likely than other
companies to say they are now relying more on their
analytics/models (78% vs. 55%) than they did before
the pandemic.
COVID hit at a time when many 20th
century
organizations around the world were already
struggling to retool for relevancy in markets
operating at 21st
century speed. In the US, millennials
now outnumber baby boomers (72.1 million to 71.6
million) according to the US Census, and are the
prime audience for the big-ticket items that drive
economies forward.
Yet far too many household-brand-name companies
have failed to respond to millennials’ expectations
that getting a mortgage or filing an insurance
claim should be as easy as selecting a hotel on sites
like Booking.com or buying a car from an online
business like Carvana.
71%of respondents said
they were making greater
use of real-time data.

As one Chief Financial Officer of a financial
services company put it in a recent Cognizant
survey,“The way our customers interact now has
changed completely, so we are having a hard time
projecting the new behavior because it’s nothing
like we have seen before.”12
Organizations like this
are being blindsided as their customers migrate
to a new generation of outstanding experiences
stemming from “digital first” providers. Having failed
to engineer intuition, these old-line providers are
increasingly at risk.
In contrast, enterprises that have made effective
digital progress are able to intuit their customers’
needs and wants in time to act. For example, we
recently helped a large convenience store chain
adjust its analytics and data to accurately identify
which products were selling the fastest in its stores
during the pandemic. This allowed the retailer to
proactively take steps to ensure that it had those
high-demand goods in stock, and to place them
near checkout counters so customers could easily
find them. Ultimately, the client said this drove
an increase in per-customer purchases of those
products of about 25%.13
The intuition engine
If the combination of machine
learning, cloud and data can
be regarded as the engine of
a modern enterprise, then, in
extending this car analogy, there
is more to an automobile than the
engine alone.
To truly engineer intuition, cloud, data and machine
intelligence are brought together in the service
of a specific business goal, realized by enabling
a customer or employee or partner experience.
This is where contemporary software engineering
methodologies come into play.
Far too many “digital transformation” activities have
failed due to the superficial use of digital technology
with little to no real understanding of the desired
business outcome. While a new mobile front-end
app often looks nice, if it is sitting atop a legacy
business process, little real progress will be made.
The project may have been quick, cheap, low risk
and involved pivots along the way (seeming to be a
la mode), but without the hard work of aligning the
technology to something of real business value — and
reengineering the supporting processes — it is like
climbing a tree to get to the moon; it will seem as
though you’re making progress toward your digital
goal (and technically you are), but you will never
reach your destination. Identifying the right use
cases is critical for maximizing ROI. In fact, 77% of
companies generating the highest returns from AI
do this one thing well.14
Enterprises responding
to a Cognizant survey
said they will spend about
35%of their AI budgets
on data modernization.

In addition, these failures have in large part been due to the use of old-fashioned software development
methodologies, which are increasingly unfit for the speed of business today. Traditional product development
release cycles typically take 18-24 months — far too slow to compete successfully with startups and “Digitall”15
companies using Agile and next-gen software engineering approaches. New software engineering
approaches — featuring scrums, guilds, pods, sprints and spirals — are central to optimizing for markets in
which software is eating everything, including software.
This is even more true since the pandemic hit, when virtual collaboration quickly became a new norm.
Roughly 88% of respondents in a recent Cognizant study agreed that a pod-based approach is now the
optimal option for software engineering. When Severn Trent Water, a UK water utility, adopted this approach,
it generated a 300% faster application release cycle and a 40% increase in its “first-time-right” ratio. In fact,
88% of its employees agreed that the new culture improved collaboration and communication.16
Every organization must master this stack, and these new development approaches, at scale, to drive
business in the Fourth Industrial Revolution. Applying these models — process by process, experience by
experience — across every value chain is how the modern enterprise is built. How intuition is engineered
(as revealed by the following examples).

Quick take
More answers,
more quickly:
responding
with intuition
in wealth
management
With fundamental changes in the wealth
management market — clients living longer
and living more — the pressure on wealth
management providers (WMPs) in this
highly competitive market continues to
ratchet higher. One effective approach to
this challenge, our research has found, is
for WMPs to provide more clients more
answers more quickly. To intuit what they
need more precisely.
In our work with one client, the challenge
was to handle a volume of calls into its
call center that was reaching humanly
unmanageable levels. Machine learning and
natural language processing (NLP) software
was applied to these call-center processes
by our teams. This triaged incoming traffic,
siphoning off low-level issues to software-
based bots, and reduced the number of
calls human agents had to handle. Data
streams surrounding high-volume inquiries
into the call center were analyzed and
mapped to answers to the most frequently
asked questions. Self-learning algorithms
began recognizing words and phrases
and identifying objectives from a range
of possible conversations. Bot-handled
inquiries spiked materially when consistent
incoming queries were recognized. Human
agents were afforded more time to handle
more complex issues.
The outcome of the project so far? In the
first year after its introduction, a $6.7 million
reduction in annual operating costs and a
5% improvement in Net Promoter Scores.
The aims of the next steps of the project?
To take the impact of the NLP to the next
level — processing in real time more
and more phone-call content, including
understanding (and acting on) a client’s
underlying emotions through sophisticated
sentiment analysis. Via the use of these
cognitive systems, software bots and human
agents can begin to deliver more insights
to more clients more quickly, resulting in
higher customer retention, lower agent
turnover, and the generation of intuitions
that will delight clients and generate higher
customer lifetime values.
These approaches will fast become
prerequisites for businesses that need to
engineer intuition. By 2022, Gartner believes,
40% of customer-facing employees and
government workers will consult an AI
virtual support agent daily for decision or
process support. And by 2025, the top 10
retailers globally will leverage AI to facilitate
prescriptive product recommendations,
transactions and forward deployment
of inventory for immediate delivery to
consumers.17

A natural language for the next
best action
A large global biotechnology company had a “good news, not-so-good news” challenge
typical of the industry. It manually collected reams of data in a free-text format from its
patient services team. Its goal: Isolate trends and identify patterns that help improve
customer care and, ultimately, patient outcomes by combining this data with traditional data
sources typically used to gain a deeper understanding of patients.
However, the unstructured approach was arduous, error-prone and typically a day late and a
dollar short. The company needed a way to accelerate time-to-value to better anticipate and
address various constituent needs before they festered. Our answer: AI in the form of NLP.
By applying NLP, our client can now more easily extract meaning from everyday language,
opening a window into the company’s “call notes.” By doing so, the company can answer key
questions that were unanswerable before, such as:

❙ How do patient experiences differ by group and subgroup?

❙ Which services provide the most value to patients?

❙ Which factors influence patients to continue treatment?
Quick take

Our team spent two months working closely with the client’s stakeholders, developing
hypotheses and performing data validation. In a series of workshops, the team partnered
with our client’s patient services managers to better understand products and disease states
by identifying the word phrases that occur most frequently in the free-text notes. Using that
information, our team built the custom taxonomies and ontologies required to inform the
NLP engine, and further identified two dozen new data hypotheses to explore.
The engagement yielded 30 meaningful insights and nine recommendations, which helped
our client to proactively:

❙ Improve patient support: Predictive modeling identified the brands and patient types
most sensitive to factors such as copay assistance and health concerns — factors that
impact patients’ likelihood to continue therapy and that contribute to better outcomes.

❙ Correlate more complete notes with higher shipments: Patient notes that documented
actions, reactions and follow-ups correlated with more frequent shipments of product.
The finding reinforced the importance of establishing close connections with patients as
a motivator for continuing therapy.

❙ Develop new KPIs: The project highlighted how each point in the patient journey —
initiation of therapy, confirmation of copay assistance, etc.— affects customer experience.
It identified tipping points in the patient journey that could increase the probability of a
patient discontinuing therapy.

Anticipating
which debt
collection
efforts are
more likely to
pay off
Credit card collections is a laborious, costly
business. Agents tirelessly call and send
endless texts, emails and letters trying to
cajole defaulting cardholders to pay up.
And since agents are typically paid on
commissions, turnover soars when they are
unable to collect. Knowing in advance when
collections efforts will pay off — and more
important, when they won’t — is therefore
mission-critical work.
A large US-based issuer of branded credit
cards came to us looking for solutions.
The firm was writing off nearly $1 billion in
consumer credit debt annually; recovery
efforts were taking $30 million a year from
the bottom line; and turnover was averaging
40%-plus. Clearly, this situation was
unsustainable.
Conversations with our client revealed that
an AI-based causality engine would go
a long way toward improving collections
activities. A causality engine is a type of
AI derived from information theory. It
applies a query about why something
happens to a large volume of data,
without preconceptions or reliance on
predetermined algorithms. A causality
model’s self-learning capability allows it
to produce outcomes even in a volatile
business climate, where it ignores outlier or
missing data, and takes in new data rapidly
and adjusts accordingly.
Our causality AI engine separated relevant
and causal factors from nonrelevant
correlative ones, giving business users
insights into what drives certain outcomes
and allowing them to choose the next best
action. Rather than developing an algorithm
and model based on preconceptions of a
desired result and then testing it for efficacy,
the causality model adopts a hypothesis as
an outcome, then parses massive amounts
of data to determine what variables relate
more than others to that outcome. Using this
approach, we were able to help the client
determine which particular strategies —
texts vs. calls, days vs. evenings — were most
effective.
The “proof-of-value”- based solution we
engineered determined that directing
collections activity toward certain “personas”
(i.e., customer demographic segments)
would result in $5 million to $7 million in
increased revenue, and as much as $10
million in annual savings. In deploying
this solution, our client is beginning to
see higher customer collection volumes,
which has increased employee commission
compensation and, at the same time,
reduced turnover rates, hiring expenses
and training costs. This virtuous cycle has
resulted from engineering intuitions that
help agents pursue “winnable cases.”
Quick take

Restocking life-saving supplies
as if by intuition
Having the right supplies on hand for every surgical procedure is a tall but essential order
for hospital systems large and small seeking to make the Hippocratic Oath more than mere
words on a piece of parchment paper.
To make this lofty goal a reality, we helped a medical products maker to revamp its end-user
applications ecosystem. We also instrumented every item on its virtual line card. This not
only provided hospitals with a real-time view of critical surgical consumables on hand but
also empowered them to automatically refresh supplies (powered by AI/machine learning
algorithms) when available products hit certain nonlinear thresholds. This is helping our
client’s customers to eliminate an all-too-common pain point: getting caught short when
manual order miscalculations or supply chain disruptions strike. This results, for example, in
not having the right item to hand off to a surgeon’s outstretched hand and then facing the
burden of manual material audits.
We engineered an edge-to-cloud platform that integrated various data sources and systems
to help our client’s customers monitor supply levels on Internet Protocol (IP)-instrumented
products across their facilities. The IoT-enabled application allows hospitals and networks
to fulfill inventory proactively through an automated dispensing system. Moreover,
hospitals and provider networks can create customized subscriptions that enable them to
automatically order bundles of products and services on demand.
So far, the results have been quite encouraging. Our client has seen:

❙ A 25% reduction in rush orders, manual orders and restocking charges

❙ Smarter staging of required supplies prior to procedures for better surgeon, nursing and
patient experiences

❙ Optimized on-premise inventory management that reduces staff time by 20 hours a week
— freeing up medical personnel to spend more time on patient care
When intuition is engineered into healthcare processes, patients, payers and providers all
benefit.
Quick take

Immediately
understanding your
work ahead
In his well-known books, Blink18
and
Outliers: The Story of Success,19
Malcolm Gladwell examined the
role of our minds and methods in
achieving extraordinary results.
In Blink, Gladwell asserts that our
ability to “thin slice” information,
i.e., to use limited information from
a very narrow period of experience
to come to a conclusion, is the
route to making spontaneous
decisions that are often better
than carefully considered ones.
In Outliers, he offers the “10,000
hours” rule as a way to understand
how Paul McCartney can write
“Yesterday” during a dream, or Bill
Gates can create PC-DOS during a
fevered week.
Blink and Outliers show the bio-mechanistic
processes of engineering intuition. They articulate
how the mystery of intuition is no mystery at all. They
show that with the right systems, the right data and
the right effort, ordinary human-level performance
can be exceeded.
The same is becoming true for businesses and
enterprises. With the right systems (machine
learning-based software running on hyperscale
platforms), the right data (generated by billions
and billions of cloud-connected devices) and the
right effort (business processes and experiences
engineered with modern software development
technologies), extraordinary business-level results
can be achieved.
By trusting data and leveraging modern software
engineering principles, any organization can be a
modern enterprise. Any organization can engineer
intuition. These three actions (among many) are
foundational to propel your enterprise toward this
objective:

❙ Become obsessive about data. In our work with
clients, we have a one-question litmus test to
determine a company’s digital readiness: Do they
act on what the data tells them? If so, this is a good
sign that the company is making true progress
to engineering intuition.“Execution of complex
decisions” and “execution of routine, rules-
based decisions” are both areas in which high-
performing respondents in a recent Cognizant
survey expect to see a significant transition
toward machine-based decisioning in the next
three years — from 16% to 24% and from 15% to
23%, respectively.20
Just as most stock trading is
now undertaken by machines, complex decision-
making is increasingly being done more quickly
and effectively by machines.

❙ Become obsessive about automation and
modernization. Put simply, if you wish to compete
at Amazon cost and Google speed, you have to
automate and modernize significant portions of
your operations during the next few quarters.

Organizations that are engineering intuition are
automating processes in ways that make the
last 25 years of Six Sigma and business process
reengineering (BPR) seem like a mere overture
to a major symphony. This is foundational work,
yet it is work still to be undertaken by millions
of organizations around the world. McKinsey
estimates the percentage of companies that
have automated at least one process end-to-end
at 31%.21
Moreover, only 10% of companies use
modern software engineering (MSE) methods in
50% of their projects, and only 7% use MSE “at
scale.”22

❙ Become obsessive about enhancing your
employees. Building modern systems is simply
a means to an end. That end? Making your
employees more efficient, more productive,
more resilient, more valuable to their customers
and more satisfied in their work. As a consumer,
you will favor companies with staff who are
fast, precise and seem to enjoy their work —
because they are using systems that enable
them to anticipate what their customers need,
so they act to supply it. Blending automation
with AI and other cognitive technologies frees
employees from repetitive tasks and augments
their capabilities, unleashing productivity and
innovation. As an employee, you will want to work
for an organization that offers the tools to do great
work, rather than one where you are mired in
drudgery and inefficiency.
With these three obsessions at the forefront of your
strategy and tactics, you will have the wind at your
back. Without them, there is tough sledding ahead.
As the author of many seminal works on decision-
making, psychologist Daniel Kahneman wrote,
“You’re better off if you collect information first, and
collect all the information in a systematic way, and
only then allow yourself to take the global view and to
have an intuition about the global view. This applies
in many domains.” Including, we believe, how a
business should set out to engineer intuition.
Engineering your future
The story of human evolution is, in
many ways, the story of our tools.
It has been said, and attributed
to various luminaries,23
that “we
shape our tools and then the tools
shape us.” Now our new tools are
shaping who and what we are
again.
From the sharpened stones used by the
Australopithecus Garhi of East Africa 2.5 million
years ago to the NVIDIA DIGITS DevBox applied
by deep-learning pioneers today, we have used
tools to lift ourselves from the savannah to the Sea
of Tranquility and beyond. In this odyssey, we have
traveled a long way from where we started. The
intuitions humans have today bear little relation
to those of our ancient ancestors. Evolution has
engineered them to be far superior. Now modern
enterprises are realizing that a new, accelerated
period of business evolution is at hand. Intuition
engineered offers an opportunity presented to
optimize the digital metamorphosis taking place
today, which will only accelerate in the coming
decades and set a new expectation for how
businesses that build our world should act: with the
sensibility and speed of human intuition.

Endnotes
1 Oxford English Dictionary definition of intuition.
2 https://en.wikipedia.org/wiki/Neuroimaging
3 https://www.youtube.com/watch?v=1UqekPMfNk4
4 Carlie Idoine and Erick Brethenoux,“When and How to Combine Predictive and Prescriptive Techniques to Solve Business
Problems,” Gartner, Aug. 11, 2020, ID G00723079.
5 http://cnb.cx/3rB8KhB
6 https://www.cognizant.com/whitepapers/ai-from-data-to-roi-codex5984.pdf
7 http://bit.ly/3rzkQaV
8 http://bit.ly/3encsaY
9 https://www.cognizant.com/the-work-ahead-ai-report/ai-is-core/
11 https://www.cognizant.com/whitepapers/disruption-data-and-analytics-modernization-in-the-covid-19-era-codex6298.pdf
12 Op. cit., endnote 9.
15 https://www.cognizant.com/whitepapers/from-to-everything-you-wanted-to-know-about-the-future-of-your-work-but-were-
afraid-to-ask-codex4799.pdf (page 32)
16 https://www.cognizant.com/whitepapers/becoming-a-software-centric-business-best-path-forward-in-an-uncertain-post-
covid-19-world-codex5451.pdf
18 https://www.amazon.com/Blink-Power-Thinking-Without/dp/0316010669
19 https://www.amazon.com/Outliers-Story-Success-Malcolm-Gladwell/dp/0316017930/ref=pd_sbs_1?pd_rd_w=xUPzm&pf_
rd_p=3ec6a47e-bf65-49f8-80f7-0d7c7c7ce2ca&pf_rd_r=18B3FN26KR8Y7M8HZTRR&pd_rd_r=ea77b30d-cbbd-49ad-930d-
2f033eb391f2&pd_rd_wg=vZoNr&pd_rd_i=0316017930&psc=1
20 https://www.cognizant.com/the-work-ahead-ai-report/ai-data-mastery/
21 https://www.mckinsey.com/business-functions/operations/our-insights/the-imperatives-for-automation-success
22 https://www.cognizant.com/perspectives/software-engineering-how-banking-and-financial-services-can-create-more-
engaging-experiences
23 https://mcluhangalaxy.wordpress.com/2013/04/01/we-shape-our-tools-and-thereafter-our-tools-shape-us/

Benjamin Pring
Vice President, Head of Thought Leadership, and Managing Director,
Cognizant’s Center for the Future of Work
Ben Pring leads Cognizant’s Center for the Future of Work and is a coauthor
of the books Monster: A Tough Love Letter On Taming the Machines that
Rule our Jobs, Lives, and Future; What to Do When Machines Do Everything;
and Code Halos: How the Digital Lives of Peoples, Things, and Organizations
Are Changing the Rules of Business. In 2018, he was a Bilderberg Meeting
participant. He previously spent 15 years with Gartner as a senior industry
analyst, researching and advising on areas such as cloud computing and
global sourcing.
Alan Alper
Vice President, Thought Leadership Programs
In his role as a Corporate Vice President, Alan Alper is responsible for much
of Cognizant’s thought leadership, globally. This includes white papers,
case studies, blogs, short-form content that appears in the Latest Thinking
section of Cognizant.com (and Digital Perspectives app), videos, podcasts,
live-streaming video webinars and specialty publications, such as Cognizanti,
the company’s flagship thought leadership journal. Over his 30-plus year
career, Alan covered the business of IT for a variety of publications, including
Computerworld, Managing Automation and Computer Industry Daily, the
industry’s first daily online publication. He earned his bachelor’s degree in
rhetoric and communications (minoring in journalism) at the State University
of New York at Albany.
Acknowledgments
The authors would like to thank Cognizant’s Thea Hayden, Lynne La Cascia, Irene Sandler, Andreea
Roberts, Rob Brown and Euan Davis for their invaluable contributions to this report.
About the authors

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