Digital Business

| The Road to AI 4

THE PATH TO AI SUCCESS

There’s no such thing as general-purpose AI. Every company’s artificial intelligence journey is unique,

as is its application of AI. Maybe the business has invested in big data systems and wants to apply

AI but is unsure where to start. Maybe it’s at the stage of identifying the areas in which AI can add

maximum business value. Perhaps it’s finalizing the underlying technology stack.

Getting up to speed on AI involves moving iteratively along a maturity curve, and for good reason:

Every application and use case requires different tools and algorithms, and every organization is at a

different place in terms of its AI maturity curve. The chatbot for a financial service provider’s IT help

desk, for example, can’t be applied to the same company’s home-loan call center. An algorithm trained

in pharma to read documents for adverse event recognition can’t be reused in a banking context to

identify anomalies in mortgage applications, even though the technology stack and technique may be

the same. Choosing the right next-level AI applications propels an organization forward on its AI

maturity curve.

With AI, each use case requires a unique training process as the system learns the relevant patterns.

Moreover, AI systems take much longer to master some tasks than others. For example, neural net-

work-powered computer vision requires extensive training and data sets to recognize and analyze

patterns in images.

AI also offers a very different experience from other digital exploits. While businesses can scale their

learning of cloud and analytics, AI requires a fresh look at existing approaches to help take advantage

of new techniques, different data sets and accelerating advances in core technologies.

Many organizations we work with have already embraced AI elements and are achieving meaningful

business outcomes:

• At a financial services company, fraud detection is 25% more accurate as a result of the real-time

algorithms our teams created.

• An energy company is saving $1 million annually per oil rig due to the predictive maintenance

program we developed.

• A leading insurer has netted a $30 million topline increase and 20% lift in lead conversion when

it launched a new customer activity hub.

(For more detail on these engagements, as well as how we’re helping our clients apply AI in their busi-

nesses, please visit the “Featured Work” section of the Cognizant Digital Business, AI & Analytics

section of our website.)

Digital Business

| The Road to AI 8

• Increased tolerance for failure. The spirit of experimentation embraces the idea that not all ini-

tiatives will pan out. With AI, every organization will make some bad bets. Not only is acceptance

of failure key, but failure in AI/ML isn’t binary: Sometimes pilots are technological successes but

yield few benefits. Perhaps AI will inspire more organizations to view failure as a badge of honor.

When Google announced it would shut down its online platform Wave just a year after its launch,

it rewarded the development team for having taken a calculated risk. Accounting software com-

pany Intuit holds “failure parties.”1 “Every failure teaches something important that can be the

seed for the next great idea,” says co-founder Scott Cook.3

Indeed, there is so much to learn in AI that every experiment is a stepping stone. A good example

is the work done by one of our energy and utility clients to reduce customer attrition with a

machine-learning system. We developed an outcome-oriented experiment that investigates new

algorithm techniques and probes data for more details on why consumers switch providers. Apply-

ing an ethnographic approach helped us to understand how and why consumers make energy

decisions. The approach points to new, untapped data sets for further investigation. Is the project

a success? Possibly. Together with the client, we defined the experiment’s success as improved

algorithmic predictability and a better understanding of AI techniques and the data’s value. The

project is ongoing, and while it might not result in the explicit answers our client is seeking, it will

deepen the AI expertise and understanding of the business problem for all involved by viewing the

issue through a human lens.

Evaluate

It’s easy for organizations to get lost here. Determining whether a pilot has produced definitive results

is tricky, as is the question of whether to extend a pilot for further iteration or to acquire additional

data sets. For example, a client that provides credit card services to small and medium businesses

(SMBs) discovered that while it typically segments customers by industry and revenue, a more telling

metric is whether the SMB’s founder is still involved. When the original owners remain hands-on, the

SMB often has little time to evaluate new financial products. The client’s next step is to determine

whether the additional campaign’s ROI will offset the costs. The lesson? Be willing to stop the pilot if

the cost-benefit doesn’t work out.

3

9The Road to AI |

Digital Business

Even thornier questions arise after the initial pilot assessment. Within the organization, what’s the

next phase so that AI adds value? Once a company has engineered a successful AI pilot, how can it

propagate that experience through the organization?

At many businesses, the evaluation stage can be a tug of war: IT wants to push AI initiatives forward to

demonstrate its proficiency with leading-edge technologies, while the business prefers to wait to under-

stand the implementation more fully. Promoting more transparent testing of technologies and techniques

can break the gridlock, enabling organizational leaders to observe project successes as they occur and

helping them to feel more comfortable about moving projects from the lab into the business.

Organizational constructs to oversee AI initiatives are important to achieve the necessary consen-

sus. Some companies establish separate teams within the innovation function, while others form

joint AI councils across IT and business units. Because data is key for AI, some businesses add the

AI mandate to their data organization. The idea is not to add to the org chart but to better under-

stand how everyone in your organization can learn from each other and avoid repeating mistakes.

The key is to create a nimble organization in which all stakeholders – business owner, process owner,

data owner and technology owner – come together to experiment with business outcome- focused

use cases (see Figure 1).

Organizational Considerations for Establishing an AI Office

Key questions to ask when planning your next AI initiative.

How should we structure the AI organization

to address the business’s LOB requirements?

What are the key roles necessary

to set up and operationalize

projects within the AI office?

What are the frameworks

available for PoVs

(proofs of value) vs.

pilots as well as projects?

What are the top guiding principles

the AI office should establish?

How should we approach

change management and

business stakeholder

management?

How should AI solutions

be governed for

accountability & reusability?

Establishingan AI office

Figure 1

Digital Business

| The Road to AI 10

Regardless of where AI is housed in the org chart, most companies recognize the need for multifunc-

tional participation. AI isn’t an island, and it can’t be a skunkworks effort. It requires a specific business

need, and unlike the nascent stage of other technologies – think blockchain or quantum computing –

AI has followed a speedy trajectory from cool technology to prospective business solution. Thanks to

its appearances in well-known demonstrations – such as Google DeepMind’s triumph in the board

game Go4 and IBM Watson’s 2011 win on TV’s Jeopardy5 – AI has fired up our collective imaginations.

Yet organizations still struggle with the question of which business use cases are best for AI, and how

to know whether they’re working.

Establish Priorities

Given AI’s growing profile, it’s common to find multiple business units within an organization – opera-

tions, technology and lines of business – each pursuing its own initiatives. Use cases abound, and

prioritization is a challenge. Which pilots share a common AI core that all functions can leverage?

Which ones can the larger company learn from? The end goal is to establish AI as a capability that the

organization as a whole can embrace.

To encourage AI experimentation while imposing order and discipline on the prioritization process,

CIOs and business leaders can ask several questions: Does a proposed project deliver limited, incre-

mental value, or is it reinventing a process through clever use of data and technology? Early successes

that feature positive business benefits, such as a boost to the top or bottom line or productivity

improvements, help organizations embrace AI faster than those that are technologically possible but

have limited value. (See Quick Take, page 11.)

After starting with the question of business value, businesses should then move on to technical feasi-

bility. Does the prospective AI system have the data it needs from which to learn patterns? Is the data

free of bias? Is the technology infrastructure able to process different types and large volumes of

data? If the answer to any of these questions is no, then the project’s technical feasibility is a limiting

factor. (For more information on generating business value from AI, read our white paper “AI: Ready

for Business.”)

Explore Further

In the exploration phase, organizations are typically deciding whether to continue focusing their AI

efforts within a single functional area, or to apply them more broadly across the company. Many are

also watchful of unfolding government regulations regarding compliance and liability, as legislative

and judicial branches tackle AI-related challenges. The important part of this step is for companies to

consider how they can better organize themselves around AI. What processes can they create that are

useful for applying their AI learnings to other parts of the organization? (See Quick Take, page 12.)

4

5

QUICK TAKE

Vision to ValueWhen a biotech company launched a pilot that applied machine learning to

natural language processing, it wanted to explore the technology’s feasi-

bility. More important than the AI techniques, however, was the company’s

vision for the pilot to contribute to its mission of improving health outcomes.

It’s the job of the company’s patient services group to stay in close tele-

phone contact with individuals who have been prescribed its specialty drugs.

To better understand the drugs’ efficacy, case managers speak regularly

with patients and document their experiences. The teams take notes on

each conversation. The metrics are similar to a call center.

The pilot’s results have helped the company double-down on its mission.

It led to new efficiency measures, such as more automated note-taking. It

also helped zero in on patients at risk of noncompliance with drug regimens

and opened opportunities for proactive intervention. Equally important, the

results prompted the company to expand its case-manager training to include

greater emphasis on empathy for patient concerns. The new approach to

training has the dual benefit of potentially improved health outcomes for

patients and greater job satisfaction among case managers.

The biotech company’s use of AI is a helpful example of vision to value:

Examine the organization’s strategic value and how the AI pilot connects

to it. The company is now using the successful results to cultivate AI liter-

acy across the organization. It’s showcasing the value of new techniques to

transform patient engagement as well as to create “aha” moments in busi-

ness leaders’ minds about the possibilities of AI/ML.

11The Road to AI |

Digital Business

13The Road to AI |

Digital Business

LOOKING AHEAD

When it comes to digital pursuits, there’s nothing like AI. Rather than applying their learnings from

other digital initiatives, businesses need to get ready for a whole new way of thinking to reap the full

success that AI can offer. From rethinking old ways of work, to recognizing new types of value,

AI requires a fresh look at existing approaches.

Businesses can develop the mindset that will instill success by educating their workforce on AI,

embracing experimentation, understanding how to evaluate AI pilots, determining project prioritiza-

tion, and pushing AI insights further into the organization. While each business will take its own path

to AI, all organizations can follow this process to optimize business results.

To learn more, please visit the AI & Analytics section of our website.

© Copyright 2018, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means,electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

Codex 3614

ABOUT COGNIZANT DIGITAL BUSINESS

Cognizant Digital Business helps our clients imagine and build the Digital Economy. We do this by bringing together human insight, digital strategy, industry knowledge, design, and new technologies to create new experiences and launch new business models. For more informa-tion, please visit www.cognizant.com/digital or join the conversation on LinkedIn.

ABOUT COGNIZANT AI & ANALYTICS

As part of Cognizant Digital Business, Cognizant’s AI & Analytics provides advanced data collection and management expertise, as well as artificial intelligence and analytics capabilities that help clients create highly-personalized digital experiences, products and services at every touchpoint of the customer journey. We apply conversational AI and decision support solutions built on machine learning, deep learning and advanced analytics techniques to help our clients optimize their business/IT strategy, identify new growth areas and outperform the competition. Our offerings include AI to Insight, Customer Intelligence, Intelligent Automation, Product Intelligence, and Risk & Fraud Detection. To learn more, visit us at www.cognizant.com/cognizant-digital-business/applied-ai-analytics.

ABOUT COGNIZANT

Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innova-tive and efficient businesses. Headquartered in the U.S., Cognizant is ranked 205 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant.

World Headquarters

500 Frank W. Burr Blvd.Teaneck, NJ 07666 USAPhone: +1 201 801 0233Fax: +1 201 801 0243Toll Free: +1 888 937 3277

European Headquarters

1 Kingdom Street Paddington Central London W2 6BD EnglandPhone: +44 (0) 20 7297 7600 Fax: +44 (0) 20 7121 0102

India Operations Headquarters

#5/535 Old Mahabalipuram RoadOkkiyam Pettai, ThoraipakkamChennai, 600 096 IndiaPhone: +91 (0) 44 4209 6000Fax: +91 (0) 44 4209 6060