How Companies Can Move AI from Labs to the Business Core

Cognizant Reports
How Companies Can
Move AI from Labs to
the Business Core
APAC and Middle East organisations have big expectations
from AI, but they’re only just getting started. To realise the
full potential of AI-led innovation, they must rapidly, but
smartly, scale their deployments and embrace a strong ethical
foundation, keeping a close eye on the human implications and
cultural changes required to convert machine intelligence from
lofty concept to business reality.
April 2020

Cognizant Reports
2 / How Companies Can Move AI from Labs to the Business Core
Foreword
AI in the time of COVID-19
Although this research study was conducted before the outbreak of the COVID-19 pandemic,
advanced forms of artificial intelligence are critical to our understanding and treatment of infectious
disease. Machine learning and deep learning are helping infectious disease scientists to more quickly
sequence and model treatment therapies and vaccinations, accelerating time-to-market and limiting
unintended consequences. They are also helping researchers to more effectively forecast and foretell
the contours of pandemics like COVID-19 before they strike. These capabilities are applicable to every
business in every industry.
COVID-19 is a global pandemic; it knows no boundaries. Swift data collection and algorithm sharing
are vital elements of a collaborative effort that will result in an Rx to defeat this deadly disease. Those
companies that have made strides in their AI journey are better positioned to weather the current crisis
and will emerge stronger to compete in the post-COVID-19 world.

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How Companies Can Move AI from Labs to the Business Core / 3
Executive Summary
The age of artificial intelligence (AI) is here, and countries in the
Asia Pacific (APAC) and Middle East zones have set their eyes on
the prize. Startup activity around AI is booming; and the region is
set to overtake the rest of the world in AI spending over the next
three years – reaching $15.06 billion by 2022, according to IDC.1
APAC and Middle East countries have made AI a top priority, as
evidenced by ambitious national initiatives such as China’s bid
to create a $150 billion AI industry by 2030, and Australia’s AI
roadmap.2
Meanwhile, the region’s tech giants such as Alibaba
have unveiled big plans to take on the AI industry’s leaders such
as Google and Microsoft.3
Against this backdrop, we surveyed 590 senior executives across the region in late 2019
to understand their companies’ AI plans and actions. Our goal was to learn how APAC and
Middle East businesses are employing AI, how this emerging technology is impacting business
and how they are overcoming challenges to reap value from machine intelligence across
various functional areas. Importantly, our study gauges how differences in revenue growth4
impact senior leaders’ perceptions of and investments in AI (see Methodology, page 25).
Our findings reveal a tale of enthusiasm accompanied by formidable challenges.
Nevertheless, APAC and Middle East companies are moving forward with a high level
of commitment and great expectations. Although most organisations that we surveyed
are at experimental or early stages of AI deployment, many appear keen to harness the
transformational power of AI. Key findings from our study include:
❙ A considerable gap remains between enthusiasm and deployments. Our survey found
that APAC and Middle East companies across eight industries consider AI to be vital to
their organisations’ future and are eager to realise the promise of AI. However, a majority
of AI projects are still languishing in lab pilot or proof-of-concept (PoC) stages. APAC and
Middle East companies must accelerate deployment to keep pace with AI’s inexorable
move into the mainstream.

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❙ Senior leaders need to transcend ongoing spray-and-hope initiatives. Senior leaders
are applying AI across a variety of applications with a similar degree of focus. This reflects
a clear inability to prioritize AI’s near- and long-term business benefits. It also suggests a
pack mentality in which senior leaders feel obliged to follow what rivals are doing inside
and outside their markets.
❙ Focusing on innovation is the silver lining. APAC and Middle East companies have
attached a high level of focus on both product and process innovation, indicating their
desire to leverage AI to drive radical business change.
❙ Organisations have yet to address a yawning talent gap. APAC and Middle East
companies across industries and growth categories expect the AI-oriented segment of
their workforce to grow. Yet, they see talent acquisition as a top challenge. This suggests
that they have yet to equip themselves with the skilled resources needed to tap AI’s vast
business benefits.
❙ Many organisations seem inclined to go it alone. A sizeable chunk of senior leaders
surveyed indicate that they tend to rely on in-house development for their AI efforts. This
could undermine the results since AI-led disruptive business change typically requires
continuous access to advanced technologies, skilled resources and the input of experts
possessing rich experience in aiding such initiatives.
❙ A risk-averse culture is undermining progress. Risk aversion appears to lie underneath
findings such as buy-in being cited among top challenges and very few projects making
it to full implementation – against a backdrop of high expectations that the region will
emerge as a force to reckon with in the AI space.
❙ Dedication to the ethical use of AI is simultaneously a strength and a weakness.
APAC and Middle East companies report a high focus on embedding ethical standards
and policies into their AI fabric (compared with a lower ethics focus expressed by
U.S. and European companies in our earlier study). Many are ensuring ethics are built
in at the design stage of AI projects. However, our study found that relatively fewer
organisations are working to ensure that AI apps learn and embrace ethical use of
machine intelligence post launch. Customer feedback emerged as a top choice for
improving the ethical use of AI. This suggests many organisations are working reactively
rather than proactively in dealing with ethical issues such as unintended bias, which
could be a potential source of risk.

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Seven prescriptions to accelerate AI with a human-centric focus
Understanding the human element of AI is critical to delivering business benefit. After all, not only do humans
act as the creators of AI apps, they also act as end users. How organisations approach AI development
has a cascading impact on the technology’s deployment and ultimate success. Which is why we believe
organisations should build their AI efforts on a strong foundation of human-centric thinking. The end goal is
becoming a data-driven organisation where human creativity thrives around AI. This report outlines seven
vital recommendations to help organisations in the region to develop and accelerate human-centric AI:
1. Formulate a robust strategy that lets business value guide the choice of AI opportunities in line with the
organisation’s digital transformation strategy.
2. Combine business and behavioural insights to build AI on the foundations of human-centricity.
3. Build a data-driven organisation by putting modern data management techniques and analytics to
effective use and encouraging a culture of insights-driven decision-making.
4. Deploy AI tools and algorithms in line with the maturity curve as they target different areas for
transformation.
5. Create an AI-centred culture through use of cross-functional teams, retraining and upgrading skills, and
encouraging knowledge sharing.
6. Create a strong foundation of ethics, supported by a comprehensive governance framework.5
7. Employ external partnerships as AI levers to bring in necessary technology, talent and expertise to help
businesses as they progress in their AI journey.
Gap in AI enthusiasm vs. AI deployment
APAC and Middle East businesses have big plans for AI
AI is seen as critical to businesses’ success, not just today but also three years down the line (see Figure 1, next
page). Our survey found that APAC and Middle East companies across eight industries are eager to realise
the promise of AI. This is especially evident for companies growing faster than average. This enthusiasm
is reflected in the responses to expected benefits: Of the eight options provided, the average score for
efficiency was highest (83%), whereas increased revenue scored lowest (72%) (see Figure 2, next page).
The percentages for all benefit areas were in a similar high range, indicating the APAC and Middle
East companies want to tap all key business results possible from AI, but this also hints at a sense of
indecisiveness with regard to what they consider to be its most viable benefit. This is perhaps indicative of
the peer pressure usually seen in the initial stage of adoption of new technologies.
Nevertheless, the silver lining is that AI is expected to have a significant or high impact on innovation, both in
processes and products, over the next two to three of years in the APAC and Middle East region (see Figures
3 and 4 , both on page 7). A high level of emphasis on innovation reveals APAC and Middle East companies’
strong desire to employ AI as a lever to transform their businesses radically (see Quick Take, page 11) while
pursuing vital efficiency goals.

Cognizant Reports
Importance of AI today
Below Average 82%
Average 71%
Above Average 84%
Importance of AI three years from now
Below Average 73%
Average 79%
Above Average 96%
Response base: 590 executives in APAC and the Middle East who reported one or more AI projects
Source: Cognizant
Figure 1
Note: Percentage of respondents from each growth category who replied "Extremely Important" or "Very
Important“ to the question “How important do you think the adoption of AI technologies is to your
company’s business success today and how important do you think it will be three years from now?”
High Expectations from AI all across APAC and the Middle East
Most APAC and Middle East leaders see AI with staying power
89%
82%
79%
78%
90%
76%
61%
73%
79%
78%
82%
83%
79%
72%
59%
69%
68%
81%
74%
73%
76%
68%
75%
67%
72%
75%
71%
69%
84%
67%
77%
81%
72%
71%
79%
71%
76%
75%
78%
64%
80%
76%
77%
70%
79%
66%
77%
76%
ANZ ASEAN China & HK India Japan Middle East
Increased efficiency Lower business operating costs
Increased revenues Ability to introduce new products or enter new businesses
Ability to compete effectively with new firms entering our business Boost our innovation capabilities
Improve customer satisfaction scores Speed up our time-to-market
Source: Cognizant
Figure 2
Percentage of respondents who said “substantial benefit” or “significant benefit”

Cognizant Reports
Percentage of respondents who expect “Significant” or “High“ level of AI-led impact on product and process innovation
in their companies over the next two to three years
Source: Cognizant
Figure 3
Expected impact on product innovation Expected impact on process innovation
Other 96% Other 96%
Services 67% Technology 75%
Retail 85% Services 78%
Insurance 85% Retail 78%
Healthcare&lifesciences 85% Manufacturing 84%
Manufacturing 86% Healthcare&lifesciences 88%
Financialservices 88% Media&entertainment 89%
Media&entertainment 89% Financialservices 91%
Technology 90% Insurance 92%
Supermajority of industries see AI driving innovation
MiddleEast 81%
India 84%
Japan 85%
ASEAN 85%
ANZ 88%
China & HK 93%
Japan 74%
ASEAN 81%
India 86%
China&HK 86%
MiddleEast 88%
ANZ 88%
Note: Percentage of respondents who expect “Significant” or “High“ level of AI-led impact on product and process innovation in
their companies over the next two to three years
Source: Cognizant
Figure 4
AI seen as a vital lever to drive product and process innovation across APAC and
the Middle East
Expected impact on product innovation Expected impact on process innovation

Customer service
Sales & marketing
Manufacturing production
Human resources
Research & development
Risk & compliance
Finance
Supply chain/procurement
Operations
Other
23% 26%
13%
8%
23% 25 %
12%
32% 35%
6%
16%
6%
45%
9%
12%
19%
12% 4%
4%
16%
9%
8%
12%
33%
12%
8%
44%
8%
24%
7%
5%
5%
4%
3%
15%
9%
12%
6%
15%
11%
9%
8%
5%
6%
%2%
8%
25%
16%
8%
14%
6%
3%
8%
10%
8%
9% 12%
6%
12%
15%
5% 4% 12% 4%
3%
6% 4%
2% 4% 3%
6%
1% 2%
2%
1% 1% 1%
1%1%
14%
14%
12%
2%
Functional areas under focus of AI across industries
AI being employed to address a wide range of opportunities
Functional areas under focus of AI across industries
Divergent preferences across industries seen over choice of functional areas
that AI opportunities/projects are primarily addressing
Cognizant Reports
APAC and Middle East companies cast their AI net wide
Companies that report below average or above average growth are more optimistic about AI at present as
well as three years down the line, while companies that are growing at an average rate exhibit lesser optimism
at present. Average growth companies must also consider the immediacy of AI adoption today or they put
themselves at risk in the journey of AI-led transformation.
A look at how different industries are deploying AI (see Figure 5) reveals a more detailed picture of their
preferences. Beyond customer service and their respective core areas, industries are also looking to
leverage AI in areas such as sales and marketing and operations.
But if the current status of projects in the region means anything, APAC and Middle East businesses, in
general, are just getting started, although this differs by region. Overall, only 22% of projects have reached
full deployment. The Middle East (at 30%) leads the region (see Figure 6 , next page). A look at growth
categories shows that businesses that said they were growing slower than average had a substantially
larger percentage of projects in early ages than their faster-growing counterparts (see Figure 7, next page).
Source: Cognizant
Figure 5
Financial
Services
Healthcare &
life sciences
Insurance Manufacturing Media &
entertainment
Retail Technology Services Other

Cognizant Reports
21% 24% 21% 28%
12%
30%
20%
28%
22%
39%
43%
28%
12% 17% 19%
19%
32%
42%
44%
29%
32%
17% 16%
4%
Initial planning
ANZ ASEAN China & HK India Japan Middle East
Proof of concept Pilot stage Full implementation
Stages of AI project development vary across APAC and Middle East
countries and regions
Source: Cognizant
Figure 6
17%
27%
15%
12%
33%
26%
55%
28%
38%
16%
12%
21%
Proof of conceptInitial planning Pilot stage Full implementation
Current status of AI opportunity/project
Below Average Average Above Average
Note: We asked respondents to select their company’s revenue growth category (based
on the last two years) from the following options: much above average, somewhat above
average, about average, somewhat below average and much below average.
Response base: 590 executives in APAC and the Middle East who reported one or more
AI projects
Source: Cognizant
Figure 7
Most AI projects remain in experimentation mode

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WiththeexceptionsofIndia(25%)andJapan(22%),whichpreferR&D,customerservicehasemergedas
thekeyfocusareaforbusinessesthroughtherestofAPACandtheMiddleEast,followedbymanufacturing
productionandR&D.Thisisfurthercorroboratedbythechoiceoftechnologiesusedbybusinesses,with
thetopchoicesbeingcomputervisionandvirtualagents(51%each)–bothtechnologiesextensivelyusedto
drivecustomerexperience.CompaniesinJapanandtheMiddleEastreportedamorebalancedspreadof
technologies.
While businesses across the board expect a broad range of benefits from AI, the areas where they see
the most value and the approaches they are adopting vary by growth, industry and region. For example,
companies with average (20%) and above average (25%) growth have prioritized customer service, but
respondents whose companies are growing below average (17%) showed a preference for R&D and sales
and marketing (16%), investing in and assembling the necessary infrastructure and talents as they go. We
believe that customer engagement must be central in digital transformation efforts to ensure customer
intimacy, which is vital to the success of AI initiatives. Therefore, slower-growing companies also should
reconsider their AI priorities by placing the customer at the core.
Given the transformative potential that AI offers, companies would do well to focus on particular AI
capabilities that can advance and differentiate the business. A case in point is one of the largest multinational
banking and financial services companies in Singapore, which suffered from the mismatch between the
increasing demands of growing digital payment channels and its traditional ways of handling it. We helped
the client achieve a unified payments view and build differentiating capabilities by moving from reactive to
proactive prevention of errors, client monitoring and compliance. This paid rich dividends in terms of reduced
customer churn, prevented costs of fines and penal interest, etc. (See Quick Take, next page.)
Working with external partners
Partnerships with systems integrators and vendors can accelerate AI efforts. Our study found, on average,
that a majority of respondents (52%) prefer working alongside systems integrators/vendors on their AI
initiatives, compared with 39% who said they tend to do everything in house and 10% who rely fully on third-
party vendors for their apps. Regardless of growth trajectory, all businesses prefer working with a mix of
software applications vendors, systems integrators and in-house development (see Figure 8 , page 12).
While building in-house AI development capabilities is critical, this seems more aspiration than reality
across APAC and the Middle East. As a transformative technology, AI requires a symbiotic partnership with
various third-party experts that can bring key capabilities to the mix – especially on the talent front, which a
majority of respondents cited as a challenge (see Figure 14 on page 17).

Quick Take
Cognizant Reports
Leading bank applies AI to
create a more unified and
proactive payments system
A leading APAC and Middle East bank needed to consolidate multiple digital payments
channels that created siloed ways of handling failures. Monitoring vital client account
activities across channels also presented a formidable challenge.
We helped the bank by developing an AI solution that provided a single payment command
centre, enhanced the monitoring of key clients, and unified payments and workforce
management capabilities. In addition, the solution facilitated early detection of delays
and failures of high-value transactions, aided by a detailed analysis of payment processing
errors. This enabled the bank to more quickly comply with regulations within those countries
in which it operates. The solution also empowered the bank to put in place exception
management of high-risk transactions that require special attention based on defined
patterns like beneficiaries and country-specific regulations.
Improved and proactive error handling improved customer satisfaction considerably, as
evidenced by the substantial reduction in customer churn rates. Improved compliance
resulted in savings from prevented fines and penal interest besides reducing costs of
operations by switching from a manual-intensive to an insights-intensive process.

12 / Creating Competitive Advantage from the Inside Out
Cognizant Reports
Slower-growing companies have cited the highest level of expected increase in AI-driven headcount,
which may also point to the fact that the future talent gap may be more pronounced in this group
vis-à-vis the other two company growth categories (see Figure 9 , next page). Companies in other
growth categories also expect headcount increases, which is expected to result in talent gaps in their
organisations as well. Closing the gap is essential to ensure a smoother transition to the AI-led future.
Tothisend,companiesmayleveragepartnershipswithcompetentexternalvendors.Closingthegapisan
immediateneed,ascompaniesdonothavetheluxuryoftimetograduallyfigurethingsoutandmoveon.In
thelongrun,theserelationshipscanevolveintoanAIecosystemthatcanquicklyadapttonewtechnologies
anddemands.
Given the transformative potential that AI offers, companies
would do well to focus on particular AI capabilities that can
advance and differentiate the business.
4% 8% 13%
40%
28%
43%
57%
63%
45%
In-house development and external partnerships are top choices
Note: We asked respondents to select their company’s revenue growth category (based on the last two years)
from the following options: much above average, somewhat above average, about average, somewhat below
average and much below average.
Source: Cognizant
Figure 8
Below average Average
Always or usually secure AI applications from vendors
Always or usually develop AI applications in house
A mix of AI applications in house and securing AI applications from vendors/systems integrators
Above Average
Approaches to develop AI apps

Need to accelerate the pace of adoption
With access to world-class infrastructure, government support, a focus on building talent, the
absence of a legacy burden, and a high level of internet and mobile penetration, APAC and the
Middle East’s AI capabilities rival the rest of the world. While China’s AI capabilities are on par with
the West, other countries in the region need to pick up the pace of innovation and adoption. To
boost AI adoption, China has appointed national AI champions6
– a select group of technology
companies that help achieve the government’s goals.
From a corporate perspective, AI projects across the region still seem to be restricted to
experimentation labs. Bringing AI out of the lab and into the lines of business is the imperative for
value realisation. Case in point: Our findings reveal that a majority of projects in the region have
yet to reach full implementation (see Figure 6 , page 9).
Banking and financial services (BFS) organisations are marginally ahead of other industries in
terms of projects implemented (31%), while retail and technology companies have a majority of
projects in the proof of concept (PoC) stage (see Figure 10 , next page).
Cognizant Reports
Organisations with below average growth rate expect more AI
talent growth
Note: We asked respondents to select their company’s revenue growth category (based on the
last two years) from the following options: much above average, somewhat above average, about
average, somewhat below average and much below average.
Figure 9 Source: Cognizant
69%
17%
14%
Below average
Decrease
40%
24%
36%
Average
No change Increase
57%
10%
33%
Above Average
AI’s impact on workforce growth

Cognizant Reports
APAC and the Middle East’s triad of challenges --
Talent, culture, ethics
Talent issues
AsianandMiddleEasternbusinessesreportedabroadsetofchallengesintheirAIjourneys(Figure14,page17).
AIisexpectedtotesttheirabilitytoretrainexistingemployeeswhileattractingandretainingfreshAItalentin
thenearfuture. AIrequiresbusinessestomasterabroadsetoftechnologies.Reinventingtraditionalbusinesses
throughAIrequiresnewskillsets inareassuchasmachinelearningsecurityanddata.
Forexample,ourCenterfortheFutureofWork(CFoW),hasidentified42jobsthatwillemergeoverthenext10
yearsthatwilldependonmasteryofnewtechnologies,includingAI.7
Theseincluderolessuchasmachinerisk
officersandcybercalamityforecasters,butalsoactivitiesthatrequireadeepunderstandingofhumannature
suchasheadofbusinessbehaviour,whosejobwillinvolvemakingsureethicalstandardsarefollowedwhen
humanbehaviourdataiscollected.
SinceAItalentisinhighdemand,andrecruitmentisoftenalengthyandexpensiveprocess,retrainingnotonly
savestimeandmoneybutcouldalsobecomeakeydifferentiatorforcompaniesthatcreateastrongtalent
pipeline.WhenaskedwhetherAIwillmakeiteasierorharderforthemtoattracttalent,theopinionissplit(see
Figure12,nextpage).Theyalsociteretrainingemployeesastheirbiggestchallenge(seeFigure14,page17).
AI projects languishing in labs, across industries
17% 14%
19% 19%
6% 11%
17% 17%
6%
33%
Financial services
Initial planning Proof of concept Pilot stage Full implementation
Healthcare &
life sciences
Insurance Manufacturing Media &
entertainment
Retail Technology Services Other
32% 34% 34%
29%
31%
20%
24%
22%
29% 25%
16% 20% 15%19%
33%
22%
25%
35%
19% 22%
28%
63%
46% 46%
35%
15%
Source: Cognizant
Figure 10

Cognizant Reports
Mixed opinion on talent issues
APAC and Middle East companies' views on whether AI applications will make it easier or
more difficult for them to attract and retain talent
Strong AI ethics focus: leaning more on design stage than post-launch
learning
Ethics at design stage AI learns ethics after launch
Yes, ethical policies and
procedures in place
No, but planning to
put ethical policies and
procedures in place
No, not planning to put
ethical policies and
procedures in place
0% 10% 20% 30% 40% 50% 60% 70%
68%
30%
2%
Yes, ethical policies and
procedures in place
No, but planning to put ethical
policies and procedures in place
No, not planning to put ethical
policies and procedures in place
0% 10% 20% 30% 40% 50% 60% 70%
54%
44%
2%
Source: Cognizant
Figure 12
0%
Significantly more difficult 7%
Somewhat more difficult 30%
Little or no change 21%
Somewhat easier 34%
Significantly easier 8%
5% 10% 15% 20% 25% 30% 35%
Source: Cognizant
Figure 11

Cognizant Reports
Ethics-driven AI
Businesses in the region appear to place greater emphasis on ethical aspects at the initial stages than post
launch, rather than maintaining a consistent focus on ethics throughout the app’s lifecycle (see Figure 12,
preceding page). APAC and Middle East companies appear to have relatively less focus on ethical policies
and practices into how AI apps behave post launch (54%), vis-à-vis the design stage (68%). This may make
organisations vulnerable to potential reputational harm and could undermine customer satisfaction.
AsustainedfocusonethicscanensurethatAIsolutionsareminimallyaffectedbyhumanbiases,intendedornot,
andbuildorreinforcetrustbetweenthebusinessanditscustomersandemployees.Interestingly,APACandthe
MiddleEastfaredbetterinthisregardthantheU.S.andEurope,asourearlierresearchfound,wherebyroughly
halfoftherespondentshadpoliciesandprocedurestoaddressethicalconcernsintheinitialdesignoftheapp
orafterlaunch.Comparatively,68%ofAPACandMiddleEastbusinessessaidtheyhadethicalpoliciesinplaceat
thedesignstage.However,forpost-launchethicalconcerns,only54%haverelevantpoliciesinplace.
Unfortunately, 74% of businesses say they rely on customer feedback to address ethical concerns post-
launch (see Figure 13). Relying on customer feedback reactively could adversely impact customer
experience, defeating a key AI goal of achieving a leap forward in customer satisfaction.
Cultural change
Despite the heightened optimism around AI, executives reported securing management commitment and
business buy-in as their biggest challenges (see Figure 14 , next page). This resistance is understandable.
After all, AI will help automate many jobs, and perhaps eliminate some. However, as cited above, it will also
create new jobs. Businesses must therefore strive to create a new organisational mindset because without
one they risk losing their ability to compete.
Reactive learning through customer feedback is the top choice
Tracking unethical behaviour after launch
Customer feedback
Which of the following does your company use to help identify and address instances of potential
unethical behaviour, such as bias, in AI applications? (Multiple answers allowed)
Testing by employees
Technology tools provided by external vendors
Custom-built technology tools developed in-house
0% 10% 20% 30% 40% 50% 60% 70% 80%
74%
62%
51%
24%
Source: Cognizant
Figure 13

Cognizant Reports
The low percentage of projects that moved to full implementation is perhaps indicative of the risk aversion
associated with AI’s disruptive impact and lingering concerns over ROI timelines. AI projects, in our
experience, are slow to yield ROI compared with initiatives such as robotic process automation (RPA)
focused on specific tasks, but that shouldn’t deter companies from accelerating AI adoption.8
One way to accelerate the pace of AI adoption is to inculcate a machine algorithm culture that attracts and
retains the best human talent (as noted above), and identifies the right opportunities for delivering value and
driving innovation. The fear of job losses to automation looms large across APAC and the Middle East and
undoubtedly plays a key role in decisions being made in the region.9
But businesses need to reassure their
employees that AI will also create jobs, as reflected in our findings (see Figure 15 , next page).
Getting AI right
The transformative nature of AI makes it a digital upgrade for the entire
organisation. This means upgrading all the key ingredients that make an
organisation tick. Putting in place and building on the key pillars is critical
for AI success, starting with a clear strategy informed by business results,
building the requisite technological capabilities, as well as establishing a
strong talent pipeline and organisation-wide cultural change.
Talent issues and buy-in seen as major challenges
Stumbling blocks in employing AI applications
Respondents who cited “Extremely challenging” and “Very challenging” to the question “To what
extent is each of the following a stumbling block in employing AI applications in your company?”
Source: Cognizant
Figure 14
Retraining employees whose job
responsibilities have been...
46% 48% 50% 52% 54% 56% 58% 60% 62%
61%
Secure senior management commitment 61%
Securing buy-in by businesses 59%
Attracting and retaining
professionals with appropriate...
57%
Securing talent 57%
Access to accurate/timely data 55%
Time required for generating benefits 54%
Securing adequate budget 53%
Measuring impact 51%

Cognizant Reports
Most companies expect AI to increase employee headcount
Increase
Expected impact of AI on the number of employees over the next three years
Decrease
No change
0% 10% 20% 30% 40% 50% 60%
56%
29%
15%
Source: Cognizant
Figure 15
Seven prescriptions for scaling up from experimentation mode
AI’s impact in APAC and the Middle East is limited by the fact that businesses remain in experimentation
mode. As companies plan their future moves, they will need to target larger benefits, such as enterprise-
wide transformation or a market-making impact, which means adopting a more open-minded approach.
This approach, however, needs to be tempered with a clear focus on ethics and an oversight framework
to build responsible AI.10
This will ensure that AI-powered systems operate ethically and create business
advantage (growing revenue and efficiency) while the technology evolves.
1. Formulate a strategy that lets business value guide choice of AI opportunities
❙ Avoid common errors. AI endeavours can be seen as merely technological projects, rather than as
steps in building a business value proposition for the technology. This drastically undermines the impact
that AI could have on the organisation. Similarly, businesses need to avoid the ivory tower mentality,
where AI enjoys a higher priority over the digital transformation of the enterprise, in which everyone is a
stakeholder. This is a sure-shot way to restrict AI’s impact.
❙ Look out for execution blind spots. Ignoring blind spots is an invitation for unwanted outcomes.
The key to avoid them is twofold: First, execute broadly, across the business, while looking for as many
opportunities as possible to give yourself the best chance of success. Second, avoid big bets that can
divert emphasis away from the enterprise’s digitizing efforts.
❙ Develop a robust AI strategy. An AI strategy would be a microcosm of the organisation’s broader
business strategy for digital transformation. This means incorporating key business metrics to ensure
that AI helps the organisation move towards new ways of doing business with an unwavering focus on
business outcomes.
❙ Combine a business impact criterion with technological capabilities. While approaching any AI
use case, organisations need to ask how that project helps their business. An idea might start off as a
1

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sure-shot success right up to the pilot stage but may not deliver the desired results. Similarly, businesses
could find themselves carried away trying to perfect the technology instead of driving transformation. To
this end, our critical recommendation is to start with a business case based on a sound hypothesis. This way,
technological advancements can be aligned with organisational vision to prevent misfires.
❙ Rethink the ways of business first and then spot the resulting opportunities. Wherever necessary,
businesses need to put in place new support structures before exploring AI opportunities. This begins by
reimagining how work gets done now that machines can do more and more of it, and viewing data with a
holistic lens. Importantly, it means building trust for ML.
A case in point is that of a mining company based in Australia where injuries and worker deaths were increasing.
We stepped in to craft an AI-based solution that is capable of predicting risk events, combining the power of
ML, text analytics and natural language processing (NLP). The predictive capability empowered organisations
to prevent human injuries and thus enhanced the brand’s reputation. (See Quick Take on page 20.)
2. Combine business and behavioural insights to build human-centric AI
❙ Embed human-centricity up front. Human-centricity is critical for balancing ambition with machine
resilience. This means an unwavering focus on AI literacy, re-skilling, up-skilling and retooling.
Organisations should ensure a focus is on human-centricity from the initial stages.
❙ Factor in behavioural insights.Today,behaviouralscienceismoreimportantthaneverforbusinesses.AI’s
transformativeimpactonbusinessesandindustriesmeansitneedstobeavaluedpartoftheorganisation.To
thisend,businessescanfuselearningsfromsociologistsandanthropologistswiththeirbusinessstrategyinputs.
3. Build a data-driven organisation
❙ Embed data analytics to drive decision-making. AI is only as good as the data it accesses. For
businesses, it is not only critical to get a holistic view of data, but also to use data analytics to drive
decision-making across the organisation.
AcaseinpointisthatofanAustraliantelecommunicationscompany,whichbuildsandoperatesnetworksand
marketsvoice,mobile,internetaccess,paytelevision,andotherproductsandservices.Withtheproliferation
ofcustomerchannelsandbusinesslines,thecompany’smarketingoperation,tobeeffective,neededto
transformitselfintoafact-basedoperation,withaunifiedviewofcustomersacrosstheirinteractionjourney.
Westeppedinasastrategicpartnerandprovidedadvisoryandfunctionalbusinessanalysisservices,and
designedasolution–thetechnicalarchitecturetobuilda“customerinteractionjourney”platform.Theplatform
capturesanyrelevantcustomer-specificactionbyfilteringeventsofinterest.Eacheventalsogetsenriched
withadditionalusefulinformationbeforebeingexplored.Thishelpedmarketingtomakemoreaccurateand
relevantcustomeroffersandtoreducetime-to-deliveryforcustomerservices.
Growing cybersecurity threats means AI will have a key role in keeping organisations – and their data – safe.
Success will depend on one critical factor: the cybersecurity talent organisations have on hand. (See Quick
Take on page 22.)
❙ Focus on compliance with data privacy laws. Compliance with data privacy laws is critical to the
trust established among businesses, governments and customers. As their AI deployments evolve,
organisations must simultaneously safeguard the privacy rights of everyone concerned.
2
3

Quick Take
Cognizant Reports
Keeping mines safe using
predictive analytics
Mining industry operations are susceptible to events that put some workers at risk. A
rise in incidents involving injuries and loss of workers’ lives was of concern to one of our
mining clients. The company sought our help to enhance safety by unleashing predictive
capabilities to prevent the occurrence of risk events by identifying risks before they harm
workers.
Our team of data and advanced analytics experts conceptualized and delivered an AI
solution that accurately predicts potential accident types that could undermine conditions
and controls in place to ensure miner safety.
We employed an analytical model that leverages advanced analytics and intuitive drill-
downs to analyse various data sources related to the events data and provide insights to
prevent such events/accidents from happening in the future. The system’s dashboard helps
visualize all the findings identified in NLP and regression models and offers insights on top
risks, causes and other external factors that lead to accidents.
In the process of identifying causes, we designed the solution with the capability to learn
and apply learnings from “reactive investigations” to “proactive onsite inspections.”
Beyond preventing reputational damage, the solution is yielding two major benefits:
reduction in the number of safety events (injuries and fatalities), thus creating a safer
workplace for miners, and also significant savings from prevented losses in production
during downtime due to injuries and accidents.

Cognizant Reports
4. Deploy AI tools and algorithms in line with the maturity curve
At a given point in time, any two organisations will have differing problems and challenges to confront.
Achieving AI maturity involves moving along the maturity curve iteratively, adopting the necessary
tools and creating new algorithms. Appropriate options will be defined by where organisations find
themselves on the maturity curve.11
5. Pave the way for creating an AI-centred culture
Cultural change, or the lack of it, reflects whether organisations fully grasp the impact AI is going to
have on them. AI’s impact on organisations will be transformative, and leaders need to recognize the
implications it has for the people who work with them across the value chain, including employees,
customers and partners. The cultural rewiring of an organisation begins at the top and needs to be
approached with this end goal in mind: becoming a data-driven organisation where human creativity
thrives around AI.
❙ Inculcate cross-functional cooperation. To realize AI’s transformative power, businesses need
a cross-functional team and a structured approach to identifying opportunities for process
improvements.
❙ Relinquish risk aversion. As businesses move beyond experimentation, they will need to create
an environment that encourages creativity and a risk-taking attitude. To make this happen, we
recommend setting up an AI office or centre of excellence (CoE) to oversee AI projects from
ideation to production while making sure ethical guidelines are followed.
Small, multi-skilled teams are critical. AI success depends on combining knowledge from
business functions, processes, data and technology. It takes an organisational village.
❙ Close the learning loop. BridgingthegapbetweenlearningfromAIexperiments,whichhappens
simultaneouslyacrosstheorganisation,andtheareaswheretheoutput/lessonscanbeappliedis
importantforadvancingcapabilities.Anorganisationalmechanismthatcanoverseetheexperiments
andupdatethebroadapproachaccordinglycangoalongwayinupgradingcapabilities.
4
5

Quick Take
Cognizant Reports
Cybersecurity:
A multi-pronged challenge
Cybsersecurity and AI have a slightly complicated relationship. From the enterprise
perspective, AI is a boon for early threat detection and response. Additionally, ML and deep
learning techniques make sure that the enterprise security apparatus learns and adapts
after every incident. AI is, in fact, critical to the future of cybersecurity. Cybercriminals are
getting more and more sophisticated, using malware and, more recently, deepfakes.12
Tackling these threats is critical to protect assets and reputations. However, the security of
human data, physical assets and reputations cannot be left to machines. Humans are the
attackers, targets and responders in the cybersecurity cat-and-mouse game, making it
critical that the AI security apparatus be fused with human insight. But this is where things
get tricky.
Even as AI-related cybersecurity threats are expected to increase manifold in the coming
years, there is conversely a deep shortage of cybersecurity talent.13
According to estimates
by (ISC)2
, the global cybersecurity talent shortfall is at four million, with APAC falling short
by a whopping 2.6 million.14
This presents a massive challenge to the region’s ambitious plans for AI. The only clear path
is to retrain existing employees to upgrade their skills, while attracting fresh data science
talent from relevant fields. This needs to be supported with a work environment that
effectively integrates the new talent and enables fast realisation of value in AI projects.

Cognizant Reports
❙ Keep on communicating. A strong culture thrives on communication. AI deployments
might succeed or fail, but communication keeps people going. Nevertheless, it is critical for
any corporate AI effort to keep all channels open for sharing knowledge and information and
reinforcing business value, trust in technology and data.
6. Embrace ethics and governance
❙ Embed ethics up front to build responsible AI applications. As AI is expected to be all-
pervasive, touching every aspect of an organisation’s business, building ethics into the fabric
of AI technology is essential. Companies must build ethics right from the initial development
stages and subsequently oversee that AI apps operate ethically and learn to evolve as well.
Governance models are critical for ensuring that ethical aspects of AI don’t get overlooked.
AI’s current limitations in the form of built-in biases15
and an inability to handle complex
situations16
are well documented.
As businesses advance their AI efforts, ethics and governance become more and more critical
to their deployments. By adopting an existing governance framework, they can ensure that
they are on track to become a responsible, AI-led business. (See Quick Take, next page.)
7. Employ external partnerships as AI levers
❙ Address talent gaps. Choose external vendors that are competent to augment the business’s
AI capabilities – vendors that have a workforce with AI-ready skills to engage an effective
partnership.
❙ Leverage partnerships to access ever-advancing AI technologies. How can organisations
better prepare for rapid AI experimentation? Securing ready access to new technologies and
techniques is an important first step. Many AI efforts get bogged down in lengthy technology
procurement processes. Businesses need to be sure they have an open cloud environment to
experiment with machine data. Better yet, they should create a robust set of partnerships that
provide access to continuously advancing AI technologies.
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Quick Take
Ethics and governance
initiatives
The rise of AI and the accompanying fears have prompted governments and other
institutions to launch efforts to allay these concerns. In APAC, the Middle East and across
the globe, several initiatives are helping businesses, both established and new-fangled,
to properly embed ethical values and embrace human-centredness into their AI thinking
and action, as well as to properly govern new initiatives as they are launched. Here are two
cases in point.
Singapore launched its AI model framework last year, becoming the first country in
the region to have such a model.17
The model is based on the principle that any AI
implementation should be human-centric, explainable, transparent and fair. This approach
is aimed at promoting trust and understanding in technology. The framework has since
been adopted by the likes of DBS Bank, Hong Kong & Shanghai Banking Corporation
(HSBC) and MasterCard.18
In its second edition, published in January, the model provides a sophisticated set of
guidelines around areas such as internal governance measures, determination of human
involvement and stakeholder interaction.19
In addition, the model also provides case
studies and a self-assessment guide to help organisations manage their progress.
Meanwhile, the Ethics and Governance of Artificial Intelligence Initiative,20
a joint project
by MIT Media Lab and the Harvard Berkman-Klein Center for Internet and Society, is
focused on helping businesses understand how AI can reflect values of fairness, human
autonomy and justice.
For businesses, these initiatives present an opportunity to adopt AI governance and ethics
framework(s) that best suit their vision.

Respondent Profile
Cognizant Reports
Which one of the following best describes your title or level?
C-suite, (CEO, CFO, CIO, CTO, CMO and COO)
Department head
Director
EVP/SVP
Vice President
0% 10% 20% 30% 40% 50%
47%
17%
21%
11%
3%
What is the growth of your company’s
revenues compared to the average company
in the same industry in the last two years?
0% 10% 20% 30% 40% 50%
Somewhat above average
About average
Somewhat below average
Much above average
Much below average
What are your company’s global
annual revenues?
US $100 million to
US $500 million
US $500 million to less
than US $1 billion
US $1 billion or more
0% 10% 20% 30% 40% 50%
42%
41%
10%
US $50 million to US
$100 million
7%
Countries Category
Australia
Singapore
China
India
UAE
ANZ
ASEAN
China
& HK
India
Japan
Middle East
25%
28%
10%
14%
15%
10%
New Zealand
Malaysia
Hong Kong
Japan
Saudi Arabia
Oman
Kuwait
Philippines
Taiwan
Thailand
Methodology
We surveyed 590 executives in the APAC and Middle East region using a combination of online and
telephone interviews. These executives are spread across 15 countries, and split into six regions and
eight industries. Nearly 50% belong to the C-suite.

Cognizant Reports
Information technology
General management
Technology
Digital transformation
Data and analytics
Digital
Operations/Manufacturing
Research & devlopment
Sales & marketing
Data sciences
Digital experience
Customer platform
Innovation
Product & Platform Strategy
Strategy
Other
Procurement/Supply Chain
Risk Management/Compliance
In which functional area do you primarily work?
0%
0% 0%
5%
5% 5%
10%
10% 10%
19%
13%
13%
7%
6%
6%
5%
5%
5%
4%
4%
3%
3%
3%
3%
1%
1%
1%
15%
15% 15%
20%
20% 20% 25%
Australia Australia
Japan Japan
19% 22%
15% 20%
India Singapore
Singapore India
New Zealand Hong Kong
Saudi Arabia Malaysia
Hong Kong New Zealand
China China
Malayasia Philippines
Philippines Thailand
Thailand Taiwan
Oman Kuwait
UAE Saudi Arabia
Taiwan Oman
Kuwait UAE
14% 17%
14% 16%
6% 13%
5% 12%
5% 12%
5% 12%
5% 8%
3% 6%
2% 4%
2% 4%
2% 4%
2% 3%
1% 3%
Which country do you work in? Which country do have responsibilities for?

Cognizant Reports
Endnotes
1 IDC: Asia-Pacific spending on AI systems will reach $5.5 billion this year, up 80% from 2018, TechCrunch, May 21, 2019,
https://techcrunch.com/2019/05/20/idc-asia-pacific-spending-on-ai-systems-will-reach-5-5-billion-this-year-up-
80-percent-from-2018/.
2 AI for health, infrastructure and natural resources key to Australia’s future prosperity, CSIRO, Nov. 18, 2019, https://www.
csiro.au/en/News/News-releases/2019/AI-for-health-infrastructure-and-natural-resources-key-to-Australias-
future-prosperity.
3 Alibaba unveils its first A.I. chip as China pushes for its own semiconductor technology, CNBC, Sept. 25, 2019, https://www.
cnbc.com/2019/09/25/alibaba-unveils-its-first-ai-chip-called-the-hanguang-800.html.
4 We asked respondents to select their company’s revenue growth category (based on the last two years) from the following
options: much above average, somewhat above average, about average, somewhat below average and much below
average.
5 Making AI Responsible – And Effective, Oct. 2018, https://www.cognizant.com/whitepapers/making-ai-responsible-
and-effective-codex3974.pdf .
6 A.I. in China: TikTok is just the beginning, Fortune, Jan. 2020, https://fortune.com/longform/tiktok-app-artificial-
intelligence-addictive-bytedance-china/.
7 21 More Jobs of the Future: A Guide to Getting and Staying Employed through 2029, Oct. 2018, https://www.
cognizant.com/futureofwork/whitepaper/21-more-jobs-of-the-future-a-guide-to-getting-and-staying-employed-
through-2029.
8 Property claims handling revisited: The insurance AI imperative, Cognizant Roundtable, Sept. 2019.
9 Job disruption in AI era likely to catch Asian governments by surprise, says MIT report, SCMP, May 2019, https://www.scmp.
com/tech/policy/article/3009168/job-disruption-ai-era-likely-catch-asian-governments-surprise-says-mit.
10 Making AI Responsible – And Effective, Oct. 2018, https://www.cognizant.com/whitepapers/making-ai-responsible-
and-effective-codex3974.pdf.
11 Insights for Getting – and Staying – Ahead in the Digital Economy, Cognizant, Nov. 2019, https://www.cognizant.com/
whitepapers/11-insights-for-getting-and-staying-ahead-in-the-digital-economy-codex5163.pdf.
12 Untangling the Deepfake Menace, Cognizant, Feb. 2020, https://www.cognizant.com/perspectives/untangling-the-
deepfake-menace.
13 Cybercrime: AI’s Growing Threat, DarkReading.com, April 2019, https://www.darkreading.com/risk/cybercrime-ais-
growing-threat-/a/d-id/1335924.
14 Cybersecurity Skills Shortage Tops Four Million, Infosecurity Magazine, Nov. 2019, https://www.infosecurity-magazine.
com/news/cybersecurity-skills-shortage-tops/.
15 Machine Bias, ProPublica, May 2016, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-
sentencing.
16 How Design Thinking Can Power Creative Problem-Solving, Drive Change and Deliver Value, Cognizant Technology
Solutions, Dec. 8, 2015, www.cognizant.com/perspectives/human-centric-design-how-design-thinking-can-power-
creative-problem-solving-drive-change-and-deliver-value.
17 Model Artificial Intelligence Governance Framework and Assessment Guide, World Economic Forum, Jan. 2019, https://
www.weforum.org/projects/model-ai-governance-framework.
18 Singapore updates model AI governance framework, Computer Weekly, Jan. 2020, https://www.computerweekly.com/
news/252477129/Singapore-updates-model-AI-governance-framework.
19 Model AI Governance Framework , PDPC Singapore, Jan. 2020 https://www.pdpc.gov.sg/Resources/Model-AI-Gov.
20 The Ethics and Governance of Artificial Intelligence Initiative, https://aiethicsinitiative.org/.

Acknowledgments
The authors would like to thank Newton Smith, Digital Business leader for Asia Pacific, for his valuable inputs.
Cognizant Reports
About the authors
Jeff Olson
Associate Vice President - Projects, Artificial Intelligence & Analytics Practice,
Cognizant Digital Business
Jeff Olson leads the Applied AI and Analytics Practice for Cognizant Australia. In this role, he
creates business value for organizations using our human-centered, agile approach to deliver
superior customer experiences, more intelligent products and smarter business operations in
the shortest amount of time. Prior to joining Cognizant, Jeff was head of big data and analytics
for Oracle, head of business intelligence at IAG and Executive Director, IT industry, at Ernst &
Young. Jeff has a BA in Management from St. John’s University in New York. He can be reached
at Jeff.Olson@cognizant.com | https://www.linkedin.com/in/jeffrey-c-olson/.
Guruprasad Raghavendran
Head, AI and Analytics, APAC Markets, Cognizant
Guruprasad Raghavendran is Head for AI and Analytics, APAC Markets, at Cognizant. He is
an industry thought leader who architects and delivers data and AI solutions that drive digital
transformation across geographies and industries. Guru combines a passion for innovation with
pragmatic data experience to turn AI’s promise into reality. He has work experience across North
America, Europe and APAC, and is a trusted advisor for many customers. Guru speaks regularly
at various industry, technology and partner forums. He can be reached at Guru@cognizant.com |
https://www.linkedin.com/in/guruprasad-raghavendran-401b5620/.

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 innovative and efficient business-
es. Headquartered in the U.S., Cognizant is ranked 195 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.
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mentioned herein are the property of their respective owners.
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