Humans + Intelligent Machines - cognizant.com · The transition to intelligent machines won’t happen without an acute focus on the relationship between humans and machines, how

Humans + Intelligent Machines Mastering the Future of Work Economy in Asia Pacific

Asia Pacific businesses are bullish on investing in artificial intelligence technologies to prepare themselves for the future of work and theyrsquore optimistic about the returns according to our recent study Theyrsquove got lots of work to do however when it comes to preparing the way for optimal collaboration between humans and intelligent machines

By Manish Bahl

The transition to intelligent machines wonrsquot happen

without an acute focus on the relationship between

humans and machines how the two will collaborate

and how the current workforce and the business

itself will adapt to AI

March 2019

The world of work is changing faster than ever and machine intelligence is at the center of this change For many reasons businesses in the Asia Pacific are particularly well positioned to benefit from the influx of AI into their operations processes workflows and business models Aggressive government policies pertaining to artificial intelligence a freedom from legacy assets and an abundance of data have put the region in a bright spot for the adoption of intelligent machines According to UBS AI will produce economic value in Asia-Pacific of between US$18 trillion to US$3 trillion a year by 20301

Businesses in the region are certain their investments in intelligent machines ndash which will reach 135 of revenues in five years according to our recent research ndash will be well worth it Business leaders in our study expect intelligent machines to account for 12 revenue growth and 74 lower costs in the next five years in addition to a double-digit boost to workforce productivity

At the same time however the transition to the intelligent machine age wonrsquot happen without an acute focus on the relationship between humans and machines how the two will collaborate and how the current workforce and the business itself will adapt to AI Itrsquos here that Asia Pac businesses have their work cut out for them Just over one-third of the organizations we surveyed feel fully prepared to handle future work with intelligent machines and fewer than half are confident about their ability to integrate machines with existing business processes Companies in the region need greater clarity on how work will shift what the new and valuable work will be and how they will be supported when transitioning to the human-intelligent machine workforce

To learn more about how organizations in the Asia Pacific are preparing for the future of work with intelligent machines Cognizantrsquos Center for the Future of Work surveyed 622 top business and IT executives at leading companies across the Asia Pacific region (see Appendix page 26 for more details on the survey methodology) Our objective was to gain insight into the changes business leaders are making to take advantage of AI-driven technologies that augment human skills the implications of intelligent machines for their companies and how they can prepare to embrace these advancements to improve productivity profitability and engagement

Humans + Intelligent Machines Mastering the Future of Work Economy in Asia Pacific 3

Executive Summary

4 Humans + Intelligent Machines Mastering the Future of Work Economy in Asia Pacific

Key findingsOur research reveals the following important trends whose impact in some cases will soon ripple around the world

bull The age of human + intelligent machine has truly arrived Eighty-two percent of respondents cited intelligent machines as the number one influence on the future of work in the next five years Companies claimed that 70 of their workforce will be prepared to work with intelligent machines during the same period

bull The economics of intelligent machines is an unstoppable force Intelligent machines are expected to propel revenue growth while pulling costs down Leaders are bullish on unlocking new performance thresholds with expectations for a 20 increase in workforce productivity in the next five years

bull The message is loud and clear Invest or be left behind On average companies plan to spend 135 of their revenue on building and managing intelligent machines in the next five years The more you invest the more you make

bull Preparation for change is crucial Only 35 of the organizations we surveyed feel fully prepared to handle working with intelligent machines and only 42 are confident about their ability to integrate AI with existing business processes The top three challenges are misalignment of workforce strat-egy with business goals (72) lack of IT infrastructure readiness (71) and a shortage of required talent and knowledge (70)

Based on our findings wersquove developed a framework to help traditional businesses systematically move toward the new world of work with intelligent machines To get there organizations must align five elements (5Ts) ndash tasks teams talent technology and trust ndash to successfully transition into the new machine age

ASIA RISING WITH THE RISE OF INTELLIGENT MACHINES



Human work is undergoing a profound transformation driven by the accelerating impact of AI algo-rithms and automation entering into our daily work life (see Quick Take next page for our definition of intelligent machines) Asia Pacific which includes over half of the worldrsquos population and produces over one-third of the worldrsquos economic output is quickly becoming a hotbed of AI adoption and innovation Several major Chinese players such as Baidu Alibaba and Tencent have jumped into the race for AI supremacy and many Asian start-ups in India Singapore and Japan are also making significant advances2 Alibaba plans to invest US$15 billion in research over three years to build seven labs in four countries focused on quantum computing and AI3

Every company in the region it seems is interested in becoming an AI-first company In fact 95 of our respondents cited AI as the number-one driver of business change in the next five years Case in point is Ant Insurance the insurance arm of Alibaba-backed Ant Financial which is leveraging advanced machine-learning algorithms natural language processing and computer vision to process an insur-ance claim in one second vs the former 49 hours it used to require (thatrsquos 176000 times more efficient)4 As Kevin Kelly co-founder of Wired has said ldquoOver time AI will not seem any more unusual than elec-tricity cars airplanes the Internet and other major transformative technologies Everything that we for-merly electrified we will now cognitizerdquo5

While AI is the heart of an intelligent machine the data and hardware infrastructure make the heart pump In this regard Asia Pac is pulling ahead in several ways

bull Asia is set to become the worldrsquos largest 5G mobile region by 20256 The arrival of 5G (100 times faster than 4G) will speed up processing times for image gesture face or speech recognition and enable interactive connection between devices providing the foundation for AI applications

bull Therersquos no dearth of data in Asia Pacific The region accounts for around 45 of global mobile traffic and will account for over 53 of the global Internet population by 20207 8 The Economist recently referred to China as the ldquoSaudi Arabia of Datardquo referring to Chinarsquos 730 million Internet users the largest for any country in the world and equivalent to almost the entire population of Europe9 The more and better data you have the better the AI systems learn and generate insights helping businesses lower costs improve productivity and offer more targeted products and services to consumers

Another key factor is the activist approach of governments in the region Itrsquos no secret that the Chinese government ndash and Chinese tech companies ndash continue to invest heavily in building AI-related capabili-ties to propel China to global AI leadership In 2017 the Chinese government issued an ambitious policy blueprint in an attempt to become the world AI leader by 2030 by which time the countryrsquos AI industry could be worth US$150 billion10 As a result it has made AI-driven education a cornerstone of its national strategy as policy makers increasingly look to AI to reform Chinarsquos education and public research as well as modernize classrooms

Whatrsquos more China overtook the US in AI start-up funding in 2017 accounting for 48 of the worldrsquos total AI start-up funding compared with 38 for the US11 Local companies such as GTCOM are moving westward and striking into the home territories of many companies based in the first world12 Chinarsquos dream to become a global AI leader is well on its way to becoming a reality

While China is driving Asiarsquos AI age other countries in the region are also moving ahead the Singapore government plans to double-down on its efforts to build AI capabilities and equip its workforce with tools to participate meaningfully in an AI-driven future From China to Japan Singapore to India Asian governments are developing national-level plans for how AI can be used to enhance domestic and regional competitiveness13


Quick Take

Defining the intelligent machineFor this report wersquove defined an intelligent machine as one that combines

bull Software that mimics human actions andor learns perceives and reacts with a form of reasoning and problem-solving based on AI and predictive analytics

bull Hardware (physical robots servers sensors mobile devices connectivity)

bull Data (contextualized and real-time) complemented by human inputs (often judgment)

In short when intelligent software is combined with massive processing power and enormous amounts of data intelligent machines come to life (For more on this topic see the ldquoSystems of Intelligencerdquo section of our book ldquoWhat to Do When Machines Do Everythingrdquo14)



THE ECONOMICS OF HUMANS + INTELLIGENT MACHINES


Itrsquos no wonder then that a large majority of respondents said intelligent machines and human- machine collaboration would be the top two drivers of change for how they work in the next five years impacting revenue growth worker productivity and business agility (see Figure 1) in fact nearly seven in 10 respondents (68) believe they will lose ground to competitors if they donrsquot embrace intelligent machines

Success with AI adoption will be based on how well companies blend and extend the strengths of humans (cognition judgment empathy versatility etc) with the capabilities of machines (accuracy endurance computation speed etc) to create a joint team for common business goals Respondents expect 70 of their workforce to be prepared to work with intelligent machines in the next five years

By turning machines and workers into collaborative ldquocolleaguesrdquo companies can reach entirely new performance thresholds In fact 72 of leaders surveyed said that humans and intelligent machines will work as an integrated team in their organization and almost 70 of executives believe that the key goal of using machines is to augment human intelligence and expand human capacity in their organization Among all industries we found banking and financial services companies are most bullish (87) on human-machine collaboration compared with the regional average (72)

In one such example DBS Bank in Singapore is leveraging AI to enhance the role of recruiters to support the fast growth of its wealth management business Its virtual bank recruiter will review resumes collect applicant responses for pre-screening questions and conduct psychometric profiling assessments on candidates By automating the pre-screening process the bank will save 40 manhours a month per recruiter enabling recruiters to spend more time sourcing candidates interviewing applicants focusing on talent advisory or even upskilling themselves15

Intelligent machines delivering new performance thresholds

62

64

65

68

68

70

71

73

82

Improve employee engagement

Improve planning anddecision making

Enable real-time decision making

Generate cost savingsand eciencies

Accelerate getting new product and services into the market

Expand ability to acquire engageand retain customers

Increase work flexibility(eg flexible working hours)

Raise employee productivity

Boost revenue growth

Q In which of the following ways will intelligent machines create value for your company in the next five years

76

82

Human-machinecollaboration

Intelligent machines

Top two forces that will have a significant impact on work in the next five years

Leaders expect a high percentage of the workforce to be prepared to work with intelligent machines

in the next five years

(Percent of respondents)(Multiple responses allowed)

Source Cognizant Center for the Future of WorkBase 622 senior executivesFigure 1


Our respondents are unlocking huge amounts of value from intelligent machines such as

bull Taking the risk to reinvent business and create new opportunities Sixty-three percent of respondents said that intelligent machines will fundamentally change business models (reconfiguring delivery models rethinking value prepositions etc) and operating models over the next five years Alibaba for example envisions the future of retail as having no distinction between online and offline shopping with the customer experience enhanced by augmented reality AI and facial recognition To facilitate its vision Alibaba is pioneering its ldquoNew Retailrdquo model and striving to go beyond e-commerce building an ecosystem of digital services that can be tapped into by consumers sellers service providers and content providers16

bull Identifying areas for AI-driven revenue growth Companies that make intelligent machines a partner-in-work will generate significant opportunities A large majority (82) of respondents cited a boost to revenue growth as their number one reason for adopting intelligent machines predicting 12 revenue growth in the next five years due to intelligent machines (see Figure 2 next page) Certain industries ndash including retail banking and financial services hospitality and insurance ndash are expecting the highest gains From conversational AI-driven chatbots to machine learning-enabled fraud detection for example Japanrsquos financial industry is adopting a variety of new AI-based technologies to improve the client experience and focus workers on higher level tasks17 In another interesting example Hong Leong Bank of Malaysia is analyzing the emotion of customers by the way they speak on the telephone to better equip its associates to handle customer queries18

bull Seeing the back office as a machine sweet spot Robotic process automation has the potential to benefit businesses through significant cost savings error reduction and the ability to instanta-neously document and optimize processes Our respondents estimate a 74 cost decrease through the use of intelligent machines over the next five years ANZ Bank is leveraging AI for back-office automation to reduce time-to-market for the approval of unsecured and personal loans According to the bank 1000 hours of back-office activity have been eliminated due to increased automation19

bull Making processes truly intelligent to super-charge profitability As the adoption of intelligent machine intensifies companies must invest heavily in rethinking their processes to proactively address radically different market conditions Although the impact of intelligent machines will be felt across business processes it will be particularly game-changing for IT new product and service development and customer service (see Figure 3 next page) For instance automated IT processes that predict slowdowns or errors and suggest how to avoid or fix them will transform IT processes by making them more responsive and cost-effective Japanrsquos largest utility provider Tokyo Electric Power Co (TEPCO) is looking at AI for predictive maintenance of its infra-structure to preempt failures and avoid downtime for customers20

A large majority of respondents cited a boost to revenue

growth as their number one reason for adopting

intelligent machines predicting 12 revenue growth in

the next five years due to intelligent machines


Intelligent machines are not optional

Q What impact will intelligent machines have on the following core processes in the next five years 75 71 71 70 68 67 65 65 61 58 57

45 46

Inform

ation services and te

chnology

New product a

nd service development

Custom

er service

RampD and innovatio

n

Supply chain and partner m

anagement

Manufactu

ring

Salesmarketin

g

HR and people managem

ent

(Percent of respondents saying ldquohighrdquo or ldquovery highrdquo impact)

Productio

n and operations

Strate

gic planning and implem

entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance

Purchasing and procure

ment


The economics of the new machine

74 average decrease in costs

120 average increase in revenue

Q Please estimate the impact of intelligent machines on your companyrsquos revenues and costs over the next five years

105 97

138

121 132 128

116

65 58

92 81

75 80 70

Manufacturing Healthcare Retail Insurance Banking andFinancialServices

TravelHospitality

Life Sciences

Percent revenue increase Percent reduction in costs



Follow the moneyAsia Pacific leaders are not afraid to heavily invest in intelligent machine initiatives Respondents said their organizations will apply an average of 135 of their total revenue on building capabilities for machines in the next five years (see Figure 4) Such investments are necessary for the many initiatives needed to restructure the business and operating models win new markets innovate and improve effi-ciencies manage maintenance costs during the transition initiate internal change set out a man-ma-chine roadmap provide interdisciplinary company training hire third-party consultants and possibly acquire companies while attracting digital-savvy talent

Go big go machine or go home

Q Please estimate the percent of revenue you expect your company to spend on building intelligent machines capabilities (technologies processes talent etc) in the next five years

126 116

156

138 148

136 124


TravelHospitality

Life Sciences

Percentage of revenue

Average of 135 of revenue on building capabilities for machines



PREPARING FOR THE TRANSITION


Therersquos a lot of playing field however between the reality of today and what lies ahead We found that only 35 of the organizations in our study feel they are fully prepared to handle future work with machines (see Figure 5) and only 42 are confident about their ability to integrate intelligent machines with existing business processes As with any major business change or technology cycle discretion is the better part of valor Therersquos a lot of groundwork and preparation involved in terms of skills culture processes and other areas and businesses will need to take an iterative experimental approach to the transition and be prepared to fail fast and adapt quickly

Job displacement and the redefinition of jobs and tasks is a key concern with AI and few respondents said they have a clear understanding of how to handle this disruption The misalignment of workforce strategy with business goals was cited as a top challenge by 72 of respondents Intelligent machines are likely to replace repetitive human tasks in the future challenging workers to focus on new skills and adjust to rapid changes in core skillsets We found leaders in insurance (75) and manufacturing (73) are more concerned about the misalignment of workforce challenges compared with other industries

Our data also revealed that a lack of IT infrastructure readiness is another key concern In fact 56 of orga-nizations agreed they face issues delivering consistent employee experiences due to a lack of integration between various technologies and platforms For decades IT has followed the theory of ldquoWhy fix whatrsquos not brokenrdquo This approach has led to the massive accumulation of outdated legacy infrastructure which is not only expensive to upgrade but also slow to meet the demands of intelligent machines Moreover turning data into actionable insights can only happen if IT infrastructure is agile flexible and quickly scalable as AI systems need to be continuously fed with an uninterrupted supply of quality data Our advice is donrsquot jump the AI gun unless you have addressed your IT infrastructure woes

It is certain that the transition to the new machine age will not be easy In fact the process can be over-whelming for many leaders For the companies working to get ahead itrsquos important to remember that there isnrsquot one element of business model transformation but five In the coming sections we examine and explain how each of these elements is necessary to transition into the new machine age as well as what they mean for you and your organization

Challenging the future of work

58

61

62

64

65

67

68

70

71

72

Peoplersquos fear of job loss

Technological integration issues withexisting systemsplatforms

Gap between IT and business operations

Poor integration of business processes

Lack of trust in intelligent machines

Lack of overall business strategy for intelligent machines

Lack of training to enable employeesto work with machines

Shortage of required talent and knowledge

Inadequate technical capability and infrastructure

Misalignment of workforce strategy with business goals

Q What are the significant challenges in leveraging intelligent machines to meet your future business objectives (Top 10 challenges shown below)

Just over one-third of organizations are preparedto handle the future of workbased on intelligent machines

(Multiple responses allowed)

(Percent of respondents naming each as a top challenge)



ASSEMBLING THE NEW MACHINE FIVE TrsquoS


Wersquove outlined five distinct elements for success with intelligent machines teams tasks talents tech-nology and trust The key to successful implementation is ensuring all five elements are integrated and aligned to create ultimate value Companies need to act now rather than wait for more certain times or for more clarity over exactly what the intelligent machine is and what it will become

eams small flexible and hybridWhile itrsquos clear that companies value the use of human-machine teams many are still not clear about the ideal strategy for the hybrid team (see Figure 6) While 50 of respondents said they plan to integrate a human + intelligent machine team into their existing workforce strategy and planning 45 said theyrsquoll develop a new workforce strategy in parallel with their

existing strategy In either case human-machine teaming will change the way organizations manage their workforce workflows workspace and culture

bull Embrace small as the new large Work demand will change in team structures A move from larger hierarchical team structures to smaller teams was considered by 72 of our respondents This type of change will require the restructuring of individual and team performance metrics and incentives as mentioned by 68 of respondents These changes will allow individuals and teams to become more fluid and flexible across roles and functions Smaller teams donrsquot always mean a reduction in employee headcount but rather leveraging people strategically by either enhancing their role or assigning new roles for business value creation

bull Forget the time zone difference The issue of time zone differences will disappear as AI is able to work any time of the day month and year regardless of location Consequently integrated teams will help cultivate a corporate culture that trusts deeply in data because AI systems are highly effective in sourcing processing and analyzing information and providing the best possible recommendations to their human colleagues in real-time As noted by 50 of respondents performance management practices will need to be changed to focus more on work output and value-added activities and less on the number of hours worked Intelligent machines will not only elevate workforce productivity but also improve work flexibility (71) and employee engagement (69) according to respondents

Man + machine the new dream team

5

45

50

We donrsquot see a need for human-machinecollaboration in our organization

In the process of developing a new strategyplan to develop a new strategy that will run

in parallel with our current workforce strategy

In the process of integrating itplan to integrate it with our

workforce strategy and planning

Q What is the current state of human-machine collaboration in your organization

We will need smaller teams instead of larger ones in the future

We will have to restructure individual and team performance metrics and incentives with intelligent machines

Managers will maintain their role however their employees may be machines rather than human every time in the future



bull Establish new roles With more machines fulfilling worker tasks businesses will require new roles such as man-machine teaming managers to identify tasks processes systems and experiences to be upgraded by newly available technologies as well as imagine new approaches skills interactions and constructs (For more on this topic read our recent report ldquo21 Jobs of the Futurerdquo)

asks learning to assign and shareBusinesses will need to deconstruct jobs and identify which tasks are best performed by humans vs intelligent machines to achieve an optimal balance of human-machine collabora-tion The majority of study respondents plan to create a task allocation framework in the next 12 to 24 months to define roles and responsibilities and set the rules for AI systems and work-

ers to coordinate to accomplish a task (see Figure 7) The creation of task allocation systems requires companies to break down work into discrete parts ndash projects tasks campaigns initiatives ndash each with its own team culture approach and goals as stated by 62 of respondents In order to embed task planning systems in a seamless manner almost half of companies plan to redesign their processes

One of the most prominent findings from our research is that no one task will be 100 driven by a machine or a human by themselves instead every task will have some degree of shared involvement As cited by 58 of respondents the overlapping capabilities of humans and intelligent machines across various tasks will help companies assign tasks between them As a result of this shared involvement AI systems can learn to better proceed with new and unknown scenarios while humans can continue to adapt and focus on higher-value tasks

It is certain that machines will liberate human workers from repetitive and physical work providing greater safety and reliability in many lines of business as they can foresee human errors through data collected by embedded sensors and avoiding hazardous situations As a result humans can perform higher function tasks that demand creativity empathy emotional intelligence judgment calls and human interaction When human-led tasks and skills are complemented with machine intelligence there is a multiplier impact on workforce productivity and business value creation

The new task allocation system to advance human-machine collaboration

We plan to break down work into discrete parts ndash projects tasks campaigns or initiatives - each with its own team culture approach and goals

We need to redesign our processes to embed task-planning system in a seamless manner

10

18

22

50

Dont know

In the process of creating it

Plan to create it in thenext 24-36 months


Q What is the current state of developing a work task allocation framework between humans and intelligent machines in your organization



The human-machine team will work in a variety of ways whether itrsquos about decision-making new prod-uct design and development handling work with a high degree of complexity andor changeability pro-viding expert advice or handling customer service For example hospitals in the National University Health System in Singapore will soon use facial recognition and AI-driven predictive analytics to speed the patient registration process and advise doctors on patients with a high probability of being readmit-ted Healthcare workers will be able to make better decisions and be freed up for more direct patient interaction21

Cognitive computing-based customer service is another human-machine collaboration opportunity that will soon become a make-or-break factor for succeeding in a fast-paced competitive business environment By processing in real-time the content of phone calls made to a call center as well as the callerrsquos underlying emotions cognitive systems can guide agents to de-escalate tense situations result-ing in higher customer retention lower agent turnover and the insights to create a better customer experience22

alent the fusion of human and technical skillsThe skills landscape has never been as complex and essential as it is today In a world of more pervasive technology specifically human skills will gain importance (Figure 8 next page) Throughout the world companies are beginning to place a premium on job applicants who demonstrate human skills such as problem-solving adaptability time management organi-

zation and communication and this trend will only strengthen as workers are freed from rote tasks 23

Doubling down on human skills will not be sufficient though Human skills will need to be tweaked for optimal human-machine collaboration For instance focusing on communications skills will not ensure effective communication with machines Workers will need to think in terms of the systems tools and pro-cesses required to make the best use of AI-driven insights and capabilities Technical skills will continue to be in demand as noted by 81 of our respondents (For more on the skills that willl gain significance in the future see our blog post ldquoThe Future of Skills Turning Humans and Machines into Collaborative Col-leaguesrdquo)

Talent scarcity will continue to be one of the biggest leadership challenges in implementing and evolv-ing intelligent machines Almost 70 of respondents said they struggle to find candidates with relevant skills In the past employers regarded contract workers as a cost play and a purely transactional relation-ship however as companies struggle to find full-time employees with relevant technical skills many now recognize the value that these types of workers bring Temp workers offer not only specific skills and flexibility but also fresh perspectives due to their cross-industry knowledge Companies can lever-age these qualities to create new business models products and services for future revenue streams

Talent scarcity will continue to be one of the biggest

leadership challenges in implementing and evolving

intelligent machines Almost 70 of respondents said

they struggle to find candidates with relevant skills


In fact 58 of leaders admitted that temps contractors and other flexible workers will become an inte-gral part of their workforce strategy which makes it important for them to begin viewing temp workers from an inclusion perspective Itrsquos no surprise that WeWork the coworking space has over 300 shared office locations dotted around the globe Accessing teaming and co-creating with a global talent pool will become the new norm for talent management in the future24

Additionally diversity must become core to companiesrsquo talent acquisition strategy Rather than blaming the lack of a talent pipeline leaders need to start creating one For instance they can encourage moth-ers who have temporarily left the workforce to re-enter it and welcome multi-aged hybrid teams With-out more diversity and inclusive representation in the workforce it will be difficult for companies to meet the demands of the digital economy (For more on this topic see our white paper ldquoMaking Room Reflections on Diversity amp Inclusion in the Future of Workrdquo)

Some countries are flipping the talent issue on its head Chinarsquos Ministry of Education has launched a five-year AI talent training program under which at least 500 teachers and 5000 students will be trained at top universities The ministry released a comprehensive ldquoAI Innovation Action Plan for Colleges and Universi-tiesrdquo to cultivate talent The plan calls for new AI teaching and research facilities as well as new courses both offline and online and the creation of 100 ldquoAI+Xrdquo cross-disciplinary studies25

Preparing the current and future workforce with relevant skills requires a reboot of traditional training and learning models and approaches Decades-old training models were fit for the industrial economy

Skills with high future relevance

Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership

Somewhat or very difficult fororganizations to find people forrequired skills

Temps contractors or other flexibleworkers will become an integral partof our workforce strategy

Q As machines change the nature of work certain skills are likely to be more or less important What will be the degree of importance for each of the listed skills in the next five years

50

60

65

71

75

78

78

80

81

81

82

84

85

87

(Multiple responses allowed) (Percent of respondents saying each skill is

ldquoimportantrdquo or ldquovery importantrdquo)



but not for the digital economy To establish new learning models businesses need to engage in more flexible partnerships accelerate their responses time provide different modes of delivery and offer new combined-skill programs to reliably prepare people for what comes next (Read more on this topic in our white paper ldquoRelearning How We Learn from the Campus to the Workplacerdquo)

echnology IT matters more than everWhether your organization is recreating a business process from scratch or injecting AI into front- middle- or back-office processes success will depend on how well the IT infrastructure is integrated with AI systems IT infrastructure needs to become agile responsive flexible secure scalable and simple to manage the transition The intelligent machine will change ITrsquos role and

priorities in three distinct waysbull From business-serving to business-changing Over the next 24 to 36 months we will see a new

phase develop for IT organizations with 76 of respondents expecting IT to manage the roadmap for a technology infrastructure that integrates AI-driven technologies (see Figure 9) However IT canrsquot bring about this mammoth change alone The encroachment of AI into many business functions will require increased business-IT collaboration particularly as the goals for AI initiatives are decidedly business-focused Seventy percent of respondents plan to create plan and commission intelligent machines with both IT and business involved By strategically embedding themselves in the business IT can bridge the gap with business and reclaim the mandate of ldquopreferred partnerrdquo

bull Augmented and virtual reality will define the new rules of engagement Reflecting the impor-tance of designing AI-driven systems for human interaction 68 of respondents said augmented realityvirtual reality (ARVR) will be a driving force for enabling workers to collaborate meaningfully with machines through a simple and intuitive interface (translating consumer behavior to business users as well as to machines for instance) Researchers are also investigating the use of AR to convey

Changing times spur shift in IT priorities

Q Which division in your organization ownswill own the intelligent machines roadmap

Q How critical will the following measures be in developing human-machineinteraction and operation

Q Which of the following best describes your plans for integrating intelligent machineswith legacy machines

24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning

Learning ampdevelopment

Innovation

Operations

Joint eortsbetween IT and

other teams

IT

49

52

65

67

68

75

Human-machine collaborationtesting environment

using augmentedvirtualreality tools

Human-machine skilldevelopment system for

workforce training

Human safety considerations inmachine design interaction

and operational machines

Human-machine operationalcontrol system with natural and

intuitive human-centric interfaces

Augmentedvirtual reality-basedinterfaces that allow

people to collaboratemeaningfully with machines

A task planning andallocation system based on a

task allocation framework

10

33

48

68

We plan to keep our intelligentmachines isolated

from other applications

We have identified corelegacy solutions that can

be enabled or even replacedby intelligent machines

We are looking for a hybridapproach that allows us to keepthe legacy system as is but also

deploy intelligent machines

We plan to replace some parts of our legacy machines

for successful intelligentmachines adoption

(Percent of respondents) (Percent of respondents)(Percent of respondents saying critical or very critical)

(Multiple responses allowed)Source Cognizant Center for the Future of WorkBase 622 senior executives Figure 9


to human workers what a robot is going to do next to make it easier to collaborate with them26 As a result of the human-machine interaction the AI systems learn to make better decisions in new and unknown scenarios while the human continues to adapt focusing on higher-value tasks (Please see our report ldquoAugmenting the Reality of Everythingrdquo to learn more about the impact of AR on the future of work)

bull Getting rid of old IT If your ancient systems are holding your organization back itrsquos time to reboot your core IT While 50 of respondents said theyrsquod need to replace parts of their legacy systems to enable intelligent machine adoption 48 said theyrsquore looking for a hybrid approach to ensure the coexistence of legacy infrastructure and intelligent machines Several IT infrastructure components including security networking and storage will be influenced by the deployment of intelligent machines Our recommendation is for leaders to focus on the specific processes or experiences they want to transform and assess whether a readily available solution exists (such as via cloud-based machine-learning platforms) At the same time businesses should be prepared for a substantial con-figuration process to make the technology fit their business requirements (For more on building an IT foundation for the future of work see our recent report ldquoThe Future of IT Infrastructurerdquo )

rust the new battleground for success There are two key questions when it comes to AI and trust Trust between workers and

employers and trust that the actions and decisions of intelligent machines wonrsquot result in unknown or negative consequences

Employer-employee trust pragmatist leaders neededThe threat to workersrsquo current jobs is an inescapable topic when it comes to AI Fifty-eight percent of respondents cited fear of job loss as a significant challenge to the adoption of intelligent machines (see Figure 10) and things could get worse ndash 39 of respondents said the feeling of isolation and company loy-

Machines altering human-trust mechanisms

Q How will trust levels in your organization change in the next five years

Peoplersquos fear of job loss will be a significant challenge in leveraging machines to meet future business objectives

Our people have a clear understanding about the changes in roles responsibilities and ownership as a result of changes in work

Our people have a clear understanding of the dierence between ldquojobsldquo and ldquotasksldquo

16

15

38

34

46

51

Trust between anemployee and an employer

Trust among employees

Increase

Decrease

Stay the same

Stay the same

Increase

Decrease

(Percent of respondents)



alty among workers will worsen in the next five years While 38 of respondents predicted a decrease in trust between employees and employers 34 said the same was true between employees themselves perhaps precipitated by massive job transition This is all the more alarming when industry reports claiming that 70 of HR executives expect AI will result in significant job losses in Asia over the next five years27 The fear of becoming irrelevant is a key concern for employees today

Considering that wersquore early in the emergence of intelligent machines itrsquos not surprising that just over one-third of respondents said they have a clear understanding about the changes in roles responsibilities and ownership that will result from changes in work Business leaders can change these perceptions in part by emphasizing the difference between ldquojobsrdquo and ldquotasksrdquo and that AI systems are often likely to take on a piece of a job with a clear need for human involvement In fact according to some sources such as the Asian Development Bank machines will boost rather than destroy jobs28

To grow trust leaders should proceed sensitively and gradually when introducing intelligent systems and focus on the human-machine collaboration issue In fact involving employees at an early stage in the development process of change will help them accustom themselves to the new technology ulti-mately elevating trust levels Prioritizing people will require changes in management culture which still tends to be hierarchical and authoritarian in many Asia Pacific organizations

Instilling trust in machinesFrom unexpected or biased results to perpetuation of dangerous errors many people fear ldquowhat can go wrongrdquo with intelligent machines In fact 65 of respondents were somewhat to very concerned about the unknown consequences of intelligent machine failures (see Figure 11) and the same number agreed that as intelligent machine use increases we will witness new and unknown consequences that may surprise us What if an AI-powered medical system makes a recommendation that leads to serious injury or even death This could have a catastrophic impact on companiesrsquo brand and finances Customer loy-alty is the result of trust cultivated over many years but it can be destroyed in a day

In spite of these concerns the majority of respondents (68) said the risk of unknown consequences is worth the business benefits delivered by intelligent machines How can we explain this dichotomy We

A catch-22 of human-machine trust

(Percent of respondents naming each as a top concern)

Q Which of the following are the biggest concerns in the event that an intelligent machine acts unexpectedly or its actions lead to negative outcomes (Top 5 shown below)

Legal issues

Loss of data (customer employee financial etc)

Financial loss

Loss of customers

Brand reputation

52

52

60

62

72

Somewhat-to-extremely concerned about theunknown consequences that could occur if an intelligent machine fails or delivers a negative outcome

The risk of unknown consequences is worth the business benefits delivered by intelligent machines




believe this is mainly because Asian leaders are fervently focused on the enormous benefits that an intelligent machine can deliver and theyrsquove yet to experience the negative aspect of machine wrongdoings It is similar to the data privacy issue although we all generally voice a desire for data privacy we are also very open with the information we share about our lives online In spite of the recent Facebook-Cambridge Analytic fiasco about data privacy many people are back to social media platforms sharing updates of their daily life

Business leaders can address the human-machine trust issue by flipping it on its head with the following three approaches

bull Put humans in the driverrsquos seat to bring greater transparency We need to train and hold AI engi-neers designers developers investors and innovators accountable for not only defining specific tasks for intelligent machines but also for recognizing the side effects of them In fact 76 of respon-dents said that developers should be held responsible for any side effects of the intelligent machine As machine intelligence continues to get smarter human intelligence will be needed to ensure it is deployed sensibly and safely After all humans were needed to train and develop the personalities of Applersquos Siri and Amazonrsquos Alexa to ensure they accurately reflected their companiesrsquo brands29

bull Win employeesrsquo trust No matter how well AI systems are designed if people donrsquot have confidence and trust in them businesses wonrsquot adopt them successfully In fact 58 of respondents said that it will be impossible to trust an AI system if it operates like a black box Employees will gain confidence in machines when they see positive results from the decisions they make Companies must focus on user acceptance as much as the technology itself by providing adequate training on the use of AI systems as well as the legal and business consequences of their failures

bull Develop self-control While organizations such as The Singapore Advisory Council have emerged to tackle the ethical use of AI and data30 therersquos no guarantee that nothing bad will happen Precau-tions aside nobody can guarantee that nothing bad will happen While regulations for machines will evolve at their own pace across countries they should not be considered the only resort for protect-ing consumer data and company brand Instead businesses need to focus on self-regulation based on openness and accountability with an obsession for maintaining consumer trust While there are no turnkey solutions for machine-risk management a good first step is for businesses to appoint a machine risk officer or amend an existing role to recognize and manage new risks and responsibilities related to machines (For more on jobs that will emerge in the digital age read our report ldquo21 More Jobs of the Futurerdquo)

As machine intelligence continues to get smarter

human intelligence will be needed to ensure it is

deployed sensibly and safely


HUMANS + INTELLIGENT MACHINES THE BALANCING ACT OF THE FUTURE OF WORK


The future of work will be shaped by two inevitable and powerful forces the growing adoption of the intelligent machine and the future partnership between humans and machines Striking a balance between the two will be the biggest opportunity and challenge for organizations While it is true that intelligent machines will increase productivity allow us to solve big problems and invent entirely new products services and experiences it is also certain that machines will replace some jobs and make some peoplersquos skills and capabilities irrelevant leaving behind those unable to keep up and compete

To win in this age of intelligent machines businesses should embrace the five Ts to reimagine tasks restructure teams remix talent reinvent the IT infrastructure and reestablish trust among workers and with intelligent machines

Business leaders in the Asia Pacific recognize that the rise of machine intelligence is the ultimate game changer today and theyrsquore preparing to navigate their companies through the coming years which will be full of change disruption opportunity and risk Elsewhere in the world organizations will follow suit knowing inaction will result in irrelevance



Methodology and demographicsWe conducted a telephone survey between May 2018 and July 2018 with 622 senior executives across industries in Asia Pacific The study was run in seven countries (China India Australia Singapore Japan Malaysia and Hong Kong) in English Japanese and Chinese Survey respondents were distributed across the financial services healthcare insurance life sciences travelhospitality manufacturing and retail industries

Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing

Financial services (excl insurance)

By industry

By employee size

17

10

24

26

15000 or more employees

10000 to 14999 employees


2000 to 4999 employees 40

Life Sciences


Endnotes1 ldquoArtificial Intelligence and Asiardquo UBS April 5 2017 httpswwwubscomglobalenwealth-managementchief-

investment-officekey-topics2017artificial-intelligence-shifting-asiahtml

2 Christina Larson ldquoChinarsquos Massive Investment in Artificial Intelligence Has Insidious Downsiderdquo Science Feb 8 2018 httpwwwsciencemagorgnews201802china-s-massive-investment-artificial-intelligence-has-insidious-downside

3 Ibid

4 Bay McLaughlin ldquoThis Week in China Tech AI Disrupting Insurance Claims China Opens Airspace for Drones and Morerdquo Forbes July 25 2018 httpswwwforbescomsitesbaymclaughlin20180725this-week-in-china-tech-ai-disrupting-insurance-claims-china-opens-airspace-for-drones-and-more56a9370a6f3d

5 ldquoAI and the Future of Workrdquo Irving Wladawsky-Berger blog Dec 18 2017 httpblogirvingwbcomblog201712ai-and-the-future-of-workhtml

6 ldquoGSMA AI Set to Become Worldrsquos Largest 5G Region by 2025rdquo GSMA June 27 2018 httpswwwgsmacomnewsroompress-releasegsma-asia-set-to-become-worlds-largest-5g-region-by-2025

7 ldquoCisco Visual Networking Index Global Mobile Traffic Data Forecast Update 2016-2021rdquo Cisco Feb 7 2017 httpswwwciscocomcenussolutionscollateralservice-providervisual-networking-index-vnimobile-white-paper-c11-520862html

8 ldquoAsia Pacific Set to Top Internet Traffic by 2020rdquo Casbaa June 8 2016 httpwwwcasbaacomnewsmember-press-releaseasia-pacific-set-to-top-internet-traffic-by-2020

9 ldquoChina May Match or Beat America in AIrdquo The Economist July 15 2017 httpswwweconomistcombusiness20170715china-may-match-or-beat-america-in-ai

10 Arjun Kharpal ldquoChina Wants to Be a $150 Billion World Leader in AI in Less than 15 Yearsrdquo CNBC July 21 2017 httpswwwcnbccom20170721china-ai-world-leader-by-2030html

11 James Vincent ldquoChina Overtakes US in AI Start-Up Funding with a Focus on Facial Recognition and Chipsrdquo The Verge Feb 22 2018 httpswwwthevergecom201822217039696china-us-ai-funding-startup-comparison

12 Alexis Chemblette ldquoHow China Is Trying to Become the Worldrsquos Leader in Artificial Intelligencerdquo Adweek May 8 2018 httpswwwadweekcomdigitalhow-china-is-trying-to-become-the-worlds-leader-in-artificial-intelligence

13 ldquoArtificial Intelligence to Nearly Double the Rate of Innovation in Singapore by 2021rdquo Fintech News March 6 2019 httpfintechnewssg29260iotartificial-intelligence-to-nearly-double-the-rate-of-innovation-in-singapore-by-2021

14 ldquoWhen Machines Do Everythingrdquo section of Cognizantrsquos website httpswwwcognizantcomwhen-machines-do-everything

15 ldquoSoutheast Asiarsquos First Virtual Bank Recruiterrdquo HRM Asia June 18 2018 httpwwwhrmasiacomcontentsoutheast-asias-first-virtual-bank-recruiter

16 Vanessa Mitchell ldquoAlibaba New Retail Model Goes Beyond E-Commercerdquo CMO July 24 2018 httpswwwcmocomauarticle644175alibaba-new-retail-model-goes-beyond-e-commerce

17 Japanrsquos Financial Industry Embracing AIrdquo Nikkei Asian Review May 18 2017 httpsasianikkeicomBusinessTechnologyJapan-s-financial-industry-embracing-AI2

18 Peter Kua ldquoHong Leong Bank Uses IBM Watson for Cognitive Bankingrdquo Asian Data Science Dec 6 2016 httpasiandatasciencecomhong-leong-bank-uses-ibm-watson-for-cognitive-banking

19 Allie Coyne ldquoHow ANZ Bank Is Expanding its Use of Cognitive Computingrdquo IT News Feb 22 2016 httpswwwitnewscomaunewshow-anz-bank-is-expanding-its-use-of-cognitive-computing-415402


20 Nurfilzah Rohaidi ldquoExclusive Japan Utility Trials AI for Predictive Maintenancerdquo GovInsider May 26 2017 httpsgovinsiderasiadigital-govexclusive-japan-utility-trials-ai-for-predictive-maintenance

21 Cheryl Goh ldquoArtificial Intelligence Platform Launched to Help Doctors Crunch Patient Data Make Better Diagnosesrdquo Channel NewsAsia July 6 2018 httpswwwchannelnewsasiacomnewssingaporeartificial-intelligence-platform-launched-to-help-doctors-crunch-10505634

22 Jerry Smith ldquoNot Your Fatherrsquos Call Center How Cognitive Computing Can Boost Customer Satisfaction and Agent Retentionrdquo Digitally Cognizant April 30 2018 httpsdigitallycognizantcomcall-center-cognitive-computing-codex3551

23 Stephanie Vozza ldquoThese Are the Five Soft Skills Recruiters Want Mostrdquo Fast Company Jan 26 2018 httpswwwfastcompanycom40520691these-are-the-five-soft-skills-recruiters-want-most

24 WeWork website httpswwwweworkcomenterpriseglobal-access

25 Rubaitul Azad ldquoChinarsquos AI Dream Is Well on its Way to Becoming a Realityrdquo Tech Hacker April 22 2018 httpswwwtechhackercoinchinas-ai-dream-is-well-on-its-way-to-becoming-a-reality

26 Evan Ackerman ldquoAugmented Reality Makes Robots Better Coworkersrdquo IEEE Spectrum July 19 2018 httpsspectrumieeeorgautomatonroboticsrobotics-softwareaugmented-reality-makes-robots-better-coworkers

27 Samantha Chen ldquoHow Artificial Intelligence Is Taking Asia By Stormrdquo Techwire Asia March 27 2017 httpstechwireasiacom201703artificial-intelligence-taking-asia-storm

28 ldquoRobots Will Boost Rather than Destroy Jobsrdquo Financial Times April 10 2018 httpswwwftcomcontent69602a90-3d4a-11e8-b9f9-de94fa33a81e

29 H James Wilson and Paul R Daugherty ldquoCollaborative Intelligence Humans and AI Are Joining Forcesrdquo Harvard Business Review July-August 2018 httpshbrorg201807collaborative-intelligence-humans-and-ai-are-joining-forces

30 Mohit Sagar ldquoSingapore Releases First AI Governance Framework in Asiardquo Open Gov Jan 25 2019 httpswwwopengovasiacomsingapore-releases-first-artificial-intelligence-ai-governance-framework-in-asia

Manish BahlSenior Director Cognizantrsquos Center for the Future of Work Asia-Pacific

Manish Bahl is a Cognizant Senior Director who leads the companyrsquos Center for the Future of Work in Asia-Pacific A respected speaker and thinker Manish has guided many Fortune 500 companies into the future of their business with his thought-provoking research and advisory skills Within Cognizantrsquos Center for the Future of Work he helps ensure that

the unitrsquos original research and analysis jibes with emerging business-technology trends and dynamics in Asia-Pacific and collaborates with a wide range of leading thinkers to understand how the future of work will take shape He most recently served as Vice-President Country Manager with Forrester Research in India

Manish can be reached at ManishBahlcognizantcomLinkedIn httpsinlinkedincominmanishbahlTwitter mbahl

About the author




About the Center for the Future of Work Cognizantrsquos Center for the Future of WorkTM is chartered to examine how work is changing and will change in response to the emergence of new technologies new business practices and new workers The Center provides original research and analysis of work trends and dynamics and collaborates with a wide range of business technology and academic thinkers about what the future of work will look like as technology changes so many aspects of our working lives For more information visit Cognizantcomfutureofwork or contact Ben Pring Cognizant VP and Managing Director of the Center for the Future of Work at BenjaminPringcognizantcom

About Cognizant Cognizant (Nasdaq-100 CTSH) is one of the worldrsquos leading professional services companies transforming clientsrsquo 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 US 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 wwwcognizantcom or follow us Cognizant

World Headquarters

500 Frank W Burr BlvdTeaneck 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

5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060

APAC Headquarters

1 Changi Business Park CrescentPlaza 8CBP 07-040506Tower A Singapore 486025Phone + 65 6812 4051Fax + 65 6324 4051

copy Copyright 2019 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any meanselectronic 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 387304

The transition to intelligent machines wonrsquot happen

without an acute focus on the relationship between

humans and machines how the two will collaborate

and how the current workforce and the business

itself will adapt to AI

March 2019






Executive Summary




















Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304






Executive Summary




















Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304




















Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304













Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304











Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304


Quick Take















62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304









62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

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g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304







62

64

65

68

68

70

71

73

82











76

82





















45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304














45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304




45 46

Inform


chnology

New product a


Custom

er service

RampD and innovatio

n


anagement

Manufactu

ring

Salesmarketin

g


ent


Productio

n and operations

Strate


entation

Financial managem

ent accountin

g

budgeting a

nalysis and re

porting

Legalrisk m

anagementcom

pliance


ment






105 97

138

121 132 128

116

65 58

92 81

75 80 70


TravelHospitality

Life Sciences







126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304





126 116

156

138 148

136 124


TravelHospitality

Life Sciences












58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304









58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304







58

61

62

64

65

67

68

70

71

72

























5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304










5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304








5

45

50




















10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304










10

18

22

50

Dont know























Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304


















Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304







Ethics and legal

Social media

Designing

Selling

Inclusion

Language

Global Operating

Technical

Analytical

Decision-making

Communication

Interpersonal

Innovation

Leadership




50

60

65

71

75

78

78

80

81

81

82

84

85

87














24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304











24

27

30

41

43

56

62

70

76

Others

HR

Finance

Corporate planning


Innovation

Operations


other teams

IT

49

52

65

67

68

75




workforce training









10

33

48

68






















16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304












16

15

38

34

46

51



Increase

Decrease

Stay the same

Stay the same

Increase

Decrease












Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304










Legal issues


Financial loss

Loss of customers

Brand reputation

52

52

60

62

72























Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304



















Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304










Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304








Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304



Demographics

0 5 10 15 20 25 30 35 40

8

12

17

21

21

TravelHospitality

4

Healthcare

Insurance

Retail

Manufacturing


By industry

By employee size

17

10

24

26





Life Sciences





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304





3 Ibid

































About the author






World Headquarters






APAC Headquarters



Codex 387304

















About the author






World Headquarters






APAC Headquarters



Codex 387304





About the author






World Headquarters






APAC Headquarters



Codex 387304





World Headquarters






APAC Headquarters



Codex 387304




World Headquarters






APAC Headquarters



Codex 387304



World Headquarters






APAC Headquarters



Codex 387304

Documents

Humans + Intelligent Machines - cognizant.com · The transition to intelligent machines won’t happen without an acute focus on the relationship between humans and machines, how