New work, new world

Hard numbers are finally here. Generative AI could deliver up to $1 trillion in annual growthby 2032, while potentially disrupting up to 90% of existing jobs. How can leaders navigate
an upheaval of this scale and realize the technology’s full potential? By investing in people.

Table of contents$1
8 Atrillion
story
Three scenarios that put the final
productivity tally in human hands

10

Decoding job
disruption
Exposure and friction scores
reveal the scope of changeon
19 2032
the horizon
How a spike in adoption
may drive a strategic evolution

25

Forging a newtrust compact
Four recommendations for
maximum productivity

30 Final word
31 About the authors
32 MethodologyAs generative AI has swept across theglobal stage, it’s been clear something
big was about to happen—to theeconomy, to business and society, topeople’s livelihoods. But how big would
the change be? When would it hit? And
would the impact be destructive or
productive, embraced or feared, unifying
or polarizing?  
Answers are beginning to emerge, and it appears gen AI
will be very big indeed.

Annual value of gen AI to US GDP by 2032

$1.043 trillion
Businesses adopting generative AI

By 2032, according to our research, the technology could
inject the US GDP with up to $1.043 trillion in additional
annual value—an economic surge greater than theentire US construction industry.
We’ll see a spike in gen AI adoption; while as many as 13%
of companies could be leveraging the technology in three
to four years, nearly half could embrace it in a decade’stime, according to our most bullish scenario.

13%

46%

This remarkable projection not only underscores thepower of gen AI to enhance, augment and automate
many work tasks across all areas of the economy,
but also sets the stage for a profound shift in how weapproach work, productivity and economic growth.

in 3-4 years

in 10 years

3

New work, new world

To quantify generative AI’s impact on
productivity and the future of work, wepartnered with Oxford Economics tocreate an economic model. The modelis calibrated to reveal three scenarios
reflecting low, middle and high levels of
business adoption.
While multiple studies have been published on this topic,
we wanted to analyze how this powerful technology
could impact actual people, actual jobs and actual
careers—and by extension, business productivity
and the economy. Our model digs into the 18,000 tasks
that drive the US economy, and carefully examines
the impact generative AI could have on the 1,000 jobs
composed of these tasks.
While we focused on the US workforce, the generalthemes that emerged from the findings can be applied
globally. This is because the tasks—and the theoreticalability for generative AI to perform or assist with them—remain the same despite differences in their relative
importance and rate of adoption from country to country.
See more details on the methodology.

4

New work, new world

Our model digs into the
18,000 tasks that drive the
US economy and carefully
examines the impact
generative AI will have on
the 1,000 jobs composed ofthese tasks.

What we learned has profound
implications for workers—and for the
future of work. Our model shows that in
the next 10 years, most jobs (90%) could
be disrupted in some way by generative
AI. Everyone from administrative
assistants to CXOs will be affected.
In some job families, workers may face months ofjoblessness as they seek to transfer their existing skills
to new roles. In total, approximately 9% of the current
US workforce may be displaced by generative AI. Basedon an analysis of previous economic shifts, it’s possible
11% of displaced employees—or almost 1% of the total
workforce—may struggle to find new work again.
Left unmanaged, this level of disruption would havesevere consequences for not only organizations and thepeople who work in them but also for productivity itself.
Because achieving the high end of our productivity
forecast requires two things: high levels of businessadoption of generative AI and low levels of disengaged
or permanently displaced employees. And here’s wherebusinesses—and the humans who run them—come in.
Addressing both of these factors requires trust—trust
between AI makers and AI users, between businesses
and policy makers, between employer and employee,
and trust in the technology itself. While all theseareas are vital, we are primarily focused on the lasttwo, because it’s here that businesses can exercise
the most control to maximize the productivity
potential of generative AI.
5

New work, new world

90%

of jobs could experiencesome degree of
disruption by gen AI

The time is now, as gen AI begins to ramp
up, for leaders to lay the foundation for
a new trust compact, ensuring gen AI
is a positive force not just for economic
productivity but also for workers andsociety. If they succeed, we could beentering an age of unfathomable wealthand efficiency. If they don’t, we could
be facing a long period of unrest andstrife, glimmers of which can already bewitnessed among AI makers themselves.
By embracing the following tenets, organizations cantake the crucial first steps to bolster confidence, build
trust and open the door to a new age of productivity
and prosperity.

1. Take care of your people
Roll out strategic reskilling programs at a pace and scale never seen before.

2. Innovate or stagnate
Plan for how you’ll operate and create value in a decade’s time.

3. Build confidence with transparency
Show how AI outcomes will serve the broader goals of business and society.

4. Put AI gains to good use
The wealth generated from AI also needs to benefit the workforce and the world.

6

New work, new world

A $1 trillion productivity story—with a catchThree scenarios that put the final
productivity tally in human hands

Generative AI is a breathtaking feat oftechnology that could have a substantial
impact on society and the economy. But
the way it plays out will be grounded indeeply human factors. Will we resist or
welcome it? Adapt or stay unchanged?
The extent of that impact will be determined by the rateof business adoption and how quickly people adapt
to working in new ways. These factors have historicallyslowed other productivity innovations. For example,
microprocessors arrived in the early 1970s, but it took two
decades for personal computers to be widely adopted
and for productivity gains to materialize.
This is why we worked with Oxford Economics to develop
three adoption scenarios.
If business adoption of gen AI is low, the annual
productivity boost in the US could grow to 1.7% by 2032.
Even this bearish estimate represents a significant
upswing, given that the long-term annual average US
growth hovers around the 2% mark. And if adoption
comes in at the high end, that figure could soar to 3.5%
by 2032 (see Figure 1). From the perspective of total
economic output, this means US GDP would see a boost
between $477 billion and $1 trillion in 10 years’ time.

8

New work, new world

Three scenarios for gen AI impact on US GDP
Change intotal factor
productivity

High$1.043

4.0%

trillion

3.5%

Central

3.0%

$752

2.5%

billion

2.0%

Low

$477

1.5%
1.0%

billion

0.5%
0.0%
2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

Generative AI could inject anywhere from $477 billion
to $1 trillion into the US economy by 2032, dependingon the level of business adoption.
Source: Oxford Economics and Cognizant
Figure 1

Decoding job disruption

Exposure and friction scores
reveal the scope of changeAnother key factor that will either
bolster or deflate gen AI-related
productivity gains is disruption to jobs
and job markets. The more disruption
workers face, the more difficult it will
be to adapt to new ways of working. To
understand what that disruption will look
like, we analyzed almost 1,000 professions
and their associated tasks and calculated
a “theoretical maximum exposure score”
for each one (referred to in this report as
the “exposure score”).
A lower score means a lower percent of the job’s
high-value tasks will be automated, so people inthat profession will be relatively unaffected. For
example, those who work on a heating, ventilation
and air-conditioning maintenance crew have an
exposure score of 5% because generative AI will
neither assist with nor automate most of their job tasks.
Contrast this with the 62% exposure score for computerscience teams—individuals who do these jobs arelooking at major change.

10

New work, new world

Explained: Exposure score
This score reflects the degree to which
an occupation will be affected bygenerative AI. The exposure score
takes into account how many of the
job’s tasks could be fully automatedby gen AI; how many could be
assisted; and the relative importanceof these tasks.

Through most of the report, unlessotherwise indicated, we use the“theoretical maximum exposure
score,” which assumes immediate andunanimous adoption of gen AI, vs. the“predicted exposure score,” which is
calculated using the central adoption
scenario.

Example: Family medicine physician•

Gen AI could automate many lower-value admintasks, such as collecting and maintaining patient
information.

•

It could also assist with higher-value diagnostic tasks.

•

But it would be less effective for an array of very
high-value tasks, such as successfully helping apatient with chronic illness to make life-saving
lifestyle changes.

•

Resulting exposure score: 33.3% by 2032

By 2032, few jobs could go untouched by generative AI
Some impact:
Exposure scores of at least 5%

Greatly impacted:
Exposure scores of at least 25%

90%
90%

52%
52%

Most jobs will see some change from gen AI, and over half could be greatly impacted.

Some
Some
impact:impact:Source:Oxford Economics
and Cognizant
Exposure
ExposurescoresscoresFigure 2
of of
at at
least
least
5%5%

11

New work, new world

Greatly
Greatly
impacted:
impacted:
Exposure
Exposurescoresscores
of of
at at
least
least
25%
25%

Little impact:
Exposure scores of 5% or lower

10%10%
LittleLittleimpact:impact:Exposure
Exposurescoresscores
of of
5%5%
or or
lower
lower

In fact, our research shows CEOs could see an exposure scoreof more than 25% by 2032, as they begin using gen AI for
everything from reviewing reports and analyzing operations
to conducting competitive assessments and making
strategic decisions. Even areas long considered the purview
of senior execs—cost reduction, program improvement andpolicy change—will increase significantly in “automatability.”
For some people, gen AI-induced change is a reality today.
Jobs involving credit analysis, computer programming, web
development, database administration and graphic design
already have an exposure score of about 50%. By 2032, as
the technology advances, exposure scores for some of thesejobs will climb to 80%.
Other types of workers will experience a surge in disruption
as time goes on, as maturity and trust in gen AI lead to more
applications of it (see Figure 3). For example, while customer
service representatives currently have an exposure score of
11%, that is projected to surpass 63% by 2032.
12

New work, new world

Job impact will grow over time

The degree to which
a CEO’s tasks could
be automated could
see them with an
exposure score ofmore than

From 2023 to 2032, the percent
of jobs with high exposure scores(25% and above) could increase
from 8% to 52%, a 550% uptick.

8%

15%

40%
Percent of jobs

In the past, technology advances andautomation largely impacted manual laborand process-centric knowledge work. This
time around, it’s different. The script has
been flipped; recall our HVAC technician
whose job will see little change. Instead,gen AI will change what’s generally calledknowledge work—everyone from entrylevel number crunchers to seasoned headsof business units to C-suite executives will
see their job evolve over the next decade.

25

%

37%

20%

by 2032

28%

Highly impacted
jobs may surge

10%

31%

10%
2023

2032
Exposure score

0-5%

5%-10%

10%-25%25%-50%Chart data reflects “theoretical maximum exposure scores.” Seemethodology for more on how these scores are calculated.

Source: Oxford Economics and Cognizant
Figure 3

50%+

550

%

by 2032

As the exposure scores demonstrate, gen
AI will affect some jobs more than others.
Some workers will need to learn how towork with gen AI as it assists with some
job tasks, while others will see their roles
taken over by the technology.
To understand how difficult it will be for displaced
workers to find new employment, we calculated a“friction score” for various occupational groups. Thisscore represents the ease or difficulty of finding new
employment with existing skills. (See methodology
for more on how the friction score is calculated.)
Interestingly, some of the hardest hit occupational groups(those with high exposure scores) have relatively low
friction scores, meaning their journey to new employment
is less complex.

Explained: Friction score

13

High friction scorechallenging= moreto find new work

Low friction score

to find= easier
new work

New work, new world

Some of the hardest hit
occupational groups (thosewith high exposure scores)
have relatively low friction
scores, meaning theirjourney to new employment
is less complex.

To better understand this dynamic, weplotted an array of job categories by their
exposure scores and friction scores (seeFigure 4). In this case, however, we used
the “predicted exposure score,” whichtakes into account a more gradual rateof adoption of generative AI, as reflectedin our central scenario. As a result, thesepredicted exposure scores are lower than
the theoretical maximum exposure scores
used throughout the report, which assume
unanimous and immediate adoption.
14

New work, new world

A look at job and worker impact in five years
We’ve mapped jobs that will be most impacted, and those with the most difficulty or ease of finding a new job.
Average friction
score (2027)

9%High job impact,
low difficultyfinding a new role

8%

High job impact,high difficultyfinding a new role

3

7%
6%
Predictive
exposurescore(2027)

9

5%
2

4%

4

3%

1

2%

Low job impact,
low difficultyfinding a
new role

1%

7

6

0

10

20

20

30

12 11

21

13

15

14

40

50

60

Average
exposurescore(2027)

Low job impact,high difficultyfinding a new role

8

10

19

0%

5

17

16

70

18

22

80

90

100

Friction score (2027)
Data in this chart represents predicted exposure scores versus theoretical maximum exposure scores. See methodology for how these scores are calculated.
Bubble size represents the relative number of workers in the job category. Color coding of bubbles corresponds to wage quintile in 2022, with darkest blue representing
the fifth quintile and lightest representing the first.

Source: Oxford Economics and Cognizant
Figure 4

15

New work, new world

1 Management
2 Business and financial operations
3 Computer and mathematical
4 Architecture and engineering
5 Life, physical and social science
6 Community and social service
7 Legal
8 Educational instruction and library
9 Arts, design, entertainment,
sports and media
10 Healthcare practitioners andtechnical
11 Healthcare support
12 Protective service
13 Food preparation and serving
related
14 Building and grounds cleaning
and maintenance
15 Personal care and service
16 Sales and related
17 Office and administrative support
18 Farming, fishing and forestry
19 Construction and extraction
20 Installation, maintenanceand repair
21 Production
22 Transportation and material
movingCombining the friction scores and thepredicted exposure scores provides
us with a clear lens into the disruption
ahead. For instance, software developers
and database administrators (withrelatively high predicted exposure scoresof 8%) have relatively low friction scoresof about 40, meaning their journey to new
employment is less complicated, likely
due to their in-demand skill set.
But many may face more significant and prolonged
disruption from generative AI. Office and administrativeroles (such as clerical assistants and receptionists)have a predicted exposure score of 4% but are saddledwith a friction score of 80, indicating they will face
an uphill battle.
US Census Bureau data, which informs our modeling,
indicates this displacement won’t be easily solved.
Previous periods of change have seen around 11% ofdisplaced workers struggle to find work—taking an
average of 39 weeks to find new job opportunities.
Disruption at that pace and scale is significant—and
if left unchecked could result in trust-shattering levelsof unemployment and economic instability for large
portions of the workforce.

16

New work, new world

Disruption at that pace andscale is significant—and if
left unchecked could resultin trust-shattering levels of
unemployment and economic
instability for large portions of
the workforce.

At the same time, we also foresee anupside to generative AI workforce
disruption, such as its positive impact
on ongoing labor shortages thatexist in key areas of the economy. For
example, in healthcare, ongoing staffing
challenges are driving up service costs
while dragging down service quality. Our
exposure scores indicate that emergency
physicians, for example, will see up to
one-third of their tasks automated overthe next decade—a process that will
enable organizations to deliver more
healthcare services without the need toramp up hiring.
Another potentially positive impact of gen AI on labor
challenges is the way it can be flexibly deployed toaccentuate an individual’s existing strengths whilesoftening their weaknesses—in effect enabling abroader spectrum of cognitive capabilities into a wider
range of roles. Armed with the right tools, for instance,
an equity analyst may not need to be a mathematicalexpert if generative AI handles that part of the job.
Instead, they would perhaps lean on a strength such ascommunication to add value in the workplace. In thisway, generative AI has the potential to significantly lower
barriers to entry to a large segment of the economy.
17

New work, new world

Positive impacts of generative
AI on the workforce are its
ability to address ongoinglabor shortages in key areasof the economy such as
healthcare, and its ability toaccentuate an individual’s
existing strengths whilesoftening their weaknesses.

2032 on the horizon

How a spike in adoption may
drive strategic evolution

Gen AI-driven change will not happen
all at once, nor will it follow an evenly
paced trajectory. Rather, our analysis
indicates it will follow a similar s-curve
as other tech advances, such asenterprise-grade software: a gradualrise, to a dramatic spike, to a plateau inwhich the technology becomes refinedand pervasive. This progression will
unfold for gen AI as businesses adapt,
workers reskill and stakeholders adjust
to the societal impact of the technology(see Figures 5 and 6).

Fast uptake in the next decade
Adoption of generative AI could move quickly
in the decade ahead.
50%
45%
40%
35%
30%

8-11
years

25%
20%

4-8
years

15%
10%
5%
0%

1-4
years

Nearly halfof businesses
will adopt
gen AI by
this timeframe

Adoption will
spike, to justover one-thirdof businesses

Adoption will
start off slowly,
with 13% ofbusinesses
implementing

2023-2026

2026-2030

2030-2033

Chart data reflects the highest possible adoption rates, as reflected in our high adoption scenario.
*To ensure we captured the full adoption rate through 2032, we included 2033 data in the calculation for this figure.

Source: Oxford Economics and Cognizant
Figure 5
19

New work, new world

The 30-year adoption viewAfter the 15-year mark, the steep climb slows, but adoption
continues to grow for at least 20 more years.
High
Adoption could
ultimately reach
100% of allcompanies

Adoption
rate

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

CentralLow
15% may
not adopt inour 30-year
timeframe

0

5

10

15
Year

Source: Oxford Economics and Cognizant
Figure 6

20

New work, new world

20

25

30

The difference between gen AI
and its predecessors is one ofpace, scale and the uniquesocietal challenges that must
be overcome. In our analysis,
there are three stages to thisgrowth story over the next 10
years—each with its own drivers,
challenges and opportunities.
These stages can and should
guide an organization’s approachto making the major institutionalchanges that will pave the way
for a positive and productive
embrace of generative AI.

2023 2026
Experimenting and preparing
This is the critical time for organizations to commit tomajor overhauls of their business and operating models
and to establish a foundation of trust with their employees
and the world at large.

Low-risk
experimentation

Gradualadoption

Caution anduncertainty
The stage we’re in today is characterized byexperimentation and cautious implementation.
Organizations are tentatively deploying gen AI
for tasks that are manageable in scope andlow in risk—like image creation, text generation
for reports and emails, and code suggestions
for developers. For example, our data showsthat marketing managers have a theoreticalmaximum exposure score of 17% as generative AI
helps execute tasks such as content generationand market analysis.
21

New work, new world

While our most bullish scenario foresees
business adoption of only 13% in this timeframe,
the importance of this phase cannot be
underestimated. This is the critical time fororganizations to explore and commit to major
overhauls of their business and operating
models, and to begin establishing a foundationof trust with their employees and the world
at large. Only with a strong bedrock willorganizations be able to drive productivity andlead their markets for decades to come.

13%

Business adoption(“high” scenario)

2026 2030
Confident adoption

Regulatory
clarity

This stage will see a notable rise in gen AI’s role acrossa range of professions. Exposure scores for generalmanagers and operations managers will grow from
18% today to 52.7% by 2032, with the bulk of the growthoccurring in this time period.

In the next phase, we expect accelerated
adoption. Our top-end prediction reveals
adoption leaps from 13% to 31% in just four toeight years. Looking at historical examples—suchas the gradual infiltration of enterprise software
into heavily regulated industries—we can expect
adoption to grow based on greater regulatory
clarity, more robust skills management, stronger
public demand and a greater understanding of
how the technology resolves business challenges.
This stage will see a notable rise in gen AI’s roleacross a range of professions. For example,
exposure scores for general managers and22

New work, new world

operations managers will grow from 18% today
to 52.7% by 2032, with the bulk of the growthoccurring in this time period.
Moreover, the generative AI regulatoryenvironment—currently a mere patchwork—will
come into focus. During this stage, gen AI will
move beyond mere task automation to becomea core component of business transformation
strategies. Forward-thinking companies will havedeveloped robust technological infrastructures
and a culture that not only accepts but also
encourages continuous innovation.

Lower barriers
to entry

Reshaped businessand operating models

31%

Business adoption(“high” scenario)

2030 2032
Embedded organizational collaboration
With a solid foundation of trust, gen AI will assist roles
that have traditionally been the exclusive domain of humanintuition and experience. For example, our exposure score
for legislators hits 37% by 2032.

As gen AI continues to mature, we will see aneven greater acceleration in adoption. In our
most optimistic prediction, we anticipate 46% ofbusinesses will have meaningfully adopted thetechnology, heralding a new era of partnership
between gen AI and decision makers.
With a solid foundation of trust establishedthrough consistent application and provenreliability, gen AI will assist roles that havetraditionally been the exclusive domain of humanintuition and experience. For example, ourexposure score for legislators hits 37% by 2032.
23

New work, new world

In this phase, the technology is not merely atool but a collaborator that offers nuanced
analysis and strategic insights. Thispartnership is predicated on the reliability
and sophistication of AI systems, whichhave evolved to understand and anticipate
the complex needs of businesses. The data,
strategy and culture of innovation fosteredin the earlier phases enable generative AI tocontribute meaningfully to high-level decisionmaking, working alongside human leaders
to navigate the complexities of modern
business landscapes.

Widespreaduse cases

Provenreliability

Meaningful role indecision making andstrategic insights

46%

Business adoption(“high” scenario)

Forging a new trust compact

Four recommendations for
maximum productivity

While the timeframe of this researchspans over a decade, leaders have little
time to spare as they prepare to get themost out of generative AI.
While generative AI promises to boost
productivity, its full potential isn’t a given.
The technology’s uncertainties may slow
its adoption and impact. Additionally, its
influence on the labor market could affectglobal prosperity and trust. Navigatingthese aspects is key to realizing AI’s benefits.
We’ve established four areas where webelieve businesses can lead the charge
by forging the levels of trust needed to
optimize productivity so businesses, workers
and societies can thrive in the age ofgenerative AI.
25

New work, new world

1. Take care of your people

The integration of generative AI into businesssettings is poised to transform job roles and bringabout some inevitable restructuring, highlighting
the need for adaptability and continuous
learning. However, the fear of layoffs should
neither be downplayed nor ignored. Addressing
this concern is crucial for fostering trust between
employer and employee, and requires proactive
and robust measures.
No business can guarantee layoffs will not occur.
However, all organizations have it in their power
to roll out a new generation of reskilling programs
at a level never seen before. Rather than being atactical, optional add-on to an employee’s work
life, these programs should be an essential part of
the workday.
Reskilling programs could take a variety of forms.
In some cases, businesses could partner withhigher education institutions to continuously
revamp curricula in selected skill areas. In others,organizations might collaborate with policy
makers, government officials, regulators and evenacross industries to create shared “academy”
26

New work, new world

systems that would not only teach gen AI skills,
but also establish new job tracks for people inroles with high exposure or friction scores.
The more robust the reskilling program is,
the more it will become a marker of competitive
advantage and brand reputation, which
will serve to attract and retain key talent.
Reskilling could ultimately be so important
to a business’s standing that it becomes anarea of shareholder scrutiny, with investments
and outcomes highlighted in the company’s
public reporting.
By reskilling even those slated to leave thecompany, businesses will grow the overall
productivity pie by specifically equipping people
to succeed and move freely between roles in the
new world. They’ll also bolster their brand as acompany that’s working with—not against—thevast socioeconomic impacts of generative AI.
Future regulations may even require businesses
to provide support for displaced workers when it
can be proved a job loss was directly caused bygenerative AI-powered automation.

The fear of layoffs
should neither
be downplayed
nor ignored.
Addressing
this concern
is crucial for
fostering trust
between employer
and employee.

2. Innovate or stagnate

As much as some employees might put off
thinking about generative AI and the changes itwill require, they won’t want the same from theiremployers. Employee trust will hinge on whether
their employers “get it” and are preparing for the
next wave of change.
To shore up employee trust, and fully participate
in all generative AI has to offer, businesses needto think about how the technology will change
how they do business and even the businessthey’re in. Rather than engaging in quarter-byquarter thinking, they need to reimagine how they
will operate and create value in 10 years’ time.
Gen AI offers organizations new ways to earnrevenue, improve operational efficiency, innovate
products and services and, ultimately, redefine
their businesses. For example, infrastructureservices companies in the banking sector may
find it less lucrative to provide the plumbingfor global finance than to enable access todata for client large language models. Or life
sciences companies may carve out an efficientand cost-effective path to direct consumer
engagement using gen AI interfaces.
27

New work, new world

This type of evolution will require both incremental
adoption of new technologies and a fundamental
rethinking of business practices, corporate
structures and the nature of work itself.
From the boardroom to the business unit,organizations should form cross-functional teams
that continuously unravel process glitches andrevenue roadblocks, and pave new paths into the
future. New decision-making frameworks will beneeded to quickly assess the best way forward—whether it’s acquiring a burgeoning startup or
forming new partnerships.
To execute newly-hatched business models,organizations will need a robust technologicalinfrastructure designed to accommodate therapid pace of gen AI development. Additionally,
new governance policies, organizational
structures and operating processes will be
required to deal with massively accelerated,
dynamic and autonomous operations. The
companies that prosper will be those that makeclear to all stakeholders that they understand thetransformative potential of gen AI and make it acentral pillar of their strategic planning.

Employee trust will
hinge on whether
it’s clear theiremployers “get it”
and are preparing
for the next wave
of change.

3. Build confidencewith transparency

Many technologies have roiled the workforce,and all have seen some level of mistrust. But
no technology has provoked as much fear andmisgiving as generative AI. Whether it’s thetechnology’s black-box logic or its potential
for bias and error, generative AI is simply notuniversally trusted.
Building trust in generative AI requires a clear
and demonstrable commitment to mitigating
any detrimental effects of the technology
on people, society and the business itself.
Organizations must be transparent about how
gen AI systems are developed and deployed,
the values they’re designed to uphold, andthe measures in place to ensure they do so.
This transparency must show in concreteterms how gen AI decisions are made, andhow outcomes serve the broader goals of thebusiness and well-being of customers.
28

New work, new world

There are many ways to establish confidence inthe decisions and outputs of a gen AI system.
For instance, trust metrics can be incorporated
into the application by providing visibility into
the data the model used to make its decisions.
Further, the system could produce a confidence
rating in the decision itself. As design of thesesystems matures, their inner workings will
become less mysterious, and overall confidence
in AI-generated content and decisions will grow.
Organizations must also build in safeguards
that minimize the risk of gen AI bias, error
and ethically problematic decisions. Using anarray of gen AI agents, surrogate predictive
machine-learning models, explainable decisionmodels, and human oversight and intervention,businesses can ensure their systems provide
non-biased, ethically responsible responses
and decisions.

Instilling trust in
the technology will
be key to businessadoption,
which, as ourresearch shows,is necessary
to reach thehighest levels of
productivity.

4. Put gains to good use
If you asked 100 employees how they thought
their employer would use productivity gains
realized by generative AI, 99 would likely respondwith a skeptical—and perhaps historicallyaccurate—response. But given the sweepingchange generative AI promises for work andsociety, the past cannot serve as prologue.
Employee-employer trust will only beforged when businesses give at least someconsideration to how generative AI benefits
will be distributed across society—without
hampering innovation and investment. We see
this as the next front of stakeholder capitalism.
In the same way today’s net zero programs
redistribute profits to take care of the planet,
tomorrow’s generative AI gains might be sharedwith employees and society.
While there are far less radical approaches,some businesses might completely transformtheir corporate credo and dedicate a highpercentage of their generative AI-driven financial
gains to a social cause, as sustainabilityminded businesses do now to combat negativeenvironmental impacts.
Another possible solution is for those who gain
the most from gen AI-related advances to29

New work, new worldcontribute proportionally to a fund dedicated tosocietal benefits. For instance, investments could
be made in educational programs to further
support reskilling. Or funds could be earmarked
for research into sustainable technologies orhealthcare improvements, laying the groundwork
for a society that is not only more efficient but
also healthier and more resilient.
Another potential use of gen AI-related
abundance, and surely a popular one, would
be to improve employees’ work lives, perhaps
by shortening the workday or week. Indeed,
many have already begun experimenting with or
instituted a four-day workweek.
The options presented here are meant tostimulate thought and discussion among
business and government leaders, policymakers,
community organizers and philanthropic
organizations. Each choice comes with its own
set of complexities and would require carefulplanning and implementation.
But as with reskilling, it will soon be the case thatthe businesses which think progressively about
reinvestment will be seen as trusted brands thatare positively engaged in the generative AI age
and the productivity it can deliver.

Employeeemployer trust
will only be forgedwhen businesses
give at least someconsideration to
how generative
AI benefits will be
distributed acrosssociety—without
hampering
innovation andinvestment.

Final word

When we started our research,
the world was grappling witha mysterious advancement that had
catapulted from popular curiosity
to top agenda item for businessand government leaders.
For all its potential, generative AI—like many
AI tools before it—inspires equal measures of
intrigue and aversion, attraction and rejection.
The truth is, for everyone who welcomesgenerative AI into their daily lives, there’s anequal number who fear the downside of thesetechnologies: their potential to widen prosperity
gaps, spread misinformation and reshape thevery essence of what we’ve always understood
to be human.  
The questions surrounding gen AI run deep.
Can we trust complex decisions made with
non-human (and possibly inhumane) logic?
Would we rely on an algorithm to have our bestinterests in mind? Would we feel safe interactingwith an entity if we’re unsure whether it’s a person
or a sophisticated AI system?
30

New work, new world

Such questions may not be answerablewith an economic model. It will take time,
experimentation and new levels of trust forged
between all who would be impacted, forbetter or worse, by generative AI. No one will,or should, welcome an engine of economic
growth if its achievements are detrimental
to a vast swath of humanity, their sense of
purpose and their very foundation of what itmeans to earn a living.
The world has the power to usher in a newera of productivity that’s extraordinarily positive
for the economy, workers and society itself. This
path requires collaboration between the public
and private sectors to agree on and upholdglobal standards of ethics and governance,
deliver reassurance on the perceived threats ofdigital disruption, and educate and empower
all in society to thrive amid new possibilities.
If those making the decisions, policies, systems
and programs know what questions to ask—
and answer them with the best interests ofhumanity in mind—then the potential ofgenerative AI won’t be a dry calculation but
a living reality with benefits for all.

About the authors

Ollie O’Donoghue

Duncan Roberts

Senior Director, Cognizant Research

Senior Manager, Cognizant Research

Ollie O’Donoghue leads Cognizant
Research, leveraging over a decadeof experience as an industry analyst
and consultant. His primary focus ison understanding the impact of new
economic and technological trends on
businesses and industries.

Duncan Roberts is a Senior Manager
in Cognizant Research. He joined thecompany in 2019 as a digital strategy andtransformation consultant in industries
ranging from satellite communications to
educational assessment. He has advisedclients on utilizing technology to meetstrategic objectives and discover the artof the possible through innovation.

Throughout his career, Ollie has provided
valuable guidance to C-suite decisionmakers, helping them navigate the best
paths for digital transformation initiatives,changing economic environments andemerging business models. He has
also contributed to refining marketingmessaging and developing go-to-market
strategies for large IT services andsoftware companies.
OliverRory.O’Donoghue@cognizant.com

31

New work, new world

Before Cognizant, Duncan worked forone of the largest publishing houses in
Europe, playing a leading role in the digitalpublishing revolution, helping transformtheir operations end-to-end and launching
new innovative products. He holds a
Master’s in Philosophy and Classics fromthe University of St. Andrews.
Duncan.Roberts@cognizant.com

Acknowledgments
The authors would like to thank the following for theircontributions to this report:
From Cognizant• Lynne LaCascia, Head of Brand andCreative Strategy, Content, Thought
Leadership and Research• Catrinel Bartolomeu, Director of
Storytelling and Content• Mike Turner, VP, Software andPlatform Engineering• Naveen Sharma, Global Practice Head,AIA Practice Leadership• Mary Brandel, editor
From Oxford Economics• Vasilis Douzenis, Lead Economist• Henry Worthington, Director• The entire Oxford Economics teamMethodology
Cognizant partnered with
Oxford Economics to assess andforecast the economic impact ofgenerative AI technology on the
US over the next 10 years. Here is acomprehensive explanation of ourapproach and rationale.
The project was executed through fivemain phases of work that enabled us to develop
assumed inputs for Oxford’s Global Economic
Model. These inputs reflect how we anticipate
the use of generative AI in US businesses will
influence structural drivers of economic activity,
notably total factor productivity (TFP) growth.
To reflect the uncertainty inherent in sucha process, we ran three scenarios to develop
a range of outcomes.
We began by using a US Department ofLabor database that contains detailed
information on the skills and requirements
for approximately 1,000 occupations—a
database that now spans more than 20
years—to classify the more than 18,000
unique tasks that must be performed in theseoccupations into categories reflecting theirmaximum potential for automation throughgenerative AI. This involved the development
and testing of a machine learning model, which
we used to generate a “theoretical maximum
exposure score” for every occupation.

32

New work, new world

To model possible adoption rates of generativeAI, we analyzed precedents from transformativetechnologies such as steam engines, electricity,
automobiles and computers. We also applied aBass Diffusion Model, a framework commonlyused to predict the speed and shape of adoption
for new technologies and products entering themarket. Using this adoption data, we calculated
predicted occupation exposure to generative AI
by mapping the theoretical maximum exposure
scores to these predicted industry adoption rates.
Because generative AI in the workplace
will inevitably have a disruptive effect, it isreasonable to expect transitional costs. To build
them into the model, we turned to the Current
Population Survey of displaced workers
from the US Census Bureau. This enabled us
to understand the proportion of workers thatwill be displaced permanently by generative
AI and also to generate a “friction score” todetermine the degree to which displaced workers
may struggle to regain employment.
These factors, combined with other, moredetailed inputs (noted in the full methodology),
enabled us to estimate generative AI’s TFP
and labor market participation impacts. The
aggregated impacts of generative AI on
productivity and employment were modeled as
shocks applied to Oxford’s Global Economic Modelto estimate the contribution of the technology to
US GDP growth above Oxford’s baseline forecasts.
For the full methodology, click here.

Cognizant Research
At Cognizant Research, we help leaders make sense of today’s volatile socio-economic climate. We bring quality research and insights
rooted in Cognizant’s deep industry and technology expertise, helping leaders make the decisions that fuel their companies’ success.
Visit us at www.cognizant.com/us/en/insights.
Learn more about how we’re working with generative AI and the people who use it to engineer modern businesses and improve everyday life.

About Cognizant
Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes
and transform experiences so they can stay ahead in our fast-changing world. Together, we’re improving everyday life.
See how at www.cognizant.com or @cognizant.

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January 2024 | WF 2064768