HARNESSING
THE VALUE OF
GENERATIVE AI
Top use cases across industries#GetTheFutureYouWant

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Executive
Summary

HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESGenerative artificial intelligence (AI) is rapidly becoming
an integral part of our private and professional lives.
While the technology has been in existence for a numberof years, it has gained widespread consumer interest
only recently and has emerged as a critical strategic
consideration. Our latest industry-focused research
reveals that generative AI is on the boardroom agenda
at 96 percent of organizations surveyed globally. While
generative AI is in its infancy in terms of scaled adoption
and implementation, nearly 60 percent of executives
globally say their leadership is a strong advocate forgenerative AI and only 39 percent are taking a “wait-andwatch” approach to adoption.
Many organizations already see generative AI as a
powerful tool that can accelerate growth, enhancecapabilities, and unlock new opportunities without drastic

restructuring of business models. Globally, about onefifth (21 percent) of executives surveyed say generative
AI will significantly disrupt their industries. Executives inhigh tech and industrial manufacturing – two industries
that have long been shaped by AI technologies and havebeen at the forefront of generative AI – are most likely toagree with this statement.
The potential of generative AI to drive innovation andimprove efficiency and productivity extends to nearly all
functions and has applications across all industries. Usecases are wide-ranging, from creating unique content
and automating and accelerating tasks, to creating
personalized experiences and generating synthetic data.
Our research reveals that generative AI has the greatest
potential within IT, sales and customer service, andmarketing functions. The high tech sector leads the way,

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Executive
Summary

HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

with the greatest share of ongoing generative AI pilots.
Executives in our survey also project efficiencies fromgenerative AI in the next three years in the range of 7–9
percent.

adverse impact of generative AI on the environment.
Nevertheless, the net impact of generative AI on anorganization’s Scope 1, 2, and/or 3 emissions is currently
difficult to forecast.

As with any new technology, generative AI is not without
risk. However, with proper planning and guardrails inplace, there is potential to transform business operations,product and service development, and customer
interaction. Nearly three-quarters of executives in our
survey (74 percent) agree that the benefits of generative
AI outweigh the risks. Given the carbon-intensive natureof training new generative AI models, it will also be
important to weigh environmental considerations. The
good news is that executives in our survey are awareof this dynamic and understand how to mitigate theWe conclude this report with a look at how organizations
can start and/or accelerate their generative AI journeys.
First, they must create a robust generative AI strategicand operational architecture. Organizations must alsoestablish internal and external guidelines around theuse of generative AI, adopt a human-centric approachto scaling the technology, and build user and consumer
trust in the AI system. Given the high carbon emissions
associated with generative AI trainings and queries,
sustainable development and use of the technology
should also be a high priority.

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Introduction

HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESGenerative artificial intelligence (generative AI) is rapidlytransforming the way we interact with technology.
Machines are beginning to mimic creative human thoughtprocesses, synthesizing tailored content with significant
implications for organizations and consumers.
This report is the second in a series of reports wehave created around this topic. Our first report, Why
consumers love generative AI, explored consumer
perceptions of generative AI; consumer use of generative
AI; and how the technology is shaping the future ofcustomer experience.
In this report, we delve into the transformative potentialof generative AI for organizations across industries,
asking how the technology could kick-start theinnovating of organizations; highlighting the functionand industry-specific use cases we believe to have thegreatest potential; and comparing adoption rates acrossindustries.
To gauge executives’ perceptions of generative AI and
adoption of use cases, we conducted a global survey of
1,000 organizations across Australia, Canada, France,
Germany, Italy, Japan, the Netherlands, Norway,
Singapore, Spain, Sweden, the UK, and the US. We
questioned executives from multiple industries, including
automotive, consumer products, retail, financial services,
telecom, energy and utilities, aerospace and defense,
high tech, industrial manufacturing, and pharma andhealthcare. For more details on the survey sample, pleaserefer to the research methodology.

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Introduction

HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESThe report explores five broad themes:

1

Organizations view
generative AI not as
a disruptor but as anaccelerator

2

Organizations see
more gain than pain
in generative AI

3Generative AI
packs the mostpunch for IT, sales,and marketing

4High tech leads
in implementing
generative AI

5

How organizations
can kick-start theirgenerative AI
journeys

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Who should read thisreport and why?
This report offers a comprehensive introduction to the transformative
impact that Generative AI holds for large businesses in industries suchas automotive, consumer products, retail, financial services, telecom,
energy and utilities, aerospace and defense, high tech, industrial
manufacturing, and pharma and healthcare. The report will help
business executives identify use cases that will illustrate the pragmatic
applications of Generative AI in IT, sales, and marketing, for example.
The report leverages a comprehensive analysis of 1,000 industry leaders(ranked Director and above) across 13 countries, each at varying stagesof Generative AI implementation. The report also offers actionable
recommendations for business leaders to kick-start their organizations’generative AI journeys. This report is the second in our series of reports
on Generative AI. Read the first report focusing on consumer reactions toGenerative AI at
https://www.capgemini.com/insights/research-library/creative-and-generative-ai/

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Fig.1
Generative AI applications across multiple formats

Selected generative AI applications

Defining
generative AI
Generative AI has the capability to learn and reapply theproperties and patterns of data for a wide range ofapplications, from creating text, images, and videos in
different styles to generating tailored content. It enables
machines to perform creative tasks previously thought
exclusive to humans. The following table summarizes the
top generative AI applications reported in our research andgives some indicative examples.

Indicative examples

Text

Summarizing and translating into multiple
languages

OpenAI’s GPT-4, Jasper1

Images and video
generation

Analyzing existing images/video to generate newcontent (e.g., video games, VR, animation)

Adobe Fireﬂy,2 Stable
Diﬀusion, Midjourney

Audio

Music generation and remixing, speech synthesis,sound eﬀects, voice conversion, audio
enhancement

Sonix.ai (a cloud-based audio
and video-transcription
solution)3

Chatbots

Chatbots to provide automated customer service
and advice

Google Bard,4 OpenAI’s
ChatGPT

SearchEnhanced search functions using natural
language processing and machine learning

Perplexity AI5

Source: 1. TechTarget, “What is generative AI? Everything you need to know,” March 5, 2023.
2. Adobe, "Adobe unveils Firefly, a family of new creative generative AI," March 21, 2023.
3. Sonix.ai, "Sonix releases the world’s first automated transcription and generative AI summarization tool," December 14, 2022.
4. Cointelegraph, "What is Google’s Bard, and how does it work?" May 10, 2023.
5. Kevin-Indig, "Early attempts at integrating AI in Search," January 10, 2023.

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01

ORGANIZATIONS VIEW GENERATIVEAI NOT AS A DISRUPTOR, BUT AS AN
ACCELERATOR
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Fig.2
Generative AI is a topic for boardroom discussion at nearly all organizations

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENT BY SECTOR

Generative AI is a top
agenda item in boardrooms

96%

Nearly all executives (96 percent) in our survey cite
generative AI as a hot topic of discussion in theirrespective boardrooms (see Figure 2), making it probably
the fastest new technology to garner such high-levelinterest. Pat Geraghty, CEO of GuideWell, a US-based
mutual insurance organization, comments: “Every single
board meeting we’ve had this year has had a standing
agenda item of AI and ChatGPT. We want to make sure we’ve
got our board with us as we’re thinking about where we’regoing.” 1

Average

100%

98%

98%

Financialservices
High tech

97%

96%

Industrial
manufacturing

Pharmaand healthcare

Telecom

95%

94%

Energyand utilities

93%

93%

Aerospaceand defense

Automotive

93%

Consumerproducts
RetailGenerative AI is a topic of discussion in our boardroom
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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Fig.3
The leaders of most organizations are strong advocates of generative AI

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENT BY SECTOR
2%

1%

1%

1%

1%

4%

2%

32%

39%

39%

41%

37%

39%

5%

3%

45%

50%

16%
39%

57%

84%
59%

67%

60%

60%

59%

59%

58%
43%

Average

51%

57%

Aerospace
Telecom
RetailIndustrial
Consumerand defense
manufacturing
products
High tech
Financial
EnergyPharmaAutomotive
servicesand utilities
and healthcare

Top leaders are strong
advocates for generative AI
Among the 96% of organizations that discuss generative
AI in their boardrooms, over half (59 percent) ofexecutives say their leadership are strong advocates forgenerative AI only six months after the technology hit
the mainstream. This rises to 84 percent in the high-tech
sector. Thirty-nine percent of executives say their leaders
are taking a “wait-and-watch” approach to the technology
and only 2 percent of executives globally say their leaders
are not convinced or are divided by the potential ofgenerative AI (see Figure 3).

Our leadership is a strong advocate of generative AI
Our leadership is taking a "wait-and-watch" approach to generative AI
Our leadership is not convinced of / is divided on the potential of generative AI
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 766 organizations that cite generative AI
as a topic of discussion in their respective boardrooms.

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96%

organizations say generative AI
is a topic of discussion in theirboardrooms

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Fig.4
About one-fifth of organizations anticipate significant disruption from generative AI

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENT BY SECTOR
52%

Most organizations do notview generative AI as adisruptor

48%

21%

21%

21%

20%

19%

18%

18%
12%
3%

Average

Industrial
manufacturing
High techPharmaand healthcare

Energyand utilities

Financialservices

Consumerproducts

Aerospaceand defense

Automotive

RetailTelecom

Generative AI can signiﬁcantly disrupt our industry

While twenty-one percent of executives anticipate asignificant disruption in their respective industries fromgenerative AI, 67 percent of executives disagree. Thatis, a majority of the executives do not see generative
AI significantly disrupting their business models.
However, executives within the high-tech and industrial
manufacturing sectors expect significant disruption at
52 percent and 48 percent, respectively (see Figure 4).
While this may be reflective of these executives’ superior
understanding of the technology’s potential, the figures
underline the widespread expectation that generative AI
will boost business overall.

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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Fig.5
Relevance of generative AI platforms for organizations across different domains

Organizations are finding
generative AI platforms
increasingly relevant
Chatbots emerge as the most relevant generative AI
application, with 83 percent of organizations citing it.
Organizations can use generative-AI-driven chatbots
to improve their customer service and also to enableimproved internal knowledge management. Seventy-five
percent of executives say that data applications can beused effectively in their organizations, and 71 percent
believe this to be true of text-generating platforms suchas ChatGPT (see Figure 5).

PERCENTAGE OF ORGANIZATIONS THAT FIND GENERATIVE AI
PLATFORMS RELEVANT TO THEIR BUSINESS
Chatbots (automating customer service and improving
knowledge management: e.g., ChatGPT)
Data (designing, collecting, or summarizing data:e.g., Jasper’s Text Summarizer)

83%
75%

Text (summarizing, automating, or translating content: e.g., ChatGPT)

71%

Search (AI-powered insights: e.g., Bing)

70%
61%

Generating synthetic data (Capgemini's Artiﬁcial Data Ampliﬁer)

54%

ML platforms (applications of machine learning: e.g., Slai)
Code (testing and coding assistant e.g.,GitHub Copilot, converting codefrom one language to another e.g., Codex, Capgemini's A2B Translator)

50%

Images (generating images: e.g., DALL-E)

48%
34%

Audio (summarizing, generating or converting text in audio: e.g., Sonix)
Video (generating or editing videos: e.g., Pictory, Synthesys)
Gaming (generative AI gaming studios or applications: e.g., Ludo AI)

26%
4%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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US financial services organization Morgan Stanley's
vast library of investment strategies, market research,
and analyst insights can be time-consuming andcumbersome for wealth-management advisors tosift through. To address this, Morgan Stanley is using
GPT-4 to power an internal chatbot that provides
instant access to any area of the archive. Jeff McMillan,
Head of Analytics, Data, and Innovation, adds: “Youessentially have [access to] the knowledge of the mostknowledgeable person in wealth management – instantly.
Think of it as having our chief investment strategist, chief
global economist, global equities strategist, and every otheranalyst around the globe on call for every advisor, everyday. We believe that is a transformative capability for ourcompany.” 2

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES“You essentially have [access to] the knowledge of themost knowledgeable person in wealth management– instantly. Think of it as having our chief investment
strategist, chief global economist, global equities
strategist, and every other analyst around the globe
on call for every advisor, every day. We believe that is atransformative capability for our company.”
Jeff McMillan
Head of Analytics,
Data, and Innovation,
Morgan Stanley.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESThe impact ofgenerative AI on
the workforce
Generative AI has the potential totransform work
Our consumer research on generative AI reveals thatmost consumers (70 percent) believe it will makethem more efficient at work and will free them fromroutine tasks to explore more strategic aspects of theirjobs.3 Most executives concur with these consumer
sentiments, with 70 percent agreeing the technology
will allow organizations to widen the scope of theroles of knowledge workers (see Figure 6). Over half
(60 percent) also mentioned that generative AI would
completely revolutionize their way of working.

Fig.6
Most executives agree that generative AI will augment knowledge work

PERCENTAGE OF ORGANIZATIONS THAT AGREE WITH THE STATEMENTS

Generative AI will augment the roles of knowledge
workers and reduce their workloads

70%

As generative AI algorithms begin to provide concepts
and initial designs, employees may shift fromtraditional ideation and creation to review and reﬁnement

69%

Generative AI will completely revolutionize the way we work60%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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60

The rise of generative AI will create new roles%

In our current survey, 69 percent of executives believe generative AI will lead to the emergence of new roles. In
addition to prompt engineers, we may also see new roles such as AI auditors and AI ethicists emerge as theinitiatives scale.

believe that generative AI will completely
revolutionize the way we work
Fig.7

Most executives believe new roles will emerge from generative AI

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT

69%

Generative AI will lead to theemergence of new job roles (e.g., prompt
engineer)

Generative AI will demand upskilling
and training initiatives
As well as opening up new job roles and requirements,
as 68 percent of executives in our survey say will happen,
the integration of generative AI into the workforce will
require a significant investment in upskilling and crossskilling of talent. In April 2023, in response to growing
demand, Coursera, a large-scale US-based open online
course provider, launched multiple new generative AI
training courses, including ChatGPT Teach-Out from the
University of Michigan, which introduces learners to large
language models (LLMs) and chatbots and discusses
the ethical use of generative AI and how the technology
might be harnessed and regulated moving forward.4

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=800 organizations.

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02

ORGANIZATIONS SEE MORE GAIN
THAN PAIN IN GENERATIVE AI
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Fig.8
Most executives across industries say that the benefits of generative AI outweigh potential risks

Most organizations believe
the benefits of generative AI
outweigh the risks
Our survey reveals that the majority of executives (74percent) believe the benefits that generative AI brings
outweigh the associated risks. The executives moststrongly convinced that generative AI is a power forgood work within the high-tech sector (84 percent); evenat the other end of the list, a substantial 69 percent ofexecutives within the energy and utilities and telecom
sectors would bet on generative AI (see Figure 8).

74%believe the benefits of generative AI
outweigh the associated risks

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT BY SECTOR
Average

74%

High tech84%

Aerospace and defense

82%

Pharma and healthcare

80%

Industrial manufacturing

77%

Retail76%

Financial services

74%

Consumer products

70%

Energy and utilities

69%

Telecom

69%

Automotive

66%

The beneﬁts of utilizing generative AI outweigh the associated risks
Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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Fig.9
Most executives say that generative AI will improve products/services and customer service

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE STATEMENTS

Generative AI will allow the design process to be
more eﬃcient and streamlined

78%

Generative AI can enable us to create products and services
that are more accessible and inclusive, serving a wider
range of customers with diverse needs and preferences

76%

Generative AI can enable us to create moreinteractive and engaging experiences for our customers

71%

Generative AI can be used to improve customer
service by providing automated and personalized support

67%

Generative AI can improve internal operations
and enhance facility maintenance65%

Anticipated benefits ofgenerative AI extend to
product design andcustomer experience
Generative AI brings numerous transformative benefits
to organizations, including enhanced decision-making,improved efficiency, personalized experiences, cost
reductions, augmented innovation capacity, risk
management, and predictive analytics. Most executives
in our survey (78 percent) believe that generative AI will
make product and service design more efficient andthat it will help them design more inclusive, accessible
products and services (76 percent). Seven in ten
executives believe generative AI will help them improvethe customer experience (see Figure 9).

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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Fig.10Generative AI implementation is expected to yield operational and customer benefits

Executives are positive but
realistic in their expectationsof generative AI
We asked executives which organizational benefits they
expect to see from generative AI within three years.
Executives expect to see improvements of 7–9 percent
across all industries (see Figure 10). Recent research from
Stanford and MIT on the applications of generative AI
in the workplace found that the productivity of techsupport agents who used conversational scripts improved
as much as 14 percent at one organization,5 suggestingsuch an estimate is realistic, if not conservative.

EXPECTED FUTURE BENEFITS OF GENERATIVE AI IN THREE YEARS FROM TODAY,AVERAGE % PROJECTED INCREASE
Improved customer engagement and satisfaction(i.e., increase in Net Promoter Score)

9%

Increase in operationaleﬃciency (e.g., improved quality)

9%

Increase in sales

Decrease in costs

8%

7%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES• Customer engagement andsatisfaction: Organizations can use generative

AI for personalization; real-time insights; intelligent
customer service; predictive analytics; continuous
improvement; and optimized customer journeys.
These benefits ultimately lead to improved customer
engagement, satisfaction, and loyalty.
KT telecom (South Korea’s leading mobile operator)
has built billion-parameter LLMs trained on the NVIDIA
DGX SuperPOD platform and NeMo framework topower smart speakers and customer call centers. Its
AI voice assistant, GiGA Genie, can control TVs, offer
real-time traffic updates, and complete a range ofother home-assistance tasks when prompted by voicecommands. It has developed advanced conversational

skills in the Korean language using LLMs – machinelearning (ML) algorithms that can recognize,
understand, predict, and generate human speechbased on huge text datasets.6• Operational efficiency: Organizations

are already reporting significant efficiencies fromgenerative AI; German biotech firm Evotec announced
a phase-one clinical trial for a novel anti-cancer
compound it developed with Exscientia, a UK
organization that uses AI for small-molecule-drug
discovery. By using Exscientia’s Centaur Chemist AI
design platform, the organizations identified thedrug candidate in only eight months. For context, theaverage traditional discovery process takes 4–5 years.7

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES• Sales: By using generative AI to produce

personalized marketing, pricing optimization, demand
forecasting, improved customer experience, enhanced
sales support, and data-driven decision-making,organizations can attract more customers, drive
life-long content-driven conversations, and boost
conversions.

20–60

%

reduction in weight achieved throughgenerative design by German-based
Claudius Peters

In an attempt to increase its sales, Italian consumerproducts organization Ferrero worked with branddesigner Ogilvy Italy to customize jars for its popular
Nutella chocolate spread. Data scientists fed a
database of patterns and colors to a generative AI
algorithm, which rapidly produced 7 million distinct
jar designs. These unique jars, branded as Nutella
Unica, were sold all across Italy, reportedly selling out
within a month. The design relied on the brand's highly
recognizable lettering, around which other elements
were customized.8• Costs: By using generative AI to automate

processes, optimize resources, implement predictive
maintenance, optimize the supply chain, mitigate
risks, and improve decision-making, organizations
can achieve cost savings and enhance overall financial
performance.
Germany-based Claudius Peters produces processing
equipment for cement, coal, alumina, and gypsum
plants. Working with technology partner Autodesk, it
used the Scrum project-management framework to
reduce costs and product weights while shortening theengineering process. The generative design produced
components with a remarkable 20–60 percent weight
reduction while meeting performance requirements.
Additionally, the design served as a re-engineering
template for conventional manufacturing, resulting
in a 30 percent lighter final design that lowered
component costs.9

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Fig.11
Organizations are prioritizing dedicated generative AI teams

Four in 10 organizations havealready established teams
and budget for generative AI
In just a few months of the public getting to know
about the technology through the launch of ChatGPT in
November 2022, nearly all (97 percent) of organizations
in our survey have plans for generative AI. Our research
reveals that 40 percent of organizations have establisheddedicated teams and budgets for generative AI, while
another half (49 percent) are contemplating doing thesame within 12 months. Only 8 percent of organizations
are yet to develop a firm strategy for integration, andas little as 3 percent are currently unsure if or how they
will integrate generative AI into their product- or servicedevelopment plans (see Figure 11).

CURRENT LEVEL OF INTEGRATION OF GENERATIVE AI INTO FUTURE
PRODUCT/SERVICE DEVELOPMENT PLANS

8%

We have already established a dedicatedteam and budget for its implementation

3%

40%

49%

We are looking at establishing a dedicatedteam and budget for its implementationin this year
We have not yet developed a concrete
plan for integration
We are currently unsure if or how wewill integrate generative AI into our
product/service development plans

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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Fig.12High tech and retail show the strongest commitment to integrating generative AI

PERCENTAGE OF ORGANIZATIONS THAT HAVE ESTABLISHED A DEDICATEDTEAM AND BUDGET TO INTEGRATE GENERATIVE AI INTO FUTURE
PRODUCT/SERVICE DEVELOPMENT PLANS BY SECTOR
40%

AverageHigh tech

74%

Retail62%

Aerospace and defense

52%

Pharma and healthcare

42%

Financial services

42%
39%

Energy and utilities
Telecom

36%

Automotive
Consumer products
Industrial manufacturing

The majority (74 percent) of executives in the high techsector say they have established dedicated teams andbudgets for generative AI. Over 60 percent of executives
from retail and 52 percent of executives from aerospaceand defense say the same (see Figure 12). Within retail,while only 3 percent of executives believed generative
AI to be disruptive to their industry (refer to Figure 4),62 percent of retail executives say their organization has
established a dedicated team and budget. This suggeststhat while the retail industry does not see this technology
as a disruptor, organizations realize they will lose out ifthey fail to implement it.

30%
23%
19%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations

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40%

of organizations have already establishedteams and budget for generative AI, while
another half (49 percent) are contemplating
doing the same within 12 months

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Governing
generative AI
Centralized funding for generative
AI initiatives is currently the favoredmodel
Of the 40 percent of organizations in our survey (320companies) that have a dedicated budget for generative
AI initiatives, 78 percent source it from their central
budget, 16 percent from their overall AI budget, and 6
percent from their IT/digital department.

Organizations expect to partnerwith developers and IT vendors
The preferred partners for generative AI initiatives aredevelopers (69 percent); IT vendors and consulting firms(66 percent); academic institutions (55 percent); and big
tech (35 percent) (see Figure 13).

Fig.13
Most organizations are working/prefer to work with developers and IT vendors for their generative AI initiatives

PARTNERS THAT ORGANIZATIONS ARE ALREADY WORKING WITH/PREFER
TO WORK WITH TO DRIVE MORE VALUE FOR GENERATIVE AI INITIATIVES
Developers (e.g., OpenAI, Stability AI)

69%

66%

IT vendors/C&SI (consulting and system integrators)

Academic institutions

55%

Big tech (e.g., Microsoft, Google)

Peer companies

35%

22%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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03

GENERATIVE AI PACKS THE MOSTPUNCH FOR IT, SALES, AND
MARKETING
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Fig.1467 percent of executives see the most potential for generative AI in the IT function

The greatest potential for
generative AI lies in the IT
function
Nearly 70 percent of executives see generative AI
bringing the most potential value to IT within its roleas an enabler for driving innovation across functions.
Over half (54 percent) also see generative AI driving
innovation for sales and 48 percent for marketing andcommunications (see Figure 14).

67%

of executives see generative AI bringing
the most potential value to IT within its
role as an enabler for driving innovation
across functions

PERCENTAGE OF ORGANIZATIONS THAT SEE THE MOST POTENTIAL FOR
GENERATIVE AI MODELS TO DRIVE INNOVATION AND CREATE VALUE FOR THEIRORGANIZATION ACROSS BUSINESS FUNCTIONSIT (e.g., driving innovation in otherfunctions, testing and coding assistant)

67%

Sales and customer service(e.g., optimizing support chatbots/self-service)

54%

Marketing and communications(e.g., creating personalized marketing campaigns)

48%

Manufacturing (e.g., 3D modeling)

31%

Product design/research and development(e.g., generating new design, faster drug discovery)

31%
26%

Operations (e.g., optimizing supply chain)
Risk management (e.g., drafting
and reviewing legal and regulatory documents)
Finance (e.g., processing invoices)
Logistics (e.g., optimizing routes)

22%
13%
9%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
Question asked: In which business functions do you see the most potential for generative AI models to drive innovation and create
value for your organization? Please select top three.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.15
Organizations use or plan to use generative AI most frequently in IT

PERCENTAGE OF ORGANIZATIONS WHO USE OR PLAN TO USE GENERATIVE AIACROSS BUSINESS FUNCTIONSIT (e.g., driving innovation in otherfunctions, testing and coding assistant)Sales and customer service(e.g., optimizing support chatbots/self-service)

54%
47%

Marketing and communications(e.g., creating personalized marketing campaigns)

46%
30%

Manufacturing (e.g., 3D modeling)
Product design/research and development(e.g., generating new design, faster drug discovery)

28%
24%

Operations (e.g., optimizing supply chain)
Risk management (e.g., drafting
and reviewing legal and regulatory documents)

21%
13%

Finance (e.g., processing invoices)
Logistics (e.g., optimizing routes)

8%

Organizations mostcommonly use or plan toimplement generative AI
in IT, sales and customer
service, and marketing
Over half (54 percent) of executives say their organization
is using or plans to use generative AI in the IT function,and slightly less than half in sales (47 percent) andmarketing (46 percent) (see Figure 15).
The top business function in which executives say theirorganization is using or plans to use generative AI varies
by industry:• Manufacturing (e.g., 3D modelling)– Automotive (73 percent)– Industrial manufacturing (65 percent)

Source: Capgemini Research Institute, generative AI, Executive Survey, April 2023, N = 800 organizations.
*Question asked: In which business function(s) does your organization use or plan to use generative AI the most? Please select allthat apply

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES• IT (e.g., testing and coding assistant)– High tech (86 percent)– Telecom (71 percent)– Aerospace and defense (69 percent)• Product design/research and development (e.g.,generating new design, faster drug discovery)– Pharma and healthcare (63 percent)• Finance (e.g., processing invoices)– Financial services (72 percent)• Sales (e.g., optimizing sales support chatbots)– Retail (62 percent)– Energy and utilities (52 percent)• Marketing and communications (e.g., creating
personalized marketing campaigns)– Consumer products (58 percent)Capgemini Research Institute 2023

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.16
Generating synthetic data is the top use case in IT

Top use cases by business function
In our survey, we assessed 32 functional use cases for generative AI, asking
executives what their organization’s current stage of implementation was foreach. In the following sections, we highlight the top three use cases for eachfunction based on the percentage of executives saying their organization has
ongoing pilots or proofs of concept (PoCs). We also discuss the value theseuse cases could bring to organizations.

Generative AI can help developers in a
number of ways
Generative AI is transforming the world of IT, delivering increased
productivity, faster development cycles, and improved accuracy and quality.
Our survey reveals that 5 percent of organizations already use it for autogeneration and completion of data tables, 4 percent for chatbots in IT service
delivery, and 2 percent for the generation of synthetic data (see Figure 16).

TOP THREE GENERATIVE AI USE CASES IN IT

Generation of synthetic data

32%
2%
31%

Improving IT service delivery throughchatbots and virtual assistants

4%

Auto-generation andcompletion of data tables

5%

Pilot/POC

28%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=314–384
organizations.

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By using generative AI to generate and populate datatables, users can redirect time and effort towards
strategic thinking. Google’s platform harnesses natural
language processing (NLP) and ML algorithms to do this.
This feature allows users to organize and analyze theirdata quickly, enhancing productivity.11
Generative AI models can also create new programmingcode based on natural language prompts, assist incompleting partially written code, and facilitate codetranslation between different programming languages.
A director at a telecom company comments: “The vast
majority of our developers internally want to use thetechnology because they believe it will allow them to produce
higher-quality code much more quickly.”
Duolingo, a renowned language-learning app, has
adopted GitHub's generative AI tool, GitHub Copilot,
to help its developers write and “ship” (deliver to thecustomer) code with unprecedented speed and accuracy,
significantly reducing development cycles. Moreover,generative AI enables automated testing, ensuringsoftware reliability and robustness while minimizing
bugs and enhancing overall test coverage. Girish
Mathrubootham, CEO of Freshworks, concurs: "We use
ChatGPT to write code. Software development used to takeanywhere from eight to 10 weeks; now it can be done in lessthan a week." 11Generative AI is also breaking through in synthetic data.
By training algorithms with real-world data samples, thegenerative model learns patterns, spots correlations,and recognizes statistical properties. The synthetic dataretains the same insights and correlations as the original,
making it a reliable proxy.12 Self-driving car organizations
such as Cruise and Waymo use AI-generated synthetic
data to train perception systems and prepare vehicles forreal-world scenarios during operations.13

Organizations hope
to optimize their sales
operations and provide
better customer service
through chatbots
According to our survey, 6 percent of organizations
are using generative AI to streamline sales operations;
6 percent to optimize sales-support chatbots; and 4
percent to enhance customer engagement throughgenerative-AI-powered virtual assistants (see Figure17). Mike Hales, product owner of a conversational AI
platform at BT Group explains their interest in generative

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.17
AI: “Scripted conversations have a one-way flow and there is no deviation.
Customers can go off in other directions. We are looking at generative AI tohelp wrap around these conversations and build conversations more quickly andefficiently.”
Generative AI automates tasks such as email generation and conversation
summaries, saving time and effort and improving productivity for sales teams.
This streamlining of operations allows sales teams to focus on high-value
activities, such as building relationships with customers and closing deals.
Additionally, organizations can use generative AI-powered chatbots to provide
faster and more accurate customer service, leading to improved response
times and stronger engagement.
Outreach, a leading sales-execution platform, introduced Smart Email Assist,
which uses generative AI to auto-generate accurate and relevant email
copy based on patterns detected in prior conversations between buyers
and sellers. The organization also plans to add new capabilities to enhanceseller productivity, improve conversion rates, and increase participation
and attainment across teams.14 Salesforce-owned enterprise chat app Slackhas integrated ChatGPT to deliver instant conversation summaries, provide
research tools, draft messages, and find answers in relation to various projects
or topics.15 Amazon is working on infusing generative AI into its shopping app.
Amazon’s intention is to improve the shopping experience by using LLMs tocommunicate more effectively with the customer.16

Using generative AI as a virtual assistant is the top use case in sales and customer service

TOP THREE GENERATIVE AI USE CASES IN SALESAND CUSTOMER SERVICE

Virtual assistants for customer engagement

Optimizing and streamlining sales operations

Optimizing sales support chatbots

Pilot/POC

35%
4%
33%
6%
32%
6%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=272–340
organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES“Scripted conversations have a one-way flow and there
is no deviation. Customers can go off in other directions.
We are looking at generative AI to help wrap around theseconversations and build conversations more quickly andefficiently.”

Mike Hales
Product owner of aconversational AI platformat BT Group

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESGenerative AI could allow marketingand communications organizations toreach their customers more effectively
Organizations are piloting generative AI in marketing and communications
to create and optimize their campaigns and also to improve their customer
targeting (30 percent) (see Figure 18).
Organizations can drive highly personalized marketing initiatives at scale
and marketers can create tailored content, offer personalized productrecommendations, and craft experiences that resonate with their target
audiences. This heightened level of personalization boosts customer
engagement, leading to higher conversion rates. Generative AI enables
efficient processing of vast amounts of real-time customer feedback,uncovering patterns, sentiments, and valuable insights to refine marketing
campaigns and improve overall customer experiences.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.18Personalizing content is the top use case in marketing
Synthetic messages as a percentage of all outbound marketing messages
from large organizations are projected to increase to 30 percent by 2025,
an impressive leap from the less than 2 percent recorded in 2022.17 Jasper, amarketing-focused version of GPT-3, is capable of producing customer-facing
content including blogs, social media posts, web copy, sales emails, and ads.
The organization actively tests its outputs and fine-tunes its GPT-3 models(which can understand and generate natural language) using best outputs
from its customers, resulting in substantial improvements over time. At
VMWare, a cloud-computing organization, writers use Jasper to create original
marketing content, spanning email campaigns, product promotions, andsocial media copy.18 Adobe Firefly, a family of creative generative AI models,enables organizations to elevate their marketing and experience strategies
by generating content in their own brand language and style. This allows
organizations to create cohesive customer experiences and personalizedinteractions at scale.19

TOP THREE GENERATIVE AI USE CASES IN
MARKETING AND COMMUNICATIONSCreating and optimizing
marketing campaigns

Real-time customer feedback analysis

Improving customer
segmentation and targeting

Pilot/POC

30%
2%
30%
2%
30%
3%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023 N=235–263
organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.19Generating design concepts is the top use case in design/R&D

TOP THREE GENERATIVE AI USE CASES IN DESIGN/R&D

Generating recommendations of
new design concepts/conﬁgurations

Optimizing productperformance and functionality

Product testing and validation processes

Pilot/POC

32%
2%
27%
4%
24%
4%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=279–325
organizations.

Generative design is opening up
product R&D
Generative design can rapidly explore a range of design possibilities at the
R&D phase. The models generate a number of variations in a short time span,
including options that could be considered outside the natural range of humancreativity. This is the use case generating most experimental interest amongorganizations we surveyed, at 32 percent, as seen in Figure 19. Organizations
could rely on generative AI to proactively assess design directions of products
based on predefined variables and criteria, conduct design iterations based on
feedback sooner and develop multi-modal product experiences.
In an interesting application of generative AI, NASA developed mission
hardware called “evolved structures,” which will be used in space telescopes
and scanners of the Earth’s atmosphere, among other uses. Ryan McClelland,
Research Engineer at NASA’s Goddard Space Flight Center in Greenbelt,
Maryland, elaborates: “You can perform the design, analysis, and fabrication of a
prototype part, and have it in hand in as little as one week.” He adds: “We foundit actually lowers risk. The parts generated by the algorithm have stress factors
almost ten times lower than parts produced by human design.” 20

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.20
Responding to queries is the top use case in risk management

Generative AI could offer a clearerview of risk
Our survey reveals that organizations are using generative AI to analyze,
generate, extract information from, and review legal and regulatory
documents and enhance risk assessment. Generative AI can then act as a
virtual assistant, supporting risk officers in their roles. The Paladin Group, an
InsurTech digital brokerage, and Dais Technology, an insurance technology
provider, have collaborated to create UnderwriteAI, a generative AI tool withaccess to vast datasets that streamlines the underwriting process, deliveringenhanced speed, efficiency, and accuracy. This specialized tool improves risk
assessment, optimizes product pricing, and generates valuable insights for
brokers and insurers.21 Allen & Overy, an international law firm, has introduced
an AI chatbot named Harvey, built using OpenAI's GPT technology. Harvey
has undergone extensive testing since November 2022, specifically for tasks
such as drafting merger and acquisition documents and client memos. The
chatbot is now available to all of the firm’s approximately 3,500 lawyers. This
move marks the first known use of generative AI within the “Magic Circle” (aprestigious group of London-based law firms).22

TOP THREE GENERATIVE AI USE CASES IN RISK MANAGEMENT

Answering queries by analyzing sensitive
information from legal documents

Improving risk assessment andprediction through real-time data analysis

Drafting and reviewing
legal and regulatory documents

Pilot/POC

26%
3%
24%
6%
22%
4%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=97–1253
organizations.

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9%

improvements expected in operational
efficiencies and customer satisfactionin three years from now due togenerative AI

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.21
Conducting financial reporting is the top use case in finance

TOP THREE GENERATIVE AI USE CASES IN FINANCE

38%

Financial reporting

4%
34%

Fraud detection

Budgeting, cash ﬂow forecasting

Pilot/POC

5%
32%
1%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=71–84 organizations.

Reporting and fraud detection are
top finance use cases
Natural language generation (NLG) is a subset of generative AI that
can translate structured financial data into simulated human speechor written language. This can be helpful for generating commentary
on financial reports or to simplify complex financial concepts. Thirtyeight percent of organizations we surveyed are piloting this use case.
Traders, analysts, fund managers, and others can access reports
generated by NLG from the numerous data feeds conventionally usedby financial-services organizations to inform decisions.23 The WallStreet Journal partnered with Narrativa, a generative AI platformorganization, to create a tool that captures data from application
programming interfaces (APIs) and, using language from existing WallStreet Journal articles, creates drafts to assist reporters and editors,
freeing them up to spend more time on analysis.24 Nevertheless, it
is important to understand that generative AI models such as GPT-4
tend to “hallucinate,” (i.e., “…produce content that is nonsensical or
untruthful in relation to certain sources”). So, while the technology
may help in creating initial drafts, it is critical to ensure the correctness
and accuracy, particularly for corporate reporting.
Fraud detection is the second-most commonly investigated use case(see Figure 21). Generative AI can be used to analyze patterns incredit-card transactions, identifying anomalies or unusual behaviors.
Notably, generative AI synthetic data that resembles fraudulent
transactions can be used to train analytical models.

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Fig.22Using predictive analytics is the top use case in logistics/operations

Supply chains in line for a generative
AI upgrade
Generative AI can help with transportation optimization, warehousemanagement, supply and demand planning, and supply chain
troubleshooting. For instance, Inspecterio, a software as a service (SaaS)
provider for supply chain, introduced a ChatGPT-driven generative AI toolthat will extract data from an organization’s ecosystem of suppliers to provide
corrective and preventive actions to improve supply chain performance.25
Project44, an organization that provides supply chain visibility solutions,launched Movement GPT, a tool that invites users to submit data requests
such as “Show me all of my shipments impacted by weather in northern
Europe” or “Do I have more reliable routing options for my next shipment?”
This then allows them to identify supply chain risks and respond or anticipate
accordingly.26

TOP THREE GENERATIVE AI USE CASES IN LOGISTICS/OPERATIONS

Optimizing supply chain planning andmanagement through predictive analytics

Reducing transportation costs
through route optimization

Improving warehouse management
through automated inventory tracking

Pilot/POC

39%
2%
25%
2%
22%
3%

Implementation

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=149–185
organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.23The power of generative AI unleashed across the value chain

Generative
AI is capableof impacting
the wholeorganization

PRODUCT DIFFERENTIATION AND INNOVATION•••

Transform sketches and descriptions into 3D
product designs
Conduct creative brainstorming (e.g., newideas, variations on existing oﬀerings from
customer data and feedback)
Personalize oﬀerings at scale

SALES AND MARKETING•••

Reﬁne targeted marketing based on
consumer behaviour and preferences
Automate consumer segmentation
Generate tailored content using consumer
proﬁles and community insights

CONSUMER EXPERIENCE••••

Personalize purchase journeys and exclusiveoﬀers based on consumer proﬁlesEnhance customer service with multilingual
chatbots/virtual assistants that addresscomplex inquiries
Create unique customer avatars with
contextual information from all interactions
Create inclusive oﬀerings that cater to diverse
consumers

Generative AI has the potential to be atransformative force across all aspects ofthe value chain, as well as for customer andemployee interactions.
OPERATIONS AND SUPPLY CHAIN••

Augment warehouse operations andinventory management through real-time
analytics
Avoid bottlenecks through advanced
predictive analytics and real-time monitoring

IT DEVELOPMENT•••

Testing and coding assistance for increased
eﬃciency and reduced manual eﬀort
Streamline workﬂows by automating
repetitive tasks
Generate synthetic data

SUPPORT FUNCTIONS••Develop training content for employees
based on role and performance
Enable self-serve and automate support tasks(e.g., HR tickets, ﬁnancial analysis andreporting, review of legal documents)

Source: Capgemini Research Institute analysis.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

04

HIGH TECH LEADS IN
IMPLEMENTING GENERATIVE AI
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Fig.24High tech and retail lead in implementing generative AI initiatives

The high-tech sector leads in
implementing generative AI
initiatives
Nearly 70 percent of executives within the high-tech
sector say they their organizations have ongoing
generative AI pilots, and 18 percent say they haveenabled generative AI capabilities in some locations orfunctions. Nearly half of executives in retail, financial
services, and telecom have ongoing generative AI pilots,and 17 percent of retail executives say they have enabled
generative AI capabilities (see Figure 24).
US quick-service restaurant Wendy’s is working with
Google Cloud on a generative AI solution, called Wendy’s
FreshAI, that will pilot in June 2023. The technology
is transforming the Wendy’s drive-through orderingexperience with Google Cloud’s generative AI and LLMs
that have the ability to discern the billions of possibleorder combinations on the Wendy’s menu.27% OF ORGANIZATIONS WHO AGREE WITH THE STATEMENT ON GENERATIVE AI
MATURITY BY INDUSTRY
53%

AverageHigh Tech
Retail

14%
34%
56%

7%

47%
61%
68%

47%

6%
35%

4%

28%

4%

49%

53%

4%

44%
64%

Energy and Utilities
Industrial manufacturing

17%
37%

47%

Automotive
Aerospace & Defense

18%
48%

Consumer products
Telecom

6%

68%

Pharma and Healthcare
Financial Services

41%

55%

3%
33%

3%

45 %

We have started exploring the potential of generative AI
We have begun working on some pilots of generative AI initiatives
We have enabled generative AI capabilities in some of our locations/functions
Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N=800 organizations; N=29–127organizations
per sector.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESTop generative AI use cases,by industry
In our survey, we assessed 38 industry-specific use cases
for generative AI. We asked executives to specify theirorganization’s current stage of implementation for eachuse case. In Figure 25, we highlight the top two use cases
for each industry based on the percentage of executives
saying their organization has ongoing pilots or PoCs.
We also discuss the value these use cases could bring toorganizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Fig.25Top two generative AI use cases by industry*% of organizations piloting
the use case% of organizations
Implementing the use case

3D modeling to create detailed shapes

41%

4%

Inverse design, composed entirely of new materials to capture speciﬁc physical properties (material science)

36%

0%

Generating scenarios and testing them for safety and performance in a simulated environment

25%

1%

Customizing vehicle features and conﬁgurations to individual customer preferences

24%

3%

Hyper-personalized, multi-sensory consumer experience

42%

0%

Finding niche audiences to survey

30%

0%

Generation of synthetic data

33%

2%

Inverse design, composed of entirely new materials to capture speciﬁc physical properties (material science)

24%

4%

Generation of synthetic customer data

34%

6%

Improved prospect proﬁling and customized product recommendations for account managers

31%

8%

3D modelling to create detailed shapes

58%

20%

Predictive maintenance

58%

10%

Generative design of parts optimized to meet speciﬁc goals and constraints

30%

0%

3D modelling to create detailed shapes

22%

4%

Advanced drug discovery and therapeutics

35%

2%

Generate, predict, and understand biomolecular data

32%

2%

Eﬃcient, self-optimizing real-time customer service chatbots

63%

11%

Industry
AEROSPACE AND DEFENSE
AUTOMOTIVECONSUMER PRODUCTS
ENERGY AND UTILITIESFINANCIAL SERVICESHIGH TECH
INDUSTRIAL MANUFACTURING
PHARMA AND HEALTHCARERETAILTELECOM

Use case

Build creative marketing campaigns and visually appealing advertisements

36%

0%

Call-center analytics

36%

2%

Restoration of old media

30%

0%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations; N=19–99 organizations per sector.
*Top two determined by the percentage of organizations piloting the use case.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESGenerative AI is offering enhanced
design capabilities for aerospaceand defense and the high-tech
sectorForty-one percent of aerospace and defense organizations and58 percent of high-tech organizations in our survey are piloting
generative AI in 3D modelling. By employing generative AI
algorithms, engineers and designers can optimize the design process,streamlining complex geometries and creating innovative structures

for aircraft, spacecraft, and defense systems. This
technology enables the production of lightweight, highly
efficient, and aerodynamically optimized components,
improving performance and reducing cost. Researchers
at Boeing are using generative AI models to explore new
ways to optimize aircraft design.“When humans design astructure, they tend to repeat existing patterns. However,when AI is tasked with generating a design from scratch,it doesn’t necessarily follow the same patterns as humanengineers. Because [AI] can put more complexity into its
electronic brain than a human can, it can optimize over abroader space,” explains Todd Citron, CTO at Boeing.28

High-fidelity 3D models are expensive to make andimplement. Game developers and studios can harness
generative AI to produce them efficiently at speed,
reducing development costs. Nvidia, a US-based
technology organization, recently unveiled newgenerative AI technologies to create exciting virtualworlds via their Omniverse platform.29 Nextech3D.AI,
a generative-AI-powered 3D model supplier for major
e-commerce retailers such as Amazon, P&G, and Kohls,
has launched technology enabling the generation of 3D
materials from text prompts.30

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESThe automotive industry
sees generative AI pilots
revolutionizing design andcustomization processes
In our survey, nearly one-quarter of executives in
the automotive industry say their organization ispiloting generative AI in the design and customization
process. Generative AI enhances autonomous vehicle
development, generating and testing scenarios forsafety and performance, customizing vehicle features tocustomer preferences, optimizing parts design; enabling
predictive maintenance, and streamlining automotive
insurance processes. Audi’s new AI network, FelGAN,
uses a generative adversarial network (GAN, a type ofML framework) to generate new lightweight rim designs
for their cars.31 General Motors is working on developing
an in-car assistant using ChatGPT. This tool will be used
to integrate schedules, predict a need to open garage
doors, and help the drivers understand their cars.32

Consumer products andretail leads use cases incustomer experience
Generative AI technology can transform customer
journeys, making them more efficient, effective, andengaging. Forty-two percent of consumer products
organizations in our survey are piloting generative AI
in the consumer journey to make it more personalized,
and 63 percent of retail organizations say they areusing generative AI to improve customer-service
chatbots. Clothing-service company Stitch Fix alreadyuses AI to make recommendations to customers, andis experimenting with DALL-E 2 to create visualizationsof clothing based on requested customer preferences
for color, fabric, and style.33 With help from Microsoft’s
Azure Open AI Service, CarMax, a US-based automotive
retailer, has streamlined the creation of text summaries
for its car research pages, quickly providing customers
with meaningful content that also boosts search-engine
rankings.34

Energy and utilities andfinancial services use
generative AI to create
synthetic data
A third of executives in energy and utilities and a thirdin financial services say their organizations are piloting
generative AI to produce synthetic data, which canshorten development timelines. Generative AI can learn
the complex relationships in the original datasets andproduce data that more reliably reflects these uniquepatterns. Synthetic data generation can also facilitate
internal data sharing, especially in organizations that
store high-volume, complex, and varied datasets (e.g.,
in energy and utilities or financial services). It canalso safeguard against privacy concerns in regulatedindustries as it stores the relationships and patterns in
the data without the need to store individual-level data.35

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESIn the energy and utilities sectors, generative AI can also be used
to track and predict energy utilization. A pilot project by US-basedenergy supplier Gridmatic and internet service provider EdgeConneX
was launched using a 24/7 carbon-free energy (CFE) approach toboost and track clean energy sourcing and utilization. By forecasting
and matching supply to demand on an hourly basis, Gridmatic's AI has
enabled round-the-clock CFE for one Texas data center.36
Within financial services, generative AI can enable fraud detection,enhance credit scoring, and generate accurate financial forecasts
and scenarios. Bloomberg has announced its fine-tuned generative
finance model BloombergGPT, which is capable of sentiment analysis,
news classification, and other financial tasks at a level comparablewith that achieved by human analysts.37 Swedbank AB trained GANs
using Nvidia graphics processing units (GPUs) as part of its fraud andmoney-laundering prevention strategy.38

Industrial manufacturers are
optimizing parts with generative
AI pilots
Thirty percent of industrial manufacturing executives in our survey
say their organization is piloting generative-optimized, tailored part
design and predictive maintenance that can reduce overhead costs
and expensive downtime. CUPRA, a high-performance Volkswagen
brand, optimizes vehicle-component design and manufacturing
using PTC Creo, which creates and tests 3D part prototypes before
manufacture, significantly reducing time to market for new vehicles
while achieving a 10 percent weight optimization of components,
resulting in a cost reduction of around 15 percent.39

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Pharma and healthcare set toaccelerate R&D, with help fromgenerative AI

Telecom organizations are leading
the charge for generative AI in callcenters

Pharmaceutical and healthcare organizations in our survey
are implementing generative AI for pilots in drug discovery
and therapeutics (35 percent) and generating, predicting,and understanding biomolecular data (32 percent). Bio-techorganizations including Cradle, Basecamp Research, Arzeda,
Biomatter Designs, Cambrium, and Absci are designing new
proteins from scratch using generative AI.40 Additionally, Profluent
Bio, a US-based biotech organization, uses LLMs to design entire
proteins.41

Over a third (36 percent) of telecom executives in our survey say
their organization is piloting generative AI in call-center analytics.
Generative AI can play a pivotal role in the telecom sector by
improving customer-service interactions; optimizing network design
and architectures; restoring old media; and facilitating text-tovideo generation for filmmaking, driving innovation and efficiency.
Orange is exploring a next-generation contact center with Google
Cloud, testing generative AI to transcribe calls, summarize customer
interactions, and suggest follow-up actions.42

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESGenerative
AI’s impact onsustainability
The carbon footprint of building
generative AI is massive
As the race to build high-performance, generative-AIpowered tools and platforms intensifies, a pressing
concern is the significant rise in computing power andsubsequent impact on energy consumption and carbon
emissions. The training of GPT-3, which forms the basis
for ChatGPT, is estimated to have consumed 1,287 MWhof energy and resulted in over 550 tonnes of carbon
emissions. This is around ten times higher than theemissions generated by an average car over its lifetime.43

However, the environmental impact extends beyond training. Serving millions of users with these models further
amplifies the energy demands. Integrating generative AI into search engines such as Microsoft Bing, which handles
about half a billion searches daily, requiring at least four times more computing power per search than in a standalone
product.44 Executives in our survey are aware of this; 78 percent agree that generative AI can have a larger carbon
footprint than traditional IT programs (see Figure 26).

Fig.26
Most organizations understand that generative AI has a larger carbon footprint than traditional IT and the need toscale sustainably

PERCENTAGE OF EXECUTIVES WHO AGREE WITH THE STATEMENTS

Generative AI can have a greater carbon
footprint than traditional IT programs78%

Our organization is conscious of the need to
implement and scale generative AI in asustainable way

77%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.
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Organizations recognize the need to
implement and scale generative AI
sustainably
Reassuringly, as Figure 26 shows, nearly 80 percent oforganizations are conscious of the need to build generative
AI in a sustainable way. Louis DiCesari, Global Head of Data,
Analytics, and AI at Levi Strauss confirms: “As the technology
matures, we should be able to figure out more efficient ways ofaddressing sustainability concerns of organizations related togenerative AI.”Few organizations have a strategy to
mitigate the environmental impact oftraining generative AI models
Only a small minority (8 percent) of organizations globally (i.e.,
only 64 organizations from the sample) in our survey plan toinvest in training generative AI models from scratch. Of these,
about half have taken steps to mitigate their environmental
impact, such as investing in renewable energy and offsetting
carbon emissions through carbon credits. The remaining half
are exploring mitigation measures such as using more energyefficient hardware and optimizing training algorithms.

Generative AI can lead to a smaller carbon footprint
Organizations also have high sustainability expectations of generative AI, including a projected 9 percent reduction
in carbon footprints at organizational level within the next three years. Airbus uses AI algorithms in its generative
design process to develop lighter-weight parts for its aircraft, resulting in lower fuel consumption and, consequently,
lessened environmental impact. The initial design was 45 percent lighter than the traditional part and is projected toreduce Airbus’s annual CO2 emissions by nearly half a million metric tonnes (mt) if rolled out across its A320 fleet.45
Currently, the net impact of generative AI on carbon emissions is unquantifiable, but we can nevertheless make a starton mitigating them (see recommendations).

Generative AI tools are driving conversations around sustainability
Generative AI tools can make corporate social responsibility (CSR) reporting easier for organizations. Using the data fed
to them, they can create an initial draft that the teams can then refine to ensure it is aligned with reporting standards.
C3 AI, for instance, uses generative AI to unify and store disparate ESG data and automate reporting.46 In anotherinteresting application, Ekimetrics, a French data and AI company, launched a chatbot that answers users’ questions
on climate such as: “Are humans responsible for climate change? What are the main principles of the Paris Agreement?
Is climate change irreversible?” To ensure the reliability of answers, the chatbot is allowed to draw its information only
from scientific reports by agencies such as the Intergovernmental Panel on Climate Change (IPCC).47

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HOW ORGANIZATIONS CAN
KICK-START THEIR GENERATIVEAI JOURNEYS
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Fig.27Key considerations for organizations to drive generative AI initiatives

We suggest a few key areas for organizations to focus on
to accelerate their generative AI journeys (see Figure 27)
amid a rapidly evolving application landscape.

Integrate generative AI into
organizational strategy andoperations
Amid strong interest in generative AI and theproliferation of applications, organizations must findanswers to some key questions:

Establish
guidelines aroundusage

Build trust andresponsibility in
the AI systems

Integrate
generative AI into
the organization's
strategy andoperations

DRIVING
GENERATIVEAI• Customer: Our consumer research highlighted

how generative AI is transforming search and online
buying behavior.48 It also details how the technology
could enrich the consumer experience; for example,
a personalized avatar could represent the consumer
over the lifetime of their relationship with the brand.
It could act as a single touchpoint, fully informed

Drive ahuman-centeredapproach toscaling
generative AI

Focus onsustainable
development

Source: Capgemini Research Institute analysis.

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by historical personal data, to guide the consumer
through each purchasing journey. At the same time, it
is important to invest in the initiatives that align withthe values and the mission of the organization so that
consumers can find consistency and connection to thebrand.

• Operations: In our research on AI from morethan three years ago, we found that over half oforganizations had implemented AI technology at
scale.49 Many of these applications could be extended
to generative AI use cases. However, organizations
need to have a clear roadmap prioritizing use cases
across functions. Once the use cases are identified,organizations should take the following steps.
Determine whether they can adapt an existing
pre-trained model or build an original to their own
specifications.

Choosing the right model: If a pre-trained model
is selected, the optimal approach is to use the bestsized model, rather than opting for the model with thelargest number of parameters.
Be aware of data-privacy requirements: Threequarters of organizations from our research identified
a lack of data privacy and protection as a major concern
in realizing the full potential of generative AI. Italy,
Spain, and Canada, among other countries, are looking
into how OpenAI is collecting its training data.50 Whenusing OpenAI or other pre-trained models from themarket, organizations may expose themselves to theseliabilities.
Train or validate the model: In case of building fromscratch, the AI model needs to be trained and, if using
an available pre-trained model, organizations will stillneed to test the model to ensure its reliability.
Monitor and update the model based on feedback:Once the system is implemented, it must be monitored
for potential issues or bias. It is also important togather feedback from the users and update themodel for new market conditions and changed user
preferences or behavior.

• Data readiness: Improved data quality andeffective data management will accelerate model
deployment. For this, organizations must look into
master-data management, build data pipelinesthat automate data flows from different sources
to databases or data warehouses, implement datacleaning techniques such as deduplication and outlier
detection, and practice active data stewardship.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESDrive a human-centeredapproach to scaling
generative AI
High tech and retail industries
lead in implementing generative
AI initiatives

Generative AI tools are intuitive to use, as reflected byconsumer uptake.51 While the technology is expected todeliver productivity gains, there are also concerns aroundgrowing complexity and technical debt.52 “The potential
for increased technical debt and orphan code is always
a concern when delivery can be accelerated,” confirms
Tracy Daniels, CDO at Truist, a US financial-services
organization.53 As the programmer’s job gets easier, CIOs
will need to govern effectively. Overall, organizations
must focus on a human-centered and human-augmented
approach to scaling generative AI initiatives.

Focus on sustainable
development
As seen in Figure 26, organizations are consciousof the need to implement and scale generative AI
sustainably. As of 2021, the information communication
technology (ICT) sector accounted for 1.8–3.9 percent
of global greenhouse gas (GHG) emissions.54 AI and ML
are responsible for only a fraction of these emissions.
However, as LLMs gain prominence, the contributionof these systems to the sectoral footprint will increasesignificantly. The training of GPT-3 was estimated to haveresulted in more than 550 Mt of CO2, and Meta’s Open
Pre-trained Transformer (OPT) had resulted in more than
75 Mt of emissions.55 The open-access BLOOM model,
which is a similar size to GPT-3, has a much lower footprintof 25 Mt of CO2 emissions.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESWhether they choose to build their own or rely on pretrained models, organizations must make a concerted
effort to be more sustainable. Steps to reduce the carbon
footprint of generative AI models include:• Using an optimum model architecture• Exploring model-compression techniques• Using parallel and distributed computing• Early stopping• Increasing batch size
• Using energy-efficient hardware• Using energy-efficient data centers

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Fig.28Organizations are concerned about underlying datasets and possible bias

Build trust and responsibility
in AI systems
Generative AI inherits certain challenges from AI.
Concerns around bias and an inability to explain results
are significant issues cited by the executives in our
survey (see Figure 28). In June 2023, the European
Parliament approved the AI Act, which regulates theuse of AI, moving one step closer to legislation. While
the original proposal for this act did not contain any
references to generative AI, with the advent and growing
popularity of ChatGPT, the EU council amended the act toinclude “general purpose AI,” “foundation models,” and“generative AI.”56 These rules will require the developersof generative AI tools to submit their systems for review
before releasing them commercially57 and to publish what
copyrighted data has been used to train their tools.58

PERCENTAGE OF ORGANIZATIONS THAT SAY THE FOLLOWING ARE CHALLENGESFOR IMPLEMENTING GENERATIVE AI

Lack of clarity on underlying data used to train
generative Al programs

51%

Lack of conﬁdence that the generative Al programs
are fair (inclusive of all population groups)

45%

Inability to explain the results from generative
Al algorithms

37%

Bias in the generative Al models leads to embarrassing
results when used by customers/clients

36%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations. Note: By "embarrassing"results, we mean undesirable or socially unacceptable results.
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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESIn addition to these challenges, there are other risks of which organizations should be aware while
using generative AI models:• Inherited risk – Organizations may not have access to the datasets used to pre-train models. In
such cases, these organizations will inherit the risks associated with these training datasets. These
could be legal risks or reputational damage due to biased output.
• Intellectual property – Datasets that have been trained on copyrighted items could also lead tolegal issues for downstream players.
• Correctness – Generative AI is known to produce “hallucinations,”59 with tools often being
unnervingly confident of their accuracy. In an example from the US, a lawyer relied on ChatGPT to
prepare a brief. The tool shared multiple precedents, assuring the lawyer that the citations werereliable; however, six of these cases turned out to be hallucinations invented by the tool and thelawyer may consequently face sanctions.60

51%

cite the lack of clarity on underlying data
used to train generative Al programs asa challenge• Data leakage – Generative AI models developed from scratch could leak confidential data throughunfiltered prompts.
Figure 29 highlights the key areas that organizations must consider when building trust andresponsibility in AI systems.

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Fig.29Building trust and responsibility in AI systems

Transparent and Explainable AI
With outcomes that can be understood, traced, and audited as appropriate

Robust and safe AI
Including fallback plans when needed

Controllable AI with clear accountability
Sustainable AI

Enabling humans to make more informed choices and keep the last say

CODE
OF ETHICS
FOR AIAI respectful of privacy and data protection
Considering data privacy and security from the design phase, for data usage that issecure and legally compliant with privacy regulations

Developed being mindful of each stakeholder, to beneﬁt the environment and allpresent and future members of our ecosystem, human and non-human alike, and toaddress pressing challenges such as climate change

Fair AI
Produced by diverse teams using sound data for unbiased outcomes and theinclusion of all individuals and population groups

AI with carefully delimited impact
Designed for human beneﬁt, with a clearly deﬁned purpose setting out what
the solution will deliver, to whom.

Capgemini Code of Ethics for AI

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES"As the technology matures, we should be able
to figure out more efficient ways of addressing
sustainability concerns of organizations related togenerative AI."Louis DiCesari
Global Head of Data,
Analytics, and AI at Levi
Strauss

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Fig.30Organizations are concerned about potential bias of underlying datasets

Establish guidelines for theuse of generative AI in theorganization
A few organizations are wary of generative AI tools,
particularly those over which they have no control. In our
research, 10 percent of organizations mentioned thatthey have either banned or are considering banning useof such tools for work (see Figure 30); Samsung, Verizon,
and Northrop Grumman have imposed a blanket ban.61
There are a few reasons for such a step: for example,
concerns around data leakage through the prompts given
to the tools, “hallucination” causing errors that may beoverlooked by the users, and underlying data exhibiting
biases or even having been sourced in unethical ways.

PERCENTAGE OF ORGANIZATIONS WHO AGREE WITH THE
STATEMENTS

We are establishing guidelines and policies on theappropriate use of generative AI tools by employees
to ensure they are used ethically, legally, and inalignment with the organization's values and mission

68%

We are establishing guidelines and criteria
around when to use generative AI vs alternatives

68%

We have banned/are looking at banning the use ofgenerative AI tools for work-related purposes

10%

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 800 organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESThe majority of organizations (68 percent) in our survey are looking at establishing
employee guidelines for generative AI, including training around which data ispermitted to be uploaded to the tools and safe usage, and validating outputs
to eliminate bias. Organizations also need to establish sustainability guidelines,
in particular around the carbon footprint of (highly carbon-intensive, albeit
irregular) LLM training and the general use of generative AI. It is estimated thata single generative AI query can result in 4–5 times higher carbon emissions thana search-engine query.62 With ChatGPT alone clocking 173 million users and 1.8
billion monthly visits in April 2023,63 one can imagine the magnitude of aggregatedemissions. Generative AI may not be the best solution to every problem, andorganizations should provide employees with a framework or checklist to decide
whether to implement it. Finally, in-house development under controlled conditions
could avoid the types of issue that have led to blanket bans.

Regulators should safeguard consumer
interest
The pace of change in generative AI technology is so rapid that regulators are
struggling to catch up. The number of applications using the technology and of
consumers adopting it is growing daily. The US Copyright Office, for instance,
initially granted copyright for a comic book that included AI-generated images.
However, later, the agency reviewed its decision and decided the writing andthe original elements of the book are eligible for copyright but not the images,
which are AI-generated.64 Regulators in a few countries have started looking into
OpenAI;65 below are a few suggestions, as highlighted in our consumer-focusedresearch on generative AI.66• Requesting disclosures• Draft/revisit policies• Establishing accountability• Provide more control to users• Engaging in discussion with developers and academics

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Conclusion
Generative AI has taken the world by storm and isundoubtedly top-of-mind for leadership across industries
globally. The speed with which the technology has
become mainstream is fascinating. Generative AI has
applications across different functions and in different
sectors. As a result, we have found many organizations
experimenting with the various use cases. Nevertheless,organizations are hitting some roadblocks. Insufficient
clarity on the underlying data of pre-trained models,possible biases, and a lack of inclusivity pose legal andreputational risks for organizations, as do “hallucinations”
and the potential for data leakage, even with custombuilt internal models. Organizations must also confront
the carbon-footprint issue. As generative AI adoption
grows, carbon emissions will multiply. Are organizations
prepared to act?
While developers such as Open AI and Google andother players look for workarounds, organizations
must also find answers to key questions, chief among

which is whether pre-trained or custom models shouldtake precedence. Both have pros and cons, but eachrequires a high-level architecture. They must also be
driven by a human-centered approach to deployment,
embedding human oversight and user feedback, andtraining employees. It is also advisable to develop a set of
guidelines around when to use generative AI and how to
use it safely. Building trust in the systems is critical.
As the technology evolves, organizations’ expectations
are bound to change. Can generative AI disrupt
industries? And what will be its impact in the long term?
Will benefits fall short of, meet, or exceed expectations?
How can the challenges around hallucination, dataleakage, sustainability, and legal and reputational issues
be overcome? And what role will regulators play in thisevolving market? It is not yet clear how all these elements
will unfold. What is clear is that generative AI technology
is part of the future and organizations should start toprepare for it.

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Researchmethodology

ORGANIZATIONS BY HEADQUARTERS
Japan
Singapore

We carried out a targeted survey of 1,000 organizations. All of theseorganizations are interested in exploring generative AI. Further, 80
percent have already started exploring the technology. The global
survey took place in April 2023. In addition to the online survey,
we conducted in-depth interviews with 10 industry executives tounderstand the potential of generative AI, the challenges they
foresee, and the capabilities required to deploy the technology. The
study findings reflect the views of the respondents to our online
questionnaire for this research and are aimed at providing directional
guidance. Please contact one of the Capgemini experts listed at theend of the report to discuss specific implications.

5%

Australia

CanadaPharma and healthcare

Germany
6%

Industrial
manufacturing

10%
France

6%

10%

8%
4%

15%

SwedenORGANIZATIONS BY INDUSTRY

15%5%High techAerospaceanddefense5%

10%5% Retail

US
15%

4%

10%
8%

Norway
Spain

6%

UK

Consumerproducts

10%8%Netherlands

Automotive

10%

Energy andutilities

15%
10%
Telecom

Financial services(banking andinsurance)

Italy

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 1,000 organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESORGANIZATIONS BY ANNUAL REVENUE
1%ORGANIZATIONS BY STAGE OF GENERATIVEAI IMPLEMENTATIONHead of AI

5%

5%

RESPONDENTS BY TITLE

20%

13%

COO/CTO/CIO 1%/CTIO/CDO

3%
23%

33%
SVP/EVP

44%

14%

1% Head of Research, AI
Director

25%

43%

24%

US $500 million – $999 million

US $10 billion – $19.99 billion

US $1 billion – $4.99 billion

US $20 billion – $49.99 billion

US $5 billion – $9.99 billion

More than US $50 billion

We are yet to begin
exploring generative AI
capabilities

We have begun working onsome pilots of generative AI
initiatives

We have started
exploring the potentialof generative AI

We have enabled generative
AI capabilities in some of our
locations/functions

Source: Capgemini Research Institute, Generative AI Executive Survey, April 2023, N = 1,000 organizations.

20%
AVP/VP

27%
Senior Director

Source: Capgemini Research Institute, Generative AI Executive
Survey, April 2023, N = 1,000 organizations.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

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65. The Verge, “ChatGPT returns to Italy after ban,” April 28,
2023.
66. Capgemini Research Institute, “Why consumers lovegenerative AI,” June 2023.

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESKey contributors
Robert Engels

Andreas MarkdalenMark Roberts

Mark Oost

Vice President and Head ofGenerative AI Lab,CTO NCE,
Insights and Data, Capgeminirobert.engels@capgemini.com

Global Chief Creative Officer& Vice President frog, Part ofCapgemini Invent
andreas.markdalen@frog.co

Principal Technologist,
Capgemini Engineering
mark.roberts@capgemini.com

Global Offer Leader AI,
Analytics and Data Science,
Insights and Data, Capgeminimark.oost@capgemini.com

Ron Tolido

Steve Jones

Valérie PERHIRIN

Jerome Buvat

CTO, Insights and Data Global
Business Line
ron.tolido@capgemini.com

Executive Vice President, Data
Driven Business, Capgemini
steve.g.jones@capgemini.com

Managing Director, Sustainability
powered by Data & AI,
Capgemini
valerie.perhirin@capgemini.com

Head of the Capgemini
Research Institutejerome.buvat@capgemini.com

Marisa Slatter

Ramya Krishna Puttur

Sumit Cherian

Director, Capgemini ResearchInstitutemarisa.slatter@capgemini.com

Associate Director,
Capgemini Research Instituteramya.puttur@capgemini.com

Senior Manager,
Capgemini Research Institutesumit.cherian@capgemini.com

Manisha Dash

Vaishnavee A

Senior Consultant,
Capgemini Research Institutemanisha.dash@capgemini.com

Consultant,
Capgemini Research Institutevaishnavee.a@capgemini.com

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESThe authors would like to especially thankSubrahmanyam KVJ and Amol Khadikar from theCapgemini Research Institute for their contributions tothis research.
The authors would also like to thank Pascal Brier, Bob
Schwartz , Nicolas Brunel, Kary Bheemaiah, Sofie
Andersson, Marek Sowa, Vincent DE MONTALIVET,
Alex Smith Bingham, Lorna Neville, Vishwanathan
Venkataramanan, Baljeet Yadav, Joyal Dias, Doug Ross,
Annette Klimczak, Myriam CHAVE, Susanna Östberg,
Mollie Mellows, Victoire GRUX, Antara Nandy, Aparajita
Paul, Rupali Chakraborty, Ashwani Kumar, Suparna
Banerjee, and Punam Chavan for their contribution to
the research.

About the Capgemini ResearchInstitute
The Capgemini Research Institute is Capgemini’s
in-house think tank on all things digital. The Institutepublishes research on the impact of digital technologies
on large traditional businesses. The team draws on theworldwide network of Capgemini experts and works
closely with academic and technology partners. The
Institute has dedicated research centers in India,
Singapore, the United Kingdom, and the United States. It
was recently ranked number one in the world for thequality of its research by independent analysts.

Capgemini’s dedicatedGenerative AI Lab to deliverconcrete business impact
Capgemini has been working with clients on AI and generative AI for years to respond to their
specific business needs, particularly in the areas of life sciences, consumer products and retail,and financial services. Through its Generative AI Lab, Capgemini focuses on developing tailoredsolutions for clients, enabling them to leverage the potential of generative AI in a trusted,
secure, and ethical framework to deliver concrete business impact.
The Generative AI Lab is a dedicated team of experts specialized in artificial intelligence fromvarious Capgemini teams around the world who are focused on following the evolutions of thetechnology as well as researching and applying the most relevant use cases in generative AI forclients. The Generative AI Lab creates and delivers assets, education, and awareness to further
enrich Capgemini’s expanding expertise in this field and the value it delivers to its clients.
For more information, visit
http://www.capgemini.com/aifutures

Visit us atwww.capgemini.com/researchinstitute/

Capgemini Research Institute 2023

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESCreatethe impossible
With custom generative
AI for enterprise

According to Gartner, full maturity of generative AI is
expected in 2–5 years, but the market is already shifting tobring generative AI closer to business use cases as 70% of
global organizations currently explore generative AI.
BUT… for business, there are many problems behindpublicly available generative AI large language
models, as they are:
TOO DISJOINTED FROM BUSINESS REALITY
Generative AI tools bring technology but do not answer
questions on how to address new requirements for risk,
privacy, and business controls.
TOO UNIVERSAL AND NOT COMPANY-SPECIFICTOO UNCONTROLLABLE
Without training, you cannot control the quality andsuitability of the output.
TOO RISKY FOR DATA AND PRIVACY
Many available tools have complex licenses or T&C fineprint allowing third parties to read and learn from yourdata, or even forbid commercial use.
TOO DIFFICULT TO ADOPT AND SCALE
Both closed-source tools like ChatGPT and open-source
alternatives do not have a built-in and enterprise-scaleready technology stack.

They are difficult to tune for real business scenarios atscale. They do not have your business knowledge andcultural context.

Capgemini Research Institute 2023

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESTherefore, CXOs need to understand how to leverage
generative AI, in a safe, secure, and controlledmanner, to fit their business reality

Capgemini helps you drive and deliver successfully your generative AI initiatives through four building blocks:

We help you change the DNA of your work throughgenerative AI

SET ORGANIZATIONAL
FOUNDATIONWith Custom Generative AI for Enterprise, we help
CXOs move from a standard risked usage of genericpublic large language models to a tailored and trusted
solution based on the use of their data and theircompany knowledge, with reliable outputs creating
significant tangible outcomes.
We leverage our proven framework to build secure,privacy-protecting, and reliable high-scale generative
solutions.
Custom Generative AI for Enterprise acts as a “onestop-shop” for enterprise-ready data, tools, and use cases
to address business and technology needs and sharea reusable, secure, and company-approved generative
toolkit.

Drive generative AI agenda withprivacy and reliability at core

HOW TO STARTBUILD RIGHT TOOLKITBENEFIT AT SCALE

Dedication workshops
enabling yourstrategy, operating
model and use caseroadmap

Use Capgemini’s
generative AI
framework to build
enterprise-readysolution

Scale across use
cases with dedicatedgenerative AI CXO
templates

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Selected client projects and engagements
We have been working with clients on Generative AI
for the last 3 years to respond to their specific businessneeds, in particular in the areas of Life Sciences,
Consumer Product & Retail and Financial Services. We
have worked for clients across industries (consumer
goods, healthcare/pharma, tech/IT, etc.) in use casessuch as content personalization at scale, knowledge
management and search, customer experience assistants,
as well as a software development assistants.
Amongst recent use cases:• In life sciences: in 2020, we delivered a client project on
resequencing of DNA with the help of Generative AI
and Deep Learning.

Generative AI to propose translations of software from“old” languages to modern languages• We worked on Product Blueprint analysis with
Generative AI for a client managing all documentation
for highly complex engineering products• We delivered the “Intelligent Document Query
Assistant” for a leading insurance company locatedin more than 50 countries, enabling non-technicalexecutives to ask questions in natural language and get
accurate answers much faster• We are currently working with a large consumer
product Group on a Generative AI content creationengine. It automatically generates several options
for Name, Features and Description (automaticallysearch-optimized), making it easier to achieve content
completeness and quality.

• ADA (Artificial Data Amplifier): using Generative AI tocreate synthetic data (Sogeti), used in particular by the
Swedish Social Insurance Agency

For more information, please visit:• We have supported various banks on code conversion
in combination with software migration projects: using

https://www.capgemini.com/solutions/custom-generative-ai-for-enterprise/

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIESFor more information, please contact:
Global contacts

Local contacts

Mark Oost
AI, Analytics, andData Science Global Lead, Capgeminimark.oost@capgemini.com

Robert Engels
Head of Generative AI Lab, Capgeminirobert.engels@capgemini.com

Steve Jones
Trusted Data & AI, Collaborative
Data Ecosystems Leadsteve.g.jones@capgemini.com

Marek Sowa
Head of Intelligent Process
Automation, Capgemini
marek.a.sowa@capgemini.com

NORTHERN &CENTRAL EUROPE
Wei Wei Feng
Gen AI Expert, Insights & Data, Capgemini
weiwei.feng@capgemini.com

NORTH AMERICA
Ajay Mohan
Head of AI, Analytics, and Data Science
Center of Excellence, Insights & Data North
America, Capgemini
ajay.mohan@capgemini.com

SOUTHERN &CENTRAL EUROPE
Tiziano Borrelli
Deputy CTO I&D Insights & Data,
South and Central Europe, Capgemini
tiziano.borrelli@capgemini.com

APAC
Aruna Pattam
Head of AI, Analytics andData Science Center of Excellence,
Insights & Data, Capgemini
aruna.pattam@capgemini.com

Bikash Dash
Program Manager, Data Science &Analytics, GenAI expert, Insights &Data, Capgemini
bikash.ranjan-dash@capgemini.com

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

More Capgemini
Research Institute Publications

Why consumers lovegenerative AI

The power of open minds: how open
innovation offers benefits for all

Mirroring reality: Digital twins
in aerospace and defense

A World In Balance: Why
Sustainability Ambition Is Not
Translating To Action

Future-ready education:
Empowering secondary school
students with digital skills

Data for Net Zero

Capgemini Research Institute 2023

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

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HARNESSING THE VALUE OF GENERATIVE AI: TOP USE CASES ACROSS INDUSTRIES

Notes

Capgemini Research Institute 2023

About Capgemini
Capgemini is a global leader in partnering with companies totransform and manage their business by harnessing the powerof technology. The Group is guided everyday by its purpose of
unleashing human energy through technology for an inclusive andsustainable future. It is a responsible and diverse organization of
360,000 team members in more than 50 countries. With its strong
55-year heritage and deep industry expertise, Capgemini is trusted
by its clients to address the entire breadth of their business needs,
from strategy and design to operations, fueled by the fast evolving
and innovative world of cloud, data, AI, connectivity, software,digital engineering and platforms. The Group reported in 2022
global revenues of €22 billion.

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