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Gener(AI)ting
the future

Quarterly review
N°9 — 2024

Gener(AI)ting the future

Generative AI today is at the threshold of an outburst of creative exuberance. The cover
for this edition of Conversations for Tomorrow represents the myriad possibilities that
arise at the intersection of light and shadow – not too dissimilar to the possibilities that
generative AI creates. At the same time, it also highlights fleeting patterns and dark
spots which one must bear in mind as they move forward. The content and design of
this issue reflect the opportunities, the challenges, and the risks that generative AI is
now throwing up in front of organizations.

2

Capgemini Research Institute

Gener(AI)ting the future

Foreword

At Capgemini, we
help organizations
prepare for tomorrow
by distilling the
unique insights and perspectives of leaders
from global business, academia, the startup
community, and wider society.
Gener(AI)ting the Future
In Conversations for Tomorrow, the
Capgemini Research Institute identifies
the strategic imperatives for the future of
business and the society it serves. In this
ninth edition of the journal, among other
areas, we explore:
• The rapid rise of generative AI (Gen AI)
• The rate at which organizations across
industries are adopting the technology
• The use cases that it enables

But adopters have also had to acknowledge
AI’s significant carbon footprint. Over
one-third of organizations in our research
are already tracking their Gen AI carbon
emissions.
Organizations globally are rapidly
embedding Gen AI across functions, with
a ripple effect for wider society. In this
edition of Conversations for Tomorrow we
focus on this AI-generated future.
We would like to thank all the leaders and
experts who have enriched this edition of
the journal with their insights. By sharing
the perspectives of such a diverse range of
accomplished individuals, we aim to present
a comprehensive overview of Gen AI and its
contribution to generating a new future.
• The CEO of one of the hottest AI startups
• One of the most-respected AI scientists
globally, who is also a board member at
Amazon

• Its impact on sustainability journeys
• How it is likely to change work and the
workforce
• How and why it needs to be regulated
Our annual research into the state of Gen
AI shows that organizations are embracing
generative AI, this is reflected by an uptick in
investment levels. The vast majority (80%) of
organizations in our survey increased their
investment in 2023; 20% maintained their
investment levels; and no organization has
decreased its investment in Gen AI from last
year.
In the past year, every organizational
domain, from sales and marketing to IT,
operations, R&D, finance, and logistics, has
seen an increase in the rate of adoption.
Early adopters are seeing benefits from
improved operational efficiency to
enhanced customer experience. Moreover,
generative AI adoption among employees
is robust in most organizations, with the
majority allowing its use.

• A former member of the European
Parliament who played a key role in the
EU AI Act
• A leading professor at Stanford
• Senior executives from Adobe,
Salesforce, OECD, Telefónica, and Itaú
Unibanco
• Capgemini’s own subject-matter experts
Pulling together such a wide range of views
was an extremely instructive exercise for
us. We hope you enjoy reading this edition
as much as we enjoyed putting it together
for you.

Gener(AI)ting the future

Capgemini Research Institute

3

Contents

4

Capgemini Research Institute

Gener(AI)ting the future

P.14

THE CEO CORNER
P.16
Arthur Mensch
CEO
Mistral AI

P.16
Aiman Ezzat
CEO
Capgemini

Gener(AI)ting the future

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5

P.26

EXECUTIVE CONVERSATIONS WITH…
P.28
Erik Brynjolfsson
Professor, Stanford

P.64
Clara Shih
CEO, Salesforce AI

P.36
Audrey Plonk
Deputy Director, Directorate
for Science, Technology and
Innovation (STI), OECD

P.76
Andrew Ng
CEO, LandingAI

P.44
Dragoş Tudorache
Former MEP, EU AI Act
co-rapporteur, European
Parliament

P.86
Chema Alonso
Chief Digital Officer,
Telefónica

P.54
Scott Belsky
Chief Strategy Officer,
Adobe

P.94
Ricardo Guerra
Chief Information Officer,
Itaú Unibanco

Contents

6

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Gener(AI)ting the future

P.104

PERSPECTIVES FROM CAPGEMINI
GENERATIVE AI FOR
MANAGEMENT
P.106
Elisa Farri
and
Gabriele Rosani
from The Management Lab
by Capgemini Invent

P. 130

INSIGHTS
FROM THE CAPGEMINI
RESEARCH INSTITUTE

GENERATIVE AI: THE
ART OF THE POSSIBLE

P.131
Gen AI in enterprise
The rise of generative AI investments in
organizations

P.116
Robert Engels
Head of Generative AI Lab,
Capgemini

P.138
Gen AI in software engineering
How software engineering is being
shaped by generative AI

OPERATIONAL AI IS
CHANGING HOW WE LOOK
AT DATA
P.124
Anne Laure Thibaud
Executive Vice President, Data,
AI & Analytics Group Offer
Leader, Capgemini
and
Steve Jones
Executive Vice President, Data
Driven Business & Gen AI,
Capgemini

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Executive Summary

Executive
Summary
Generative AI (Gen AI) is making rapid
inroads into organizational structures,
transforming them rapidly from the inside
out. As businesses across industries begin
to implement Gen AI, several key themes
emerge that highlight its potential to impact
organizations, workforce, and society.

Gen AI has the potential
to reimagine the future
of the workforce
Gen AI has potential to unlock human
creativity, allowing employees to focus on
complex strategic tasks. Clara Shih, CEO,
Salesforce AI, comments: “AI will allow
workers to move away from repetitive tasks
to focus on doing what humans do best,
which is building relationships, unlocking
creativity, making connections, and
addressing higher-order problems.”
Scott Belsky, Chief Strategy Officer and
Executive Vice President, Design and
Emerging Products, Adobe, adds: “We are
most creatively confident when we are five
years old. We lose our creative confidence
as we get older because of the skills gap,
exposure to criticism, and just the lack of
access to creative tools. Generative AI is
fundamentally changing this.”

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Andrew Ng, CEO, LandingAI, emphasizes the
boost to productivity: “For many jobs, AI will
only automate or augment 20-30% of tasks.
So, there's a huge productivity boost, but
people are still required for the remaining
70% of the role.”

We are most creatively
confident when we
are five years old.
We lose our creative
confidence as we
get older because
of the skills gap,
exposure to criticism,
and just the lack of
access to creative
tools. Generative
AI is fundamentally
changing this.”
Scott Belsky
Chief Strategy Officer, Adobe

Executive Summary

Whenever a
traditional activity
gets replaced or
augmented with
one based on bits,
it usually brings
significant energy
and environmental
benefits.”
Erik Brynjolfsson
Stanford

This shift is fostering a culture of continuous
learning and adaptability. Erik Brynjolfsson,
Professor at the Stanford Institute for
Human-Centered AI and Director of the
Stanford Digital Economy Lab, discusses the
importance of workforce skill enhancement:
“AI requires significant changes in the
economy to create full impact, particularly
in terms of organization and skills of the
workforce. Identifying which skills are
important, followed by self-learning and
training programs, are required to prepare
the workforce. Secondly, businesses will
need to restructure and adapt to capitalize
on new technologies.”

In the article, Generative AI for
Management, Elisa Farri, Vice-President
at Capgemini Invent, and Gabriele Rosani,
Director at Capgemini Invent, comment:
“AI's capability to collaborate on a cognitive
and emotional level, offering insights and
contributing to complex decision-making
processes, is an area that many managers
have yet to fully realize or integrate into
their strategic thinking.”
They add: “Executives need to cultivate the
ability to adopt the co-thinking mindset.
Whether in individual tasks or team
endeavors, mastering this shift will become
a vital competitive advantage.”

Gen AI also has a significant impact
on managers and leadership.

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Executive Summary

In Gen AI deployment,
ethical considerations
are paramount
Gen AI has emerged as a transformative
innovation. However, its potential
for misuse emphasizes the need for
organizations to uphold strong ethical
standards.
Aiman Ezzat, CEO, Capgemini, stresses
the importance of safe use of Gen
AI: “Organizations should establish
employee guidelines for safe use of Gen
AI and validating outputs to eliminate
bias.”
Arthur Mensch, CEO, Mistral AI,
underscores the importance of managing
AI-driven products within ethical
boundaries: “When an organization is
making an AI-driven product, it must
consider the decisions and outputs the
system will make. These decisions and
these outputs should be constrained to
respect the company's role.”
Audrey Plonk, Deputy Director of
the OECD Directorate for Science,
Technology, and Innovation, is
responsible for the OECD’s digital
policy portfolio. She elaborates on data
concerns: “Data privacy considerations
are a key aspect that organizations and
individuals are exploring extensively.
There is a lot of work to be done to
improve transparency and determine
which sources of data should be used to
train AI models. It is essential to put the
appropriate safeguards in place, including
data protection considerations.”

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Sharing Capgemini perspectives, Anne Laure
Thibaud, Executive Vice President, Data
AI and Analytics Group Offer Leader, and
Steve Jones, Executive Vice President, Datadriven Business and Gen AI, remark: “The
assumption is that Gen AI cannot be trusted
in the same way as a human employee and
given the opportunity, will act outside its
boundaries. Organizations are compelled to
build all the information about their culture,
mission, and guardrails into the AI they use
to retain control of it.”

Executive Summary

The path to responsible
AI
To secure the future of AI, comprehensive
safeguards and collaborative efforts are
essential. Unified actions from policymakers
and organizations are crucial to ensure the
responsible use of Gen AI.
Dragos Tudorache, former member of the
European Parliament and Rapporteur on the
EU AI act, says: “Most companies working
with AI already had general principles or
codes of conduct or self-regulation in place.
There were guidelines outlined by UNESCO,
OECD, and even by the European Parliament.
But we realized these measures were
insufficient to mitigate the very real risks,
such as discrimination bias.”

Organizations should
establish employee
guidelines for safe
use of Gen AI and
validating outputs to
eliminate bias.”
Aiman Ezzat
CEO, Capgemini

On the need to draft the EU AI Act, he adds:
“We needed to put stronger safeguards in
place that command respect and, ultimately,
help society to trust in the interaction with
this technology, hence the decision to
formulate the policy.”

Ricardo Guerra, CIO, Itaú Unibanco,
highlights the responsibility of
organizations to use AI ethically:
“Organizations have to take a lot of the
responsibility for use and governance of AI
and other technologies. But governments
must still stay informed and try to
implement supportive regulation.”

Steering towards a
sustainable future with
Gen AI
Gen AI will play a pivotal role in addressing
climate change. Additionally, businesses
must adopt sustainable practices that align
with environmental goals.
On climate engineering, Andrew Ng
elaborates: “Given the world's collective
inability to move CO2 emissions in the way
we know it needs to, I think it is past time
to take climate engineering more seriously.
I think AI, specifically large AI foundation
models of climate, have a large role to play
in that.”
Ricardo Guerra talks about sustainable
data centers: “We're learning when to
use different solutions and emphasize
investing in sustainable data centers and
green technologies. We're also closely
monitoring the market, prioritizing
providers that offer green solutions.”
Erik Brynjolfsson says: “Whenever a
traditional activity gets replaced or
augmented with one based on bits, it
usually brings significant energy and
environmental benefits.”

Gener(AI)ting the future

Capgemini Research Institute

11

Executive Summary

The rise of open-source
and small language
models
Small language models (SLMs) are highly
cost-effective, resource-efficient, and
have minimal environmental impact.
Open-source models promote innovation,
enhance collaboration, and ensure greater
transparency in development and usage.
Aiman Ezzat says: “There are clear
advantages to both open and closed
approaches. Openness boosts innovation
and drives collaboration. Open models also

allow everyone to scrutinize the model for
potential sources of bias, demystifying the
‘black box’ nature of AI models.”
Arthur Mensch comments: “Smaller models
also mean the applications are less costly
to run and, more importantly, if you have
a model that is 100 times smaller, you can
call it 100 times more for the same cost,
bringing a little more intelligence to your
application with each call.”
Clara Shih adds: “The future of AI will be a
combination of both large and small models
because of climate impact, as well as for cost
and performance reasons.”

When an organization
is making an AI-driven
product, it must
consider the decisions
and outputs the system
will make. These
decisions and these
outputs should be
constrained to respect
the company's role.”
Arthur Mensch
CEO, Mistral AI

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Executive Summary

The future of AI will
be a combination of
both large and small
models because of
climate impact, as
well as for cost and
performance reasons."
Clara Shih
CEO, Salesforce AI

Getting Gen AI right
requires sound
technical strategy and a
culture of innovation
Effective Gen AI adoption requires a wellcoordinated strategic approach with a
strong technical foundation, support from
leadership, and an organizational culture of
innovation.
Chema Alonso, Chief Digital Officer,
Telefónica, suggests: “You need to have a
robust technical strategy based on cloud
and sound data, and the rest will fall into
place. Secondly, you need to have strong
support from top management. Finally,
you need sufficient budget. Once you have
that, you need to make sure that your whole
organization is very well trained on Gen AI –
what can and cannot be done.”

Ricardo Guerra says: “Adopting Gen AI
requires a culture of innovation. With Gen
AI, we need to engage the business and
design teams actively, as they must identify
opportunities beyond mere tech adoption.”

Innovative startups
point to the future of
Gen AI
From content creation to multi-agent
systems, alternative computing, and hybrid
AI, emerging tech startups are pushing the
boundaries of the next generation of AI
applications.
Synthesia uses AI to create customizable
video content featuring realistic avatars,
allowing businesses to generate engaging
video presentations. Soundraw offers
an AI-powered platform for generating
original music without the risk of copyright
infringement.
In the realm of alternative computing,
Mythic develops analog chips for faster,
more efficient AI tasks such as matrix
multiplications, while Groq creates AIoptimized language processing units (LPUs)
designed for running LLMs.
Liquid AI is pioneering the development of
highly efficient, task-specific models using
liquid neural networks, with applications
such as drone navigation, showcasing Gen
AI’s wide range of possibilities.

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13

The CEO Corner

Arthur Mensch
CEO
Mistral AI

The CEO
Corner

in discussion with

Aiman Ezzat
CEO
Capgemini

14

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Gener(AI)ting the future

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15

The CEO Corner

Arthur Mensch
CEO, Mistral AI

Aiman Ezzat
CEO, Capgemini

Arthur Mensch is a French entrepreneur and

With more than 20 years’ experience

scientist.

at Capgemini, Aiman Ezzat has a deep

In 2023, Arthur Mensch, along with Guillaume
Lample and Timothée Lacroix, founded Mistral
AI with the mission of making generative AI
ubiquitous and pioneering a new approach to AI

knowledge of the Group’s main businesses.
He has worked in many countries, notably
the UK and the US, where he lived for more
than 15 years.

- one that is more open, portable, independent,

Aiman was appointed CEO in May 2020.

and accessible to all.

Prior to that, from 2018 to 2020, he served

After more than 10 years of academic work
focused on the possibilities of machine learning
in the field of brain imaging and on optimization
of machine learning, he joined DeepMind Paris in
2020 as a researcher, where he spent three years
and played a key role in the development and
deployment of flagship projects in generative AI.

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Gener(AI)ting the future

as the Group’s COO and, from 2012 to
2018, as CFO. Aiman is also on the Board
of Directors of Air Liquide and is a member
of the Business Council and the European
Round Table (ERT) for Industry.

The CEO Corner

What inspired you to form a new player [in
Mistral] in the generative AI (Gen AI) space –
and why in Europe?
— Arthur: My co-founders and I have been working in the
Gen AI space for over 10 years, previously in large US-based
organizations. When development accelerated at end-2022,
it gave us an opportunity to create some very strong models
in a short period of time. We secured funding, assembled
a dedicated team and the GPUs [graphic processing units]
required to train the LLMs [large language models], and
were ready to go.
Why Europe? Europe is a great place to start a company. The
education systems in France, Poland, or the UK, for example,
are great for training AI scientists. We brought in recent
PhDs from Paris; we were able to get the most important
thing to get started – the team. As the only player in Europe
in the field of conventional language models, we had some
strong geographical business opportunities.

We use both an open-source
model and a portable platform
for model deployment."
Arthur Mensch

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The CEO Corner

What do you see as the advantages of the open-source gen
AI model?
— Arthur: We use both an open-source model and a portable platform for
model deployment. Even our commercial models are licensed. This allows
users to customize the models to their needs.
It offers portability and comfort. With a model that you can deploy on any
platform, on a private cloud or on-premise or on dedicated instances on
the cloud, you can use the technology where your data is. So, this adapts to
the data-governance constraints of the enterprises, and our customers very
much appreciate this flexibility.
— Aiman: There are clear advantages to both open and closed approaches.
Openness boosts innovation and drives collaboration. Open models also
allow everyone to scrutinize the model for potential sources of bias,
demystifying the “black box” nature of AI models. There are also challenges.
Customizing open models for a particular industry or organization is tricky,
but using open models out of the box can lead to suboptimal performance.
Fine-tuning any foundation model, open-source or proprietary, is a timeconsuming, resource-intensive process that requires significant financial
investment. Hence, it is important for enterprises to assess ROI carefully
before pushing out the Gen AI boat.

"Open models also allow
everyone to scrutinize
the model for potential
sources of bias."
Aiman Ezzat

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The CEO Corner

Do you see organizations using a generalized Gen AI model going
forward or many different specialized models?
— Arthur: We see the field moving in these two directions simultaneously. A strong
generalized model gives a good platform for testing solutions. But this can be a
slow and costly process, offering poor ROI for specific tasks. You want your LLM
to offer an intelligent, dynamic solution for a specific issue, whether that’s parsing
the logs of an IT system or parsing the conversation between a customer and a
customer agent.
From a scientific point of view, smaller models
can solve specific issues, but they must be
finely tuned. We want to bring solutions to
market that develop the smallest possible
model to solve a specific defined task, which
will allow for low-latency applications.
Smaller models also mean the applications
are less costly to run and, more importantly,
if you have a model that is 100 times smaller,
you can call it 100 times more for the same
cost, bringing a little more intelligence to
your application with each call. We call this
“compressed knowledge”. We specialize
models in order to make differentiated
applications that go fast, that call LLM often
and that are cost-controlled.

If you have a model
that is 100 times
smaller, you can call it
100 times more for the
same cost, bringing a
little more intelligence
to your application
with each call."
Arthur Mensch

— Aiman: There’s a very clear market for both generalized and specialized models.
A generalized model can serve those use cases that don’t require extensive
customization. These are “low-hanging fruit” that rapidly demonstrate the power of
Gen AI.
Developing and training specialized models for some basic use cases might even
be counterproductive in terms of cost and sustainability. That said, there are use
cases that benefit from specialized models, for instance, in terms of performance
characteristics or in detecting and responding to specific nuances of the industry
or use case. Any use case that requires high performance or deep domain
expertise will likely continue to go down the path of specialized models. At the
same time, specialized models potentially require significant resources in terms
of maintenance and regular updates, so organizations might prefer a generalized
model for use cases with less stringent requirements. I see a future where both
types of models coexist harmoniously.

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The CEO Corner

Any use case that
requires high
performance or deep
domain expertise
will likely continue to
go down the path of
specialized models."
Aiman Ezzat

What are the most innovative use cases that you are
seeing in Gen AI?
— Arthur: In financial services, for instance, Mistral has built models that
extract information from financial reports and summarize it for bankers to
analyze. This harnesses the power of generative AI to process a large amount
of text and detect weak signals, which is very much the core business of
banks. The other successful deployment is in customer services.
— Aiman: We have been working on several innovative cases using Gen
AI across industries. In life sciences for instance, we have developed with
generative AI a solution to design new drug molecules. This method
significantly boosts the process of generating new structures, offering
researchers a potent tool for designing molecules aimed at specific
biological targets. It illustrates AI's transformative potential in accelerating
and refining drug discovery, particularly in the preliminary phases.

20

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The CEO Corner

Given the energy required to create and train the large
models, what are the sustainability implications for Gen
AI?
— Arthur: Most of the compute and energy resources required to run these
systems are used at inference time rather than at training time. So you train
for a couple of months, and when the models are deployed on many, many
GPUs, then the large energy consumption is more linked to the usage than
to the training itself. There are trade-offs between the amount you spend
on training and the compression that you can achieve. If you invest more in
training, you can make smaller models, achieving the same performance as a
larger model with less compute. These smaller models consume less energy
to deploy at inference time.
At Mistral, we focus on
compressing knowledge and
making models that are smaller
than those the competition
produces. Limiting carbon
emissions is a cause that is
very dear to our heart and the
reason why we deployed our
solutions in Europe. In Sweden,
in particular, renewables
compose a high proportion of
energy consumption.

Most of the compute and
energy resources required to
run these systems are used
at inference time, rather than
at training time."
Arthur Mensch

— Aiman: Our research shows that more than three-quarters of organizations are
conscious of environmental concerns around Gen AI. As a leader in the eco-digital
revolution, we at Capgemini recognize the need to weigh the immense potential of Gen
AI against its cost to the planet and society. We are committed to taking a “sustainable
by design” approach to developing Gen AI solutions that harness cutting-edge data, AI,
and climate tech to maximize business outcomes in a sustainable manner.
Mitigation strategies include optimizing the amount of data required to train the
models, working on smaller, task-specific energy-efficient models that employ more
efficient training and operating algorithms, and powering the AI infrastructure with
renewable energy as well as using more energy-efficient datacenters. We also promote
transparency in AI development and operation by monitoring and disclosing the energy
consumption and carbon footprints of Gen AI models. Our Gen AI lifecycle analysis tools
help organizations to mitigate environmental impact.

Gener(AI)ting the future

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21

The CEO Corner

How should large organizations address ethical considerations and
potential bias in deployment of AI models?
— Arthur: When an organization is
making an AI-driven product, it has to
consider the decisions and outputs the
system will make. So, these decisions
and these outputs should be constrained
to respect the company's role. What it
means is that before deployment of a
new AI product, the first thing to think
about is how do you evaluate success.
How do you ensure that the model is
behaving as it should and not producing
unwanted outputs? And is it able to
provide a nuanced but unbiased answer
to complex questions?
Owing to our open approach, the
customer can make their own editorial
choices from these evaluations.
— Aiman: Large organizations should be
conscious of a variety of risks: Inherited
risk, intellectual property, correctness,
data leakage, and user privacy.
Organizations should establish employee
guidelines for the safe use of Gen AI
and validating outputs to eliminate
bias. At Capgemini, we have applied a
governance model to ensure this.

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Gener(AI)ting the future

"The rare
talent that we
recommend
every
organization
look for is
the software
engineer who
can also do data
science."
Arthur Mensch

The CEO Corner

How are you bridging the AI talent demand-supply gap?
— Arthur: It has been a challenge to get the best AI scientists. We
recommend hiring very strong data scientists who can undertake software
development. Since we are making the tools and the foundation for the
model itself, training the model is not a necessity within the enterprise
setting. To make the most interesting products, clients must understand
how to use the platform.
So, the capacity for doing this is really adjacent to what we used to call
data science a decade ago. It's the ability to run experiments, to evaluate
certain systems, to see what is failing, and to see how to try and improve it.
This scientific mindset, running experiments on a computer and measuring
success, which is really the data scientist's job. The changes with the data
scientist's job today is that the software requirement is stronger because,
if you want to make an interesting application, you also need to dive deep
into the way you assemble the software, connect it to the LLM, the LLM to
the database, and an LLM to tools. Having a system mindset is necessary to
create successful applications. The rare talent that we recommend every
organization look for is the software engineer who can also do data science.
— Aiman: We are investing over €2 billion over three years in Gen AI and
have already trained over 120,000 team members on generative AI tools
thanks to our Gen AI Campus. We have also launched a dedicated platform
to industrialize our custom generative AI projects. We will also focus on
obtaining certifications and building centers of excellence, as well as specific
go-to-market skills. Ultimately, Gen AI training will be a key requirement in
all of our development and training curricula.

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23

The CEO Corner

How do you see gen AI driving transformation in large
organizations?
— Arthur: The first step is to take a model – a Mistral model, for instance
– and connect it to the enterprise context. The enterprise context is
located across different databases or SaaS [Software-as-a-Service]
systems. You can then generate assistants with access to the enterprise
context, to help every employee navigate the enterprise processes and
organization. That's typically what our customers do first. They create a
knowledge management tool or general assistant for employees.
— Aiman: Driving transformation with generative AI goes beyond the
technology. Success depends on a broad strategic vision that covers
everything from applying it to the right use cases, potentially adapting
internal processes, to optimizing customer-facing operations. In addition,
the value of generative AI depends on two key foundations: the data and
the human elements. Leaders need to have the right data foundations
in place to ensure they are realising the full potential of Gen AI. Equally
important is training employees to not only use AI effectively but also to
trust it, which is key to adoption.

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Gener(AI)ting the future

The CEO Corner

Arthur Mensch
CEO, Mistral AI

Aiman Ezzat
CEO, Capgemini

"The first step
is to take a
model – a
Mistral model,
for instance
– and connect
it to the
enterprise
context."

"There’s a very
clear market
for both
generalized
and specialized
models."

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Executive Conversations

Executive
conversations
with…
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Executive Conversations

STANFORD

SALESFORCE AI

Erik Brynjolfsson

Clara Shih

Professor

CEO

h p.28

h p.64

OECD

LANDINGAI

Audrey Plonk

Andrew Ng

Deputy Director, Directorate for
Science, Technology and Innovation (STI)

CEO

h p.76

h p.36

EUROPEAN PARLIAMENT

TELEFÓNICA

Dragoş Tudorache

Chema Alonso

Former MEP, EU AI Act co-rapporteur

Chief Digital Officer

h p.44

h p.86

ADOBE

ITAÚ UNIBANCO

Scott Belsky

Ricardo Guerra

Chief Strategy Officer

Chief Information Officer

h p.54

h p.94

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Executive Conversations

ERIK
BRYNJOLFSSON

Professor at the Stanford Institute
for Human-Centered AI, and
Director of the Stanford Digital
Economy Lab

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GENERATING
GROWTH
THROUGH AI
Erik Brynjolfsson is the Jerry Yang
and Akiko Yamazaki Professor
and Senior Fellow at the Stanford
Institute for Human-Centered AI
(HAI), and Director of the Stanford
Digital Economy Lab. He is also the
Ralph Landau Senior Fellow at the
Stanford Institute for Economic
Policy Research (SIEPR), Research
Associate at the National Bureau
of Economic Research (NBER), and

the Co-founder of Workhelix. One of the most
cited authors on the economics of information,
he was among the first researchers to measure
the productivity contributions of IT and the
complementary role of organizational capital and
other intangibles. He is the author of nine books,
including the bestseller The Second Machine
Age: Work, Progress, and Prosperity in a Time
of Brilliant Technologies (2014) with co-author
Andrew McAfee, and Machine, Platform, Crowd:
Harnessing Our Digital Future (2017).

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Executive Conversations

"AI – IN PARTICULAR GENERATIVE
AI – IS THE ELECTRICITY OF OUR
ERA, INCREASINGLY UBIQUITOUS
AND SPAWNING COUNTLESS
COMPLEMENTARY INNOVATIONS."
How is generative AI transformational?
The biggest driver of productivity growth for
businesses and the economy as a whole, is what
economists call “general-purpose technologies”
or GPT, the same initialism AI researchers now use
for “generative pre-trained transformers.” AI – in
particular generative AI – is the electricity of our
era, increasingly ubiquitous and spawning countless
complementary innovations.

Are we ready to harness the full benefits
of generative AI?
Unlike some earlier technologies, AI requires
significant changes in the economy to realize its
full impact, particularly in terms of organization
and workforce skills. To prepare the workforce, it
is necessary to identify which skills are important,
followed by self-learning and training programs.
Businesses will need to restructure and adapt to
capitalize on new technologies.

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Gener(AI)ting the future

Erik Brynjolfsson
Professor at the Stanford
Institute for HumanCentered AI, and
Director of the Stanford
Digital Economy Lab

Executive Conversations

As a global economy, we need to become more dynamic, making it easier
for people to transition to new work models, new kinds of jobs, and even
new geographies. The government can play a role by making regulatory
regimes more sympathetic and flexible.

How should organizations adapt to take full advantage
of generative AI?
The value of generative AI comes largely from the insights and
knowledge of the workforce, which it can capture and distribute. So,
it's important to reward the people who are creating those insights in
the first place, requiring new metrics and recognition of where value is
created. It is also going to require real flexibility and adaptability of the
workforce.
Insight generation, in turn, depends on the quality of data generated.
Data is the lifeblood of machine learning (ML), and proprietary data gives
a competitive advantage to organizations. This kind of advantage will
come from the workforce, operations, better data capture, and making
that data available to ML systems. Not all companies have done a good
job of capturing and curating data, but the ones who have will find that
they are sitting on a goldmine. Companies with better data are going to
be the biggest winners in this paradigm.

Should AI be used to augment or
replace human efforts?

Not all companies
have done a good
job of capturing
and curating data,
but the ones who
have will find that
they are sitting on a
goldmine."

I'm not opposed to automation, but too
many managers overemphasize automation.
While AI is increasingly powerful, in
most cases it’s more effective to keep
humans in charge rather than expect
complete automation of new tasks. Most
organizations need to strike a better
balance.

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Executive Conversations

Furthermore, replacing a human with a
machine tends to concentrate wealth
and power among capital owners. In
contrast, if a technology augments
human resources, it increases the value
of those human contributions, which
leads to a more equitably distributed
prosperity. This will result not only in a
more inclusive kind of society, but also
one with more total value creation.

THE COLLABORATION REVOLUTION

What role do you see for
collaboration?
Collaboration is key. Humans and
machines need to work together, but
humans working together create the
most value. We should concentrate
on building a teamwork ethic through
projects and education programs.
Businesses that create structures
where people can collaborate and
share information effectively are really
powerful.
The work-from-home revolution has
given rise to a whole set of tools, such
as Slack and Teams, which enable
collaboration across widely distributed
geographies. One can draw together
the best people for a particular question
or a project, regardless of location.
Ultimately, we are creating a world where
billions of human brains can connect and
share information simultaneously. That
makes me very optimistic for the future
in terms of innovation and progress.

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"Humans and
machines need to
work together, but
humans working
together create
the most value."

Executive Conversations

TECH FOR SUSTAINABILITY

Ultimately, we are
creating a world where
billions of human
brains can connect
and share information
simultaneously.
That makes me
very optimistic for
the future in terms
of innovation and
progress."
How do you see organizations
balancing their goals of
sustainable growth and
technological advancements?
One of the exciting benefits of digitalization
is its contribution to sustainability. The basic
economic fact is that bits are much cheaper
to transmit, share, and work with than
atoms. So, whenever a traditional activity is
replaced or augmented with one based on
bits, it usually brings significant energy and
environmental benefits. For instance, while
data centers may appear to involve heavy
energy use, that must be weighed against
the alternative of the traditional commute
to the office, or the shipping of products
to homes.

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Executive Conversations

Digitalization improves the efficiency of existing operations. It optimizes
truck routes for better organized delivery systems, designs more
effective aircraft and locomotive engines, builds heating and cooling
systems for more energy-efficient buildings, and more. These are all
opportunities to use digitalization to improve sustainability and lighten
our impact on the earth.
The average US citizen now has a smaller carbon footprint than 50 years
ago. Our lifestyle is more efficient. This can largely be attributed to the
digital revolution.

Whenever a traditional activity is
replaced or augmented with one based
on bits, it usually brings significant
energy and environmental benefits."

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Executive Conversations

Erik Brynjolfsson
Professor at the Stanford Institute
for Human-Centered AI, and
Director of the Stanford Digital
Economy Lab

"The average US citizen now has
a smaller carbon footprint than
50 years ago. Our lifestyle is
more efficient. This can largely
be attributed to the digital
revolution."

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Executive Conversations

AUDREY PLONK

Deputy Director, Directorate
for Science, Technology and
Innovation (STI),
OECD

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TOWARDS AN
EQUITABLE
GLOBAL DIGITAL
TRANSFORMATION
Audrey Plonk is responsible for the
OECD’s portfolio of work on digital policy,
which includes data governance and data
flows, artificial intelligence and emerging
technologies, security and safety online, and
connectivity and infrastructure. She plays
a leading role in overseeing and advancing
evidence-based policy analysis on the drivers,
opportunities, and challenges of digital
transformation in collaboration with policy
communities and stakeholders. She also
supports and represents the OECD in related
international initiatives.

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Executive Conversations

THE ROLE OF GENERATIVE AI

What role do you see for generative AI in the
economy?
Venture capital (VC) investments in generative AI start-ups
have boomed since 2022. In the first half of 2023 alone, the
estimated global VC investment in generative AI was $12
billion.1 That is a big economic shift.
The rapid development of generative AI has significant
implications for the economy. This technology is already
being used to create individualized content at scale,
automate tasks, and improve productivity in key sectors,
including education, healthcare, arts, and software
development. At the same time, it has put a strain on
resources, increasing demand for high-end computing
resources to build and train models.

Audrey Plonk
Deputy Director, Directorate
for Science, Technology and
Innovation (STI),
OECD

Generative AI also has the potential to change labor
markets. We are still working out how best to use it. It’s a
supplement to human experience and knowledge, not a
replacement thereof. Practical policy solutions, including
upskilling and reskilling, will be important to leverage
the benefits of this technology while
mitigating its risks. Despite some initial
backlash, I see generative AI becoming
part of school and university curricula.
Our report on Initial policy considerations
for generative artificial intelligence covers
some of the transformative impacts
of generative AI across sectors and
includes recommendations to support
policymakers in addressing them.2

In the first half of 2023 alone, the
estimated global VC investment
in generative AI was $12 billion.
That is a big economic shift."

1.

OECD.AI Policy Observatory (2024), using data from Preqin. Available at: www.oecd.ai/en/
data?selectedArea=investments-in-ai-and-data.

2.

Lorenz, P., K. Perset and J. Berryhill (2023), “Initial policy considerations for generative artificial intelligence”,
OECD Artificial Intelligence Papers, No. 1, OECD Publishing, Paris, https://doi.org/10.1787/fae2d1e6-en.

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On the sustainability front, as a general-purpose technology with
applications across sectors, AI can create efficiencies that decrease
environmental impacts and lower emissions. At the same time, the training
and use of large-scale AI systems also require massive amounts of “AI
computing,” including processing power, memory, networking, storage, and
other resources. The environmental impacts of AI compute and applications
should be further measured and better understood to fully grasp their
net effects on the economy and the planet. Our report on Measuring the
environmental impacts of artificial intelligence compute and applications
provides a comprehensive overview of the issue at hand, including policy
priorities going forward. 3

Gen AI is a supplement
to human experience
and knowledge, not a
replacement."

Could you elaborate on how
some countries are harnessing
generative AI while others face
challenges?
Open data policy is likely to have a
big impact on generative AI. There
is a big push, including in OECD
member countries, for governments to
democratize access to high-quality data to
build algorithmic models.

There is an increased focus on investments in research in new technologies,
particularly AI. Governments are rethinking resource allocation and are
putting incentives in place. They are increasingly focused on the domestic
ability to build, use, and diffuse AI. They aim to create efficient businesses,
supported by innovative product solutions and enhanced problem-solving
capabilities. As a result, there are many dynamic startup communities in the
AI space.
There needs to be a collective, coordinated global approach to
standardization and policy alignment around AI development, deployment,
and governance. The OECD AI Principles are a key standard in this area,
guiding AI actors in their efforts to develop trustworthy AI, and promoting
interoperability in AI policy frameworks.

3.

OECD (2022), “Measuring the environmental impacts of artificial intelligence compute
and applications: The AI footprint”, OECD Digital Economy Papers, No. 341, OECD
Publishing, Paris, https://doi.org/10.1787/7babf571-en.

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Executive Conversations

Values-based principles

Recommendations for policy makers

Inclusive growth,
sustainable development
and well-being

Investing in AI research and
development

Human rights and
democratic values, including
fairness and privacy

Transparency and
explainability

Robustness, security
and safety

Accountability

Source: OECD - https://oecd.ai/en/ai-principles

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Fostering an inclusive
AI-enabling ecosystem

OECD’s AI
Principles

Shaping an enabling
interoperable governance and
policy environment for AI
Building human capacity
and preparing for labour
market transition

International co-operation
for trustworthy AI

Executive Conversations

ALLEVIATING ETHICAL CONCERNS

What are your thoughts on copyright issues and misuse of
personal data with implementation of generative AI?
The discussion around copyright is extremely important but also difficult.
We see countries taking different approaches as they navigate the trade-offs
between enabling access to training data to spur innovation, and minimizing
the risks of harm to content owners. For example, Japan has removed all
copyright protections from generative AI training, which is an interesting
approach. Meanwhile, in the US, several generative AI lawsuits are underway.
The concept of “fair use” is likely to be a prominent topic of discussion going
forward.
In this context, data privacy considerations are a key aspect that
organizations and individuals are exploring extensively. There is a lot of
work to be done to improve transparency and determine what data sources
should be used to train AI models. It is essential to put the appropriate
safeguards in place, including data protection considerations. In the coming
months, we expect an OECD report on AI and intellectual property rights,
focusing on policy considerations and potential policy solutions related to
copyright and data scraping challenges.

"There is a lot of work to be
done to improve transparency
and determine what data
sources should be used to train
AI models."

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Executive Conversations

A NEED FOR MORE INCLUSIVE ACCESS TO TECHNOLOGIES

How realistic is the goal of an equitable global digital
transformation?
Promoting digital inclusion across countries and population groups, including
genders, age groups, and income levels, is essential to ensure a human-centric
digital transformation. Digitalization can help bridge digital divides, but it can also
exacerbate existing inequalities.
To use gender as an example: in 2023, more than twice as many men than women
at the OECD, on average, wrote a computer code.4 More broadly, girls show lower
enrollment rates in disciplines crucial for success in a digital landscape, like STEM
and ICT. This has downstream effects on the participation of women in scientific
discovery. For instance, only 8% of AI journal articles in 2023 were written exclusively
by women, compared with almost half (41%) written exclusively by men.5
To foster an inclusive and sustainable digital economy, individuals of all backgrounds
need to be well-equipped to make the most of digital technologies. This includes,
for instance, having access to high-speed, high-quality, and affordable connectivity,
as well as having the necessary skills to reap the benefits of digital transformation.
Policies that promote such initiatives, as well as positive multistakeholder
collaboration, are necessary to address inequalities in the digital age.

Open data policy
is likely to have
a big impact on
generative AI."

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4.

OECD Going Digital Toolkit

5.

“Live Data from OECD.AI - OECD.AI.” 2024. Oecd.ai. 2024. https://oecd.ai/en/
data?selectedArea=ai-research&selectedVisualization=ai-research-publicationsexclusively-by-gender.

Capgemini Research Institute

Gener(AI)ting the future

Executive Conversations

Audrey Plonk
Deputy Director, Directorate
for Science, Technology and
Innovation (STI),
OECD

"To foster an inclusive and
sustainable digital economy,
individuals of all backgrounds
need to be well-equipped to
make the most of digital
technologies."

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Executive Conversations

DRAGOŞ
TUDORACHE

Former Member of the
European Parliament –
Rapporteur on the AI Act

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GUIDING AI TO
THE RIGHT PATH
Dragoş Tudorache is a former
member of the European Parliament
and Vice-President of the Renew
Europe Group. He also serves as
Chair of the Special Committee on
Artificial Intelligence in a Digital Age
(AIDA), and the LIBE rapporteur on
the AI Act.

As well as artificial intelligence (AI)
and new technologies, his interests
in the European Parliament include
security and defense, transatlantic
issues, the Republic of Moldova, and
internal affairs. Dragoş is currently
based in Brussels, Belgium.

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Executive Conversations

RELEVANCE OF THE AI ACT

Why do you think the EU felt there was a
need to draft an AI Act?
AI will change the world. It will have a huge impact on
mankind, much more than any other technology to date.
While we already see the great potential benefits of AI, it
also brings significant risks. There are also broader risks
linked to the way our democracies and societies function,
and the elements of truth and trust, which are so
fundamental for the social contract within our societies.
The European Parliament recognized the importance of
conversational AI early on and the Commission President
committed to bringing legislation forward. Given the
speed at which AI evolves, society needs some signposts
to know where to take it.
Most companies working with AI already had general
principles, codes of conduct, or self-regulation in place.
There were guidelines outlined by UNESCO, OECD, and
even by the European Parliament. But we realized that
these measures were insufficient to mitigate the very real
risks, such as discrimination bias, etc.

Dragoş Tudorache
Former Member of the
European Parliament –
Rapporteur on the AI Act

We needed to put stronger safeguards in place that
command respect and, ultimately, help society to trust in
the interaction with this technology, hence the decision
to formulate the policy.

We needed to put stronger safeguards in place that
command respect and, ultimately, help society to
trust in the interaction with this technology, hence
the decision to formulate the policy."

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Executive Conversations

A key goal of the AI Act is to
keep humans in the loop when
it comes to AI. Why do you
think that is important?
No matter how sophisticated or smart a
machine is, it's still a machine. We can’t
take the recommendations of a machine
on trust. They have to be verified by a
human supervisor.
The whole idea of making AI humancentric means that there must ultimately
be human responsibility for the
decisions or recommendations made by
the model about any area that touches
upon human rights or the broader values
and interests of society.
However, this regulation only covers
a small fraction of what AI comprises.
There is a huge amount of AI used in
industrial robotics, for example, such
as for optimizing a production line.
These applications of AI have nothing
to do with my rights as an individual or
social values and, therefore, are free of
regulation and must remain that way.

"Innovation must
be untrammeled
and freely
expressed.
However, when
it intersects with
people's rights
and interests, it
must be filtered
through the
appropriate
standards."

Innovation must be untrammeled and
freely expressed. However, when it
intersects with people's rights and
interests, it must be filtered through the
appropriate standards.

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Executive Conversations

RISK ASSESSMENT OF AI USE CASES

You have also come up with a robust set of metrics to
identify potential AI risks. Can you help us understand
your framework?
The AI Act categorizes applications or use cases of AI into three major
categories. The first is prohibitions, which we see as contrary to the
fundamental principles of our society and, therefore, are to be eliminated.
The medium/low-risk category generally encompasses transparency-related
obligations, which don’t require extensive regulation.
The bulk of regulation applies to high-risk AI. This is often sophisticated and
highly competitive AI destined for market, and which must, therefore, meet
strict standards.
Those obligations are, again, roughly classified into three buckets, one of
which deals with transparency related to the data that one uses to train, and
other processes used in development.
Secondly, there is explainability: one must explain how the organization
instructed and worked with the AI, and then how they handled
documentation, registration, and so on.
Thirdly, one has to show the proof or evidence, which helps market
regulators keep track of what goes on, and to support interaction with the
companies as required. This proof is also important for helping the entities
downstream to understand that the AI value chain is highly complex.

Hypothetically speaking, how would the AI Act address a
low-risk system developing into a high-risk one?
This is a dynamic market with many changes, not all of which can be
foreseen.
So, we try to do two things. The first is to make the obligation as technologyneutral as possible, i.e., to formulate them in such a way that they are

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Executive Conversations

indifferent to the evolution of the technology. An obligation to provide the
training data means the same thing irrespective of AI’s use case, version,
size, or the complexity of the machine.
Secondly, we keep the regulation alive to counterbalance the evolution of
use cases from no or low risk to high risk, and vice versa.
We've introduced a scientific and advisory board for future decision-makers
on AI in the EU, to alert us to changes in levels of risk for a specific use case.
Since we understand that the project will evolve, we have retained the
annexes and the descriptions to be adaptable to modification of the
technical criteria, such as the one used to describe the threshold of high risk.

BALANCING INNOVATION WITH REGULATION

Are you concerned that regulation will slow the pace of
innovation?
Personally, I think predictability and simplicity can benefit our business
environment.
I also think that self-discipline alone will
not be sufficient to manage AI.

A sense of clarity and
predictability will help
organizations."

However, to allow innovation to
flourish, we must keep this regulation
as light-touch as possible and support
organizations in complying. While we
may impose some rules, we also create
facilitation and tools to help people and
organizations comply with those rules.

I don’t think this regulation will stop innovation, however. Rather, it will bring
clarity and a necessary sense of direction. I spoke to a lot of venture capital
firms who want assurance that, when they commit capital to a project, it
will meet certain standards. A sense of clarity and predictability will help
organizations.

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Executive Conversations

What do you say to
those who suggest
that the burden
of regulation falls
much more heavily
on startups and
other smaller
companies than on
large, established
companies?
Yes, we were very aware
that big tech can afford
the measures required
to conform much more
easily than smaller
organizations. They
just hire a couple more
lawyers or compliance
officers. But smaller
companies don’t have
those resources, and
we wanted to level the
playing field as much as
possible.
Consequently, there are
special provisions for
small- and medium-sized
enterprises (SMEs), which
are exempt from some of
the heavier parts of the
regulation.

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FOR COMPLIANCE, SELF-REGULATION IS THE KEY

How should large organizations look at selfregulation itself as they develop and deploy
their AI systems?
It can be done in three different ways. First, there's selfassessment, which means a lot of the investigation and
checking has to come from our own analysis.
Then there's a provision for codes of conduct. This is also a
way of co-generating compliance and giving organizations a
nudge towards exercising self-restraint and self-discipline,
even outside of the stricter parameters of the obligations of
the law.
Thirdly, and very importantly, comes the regulation of the
foundational models. AI is a fluid technology, and there’s
still a lot that we don't understand about it. It takes time
to develop a framework of standards that we can expect
organizations to understand and adhere to.

"SELF-DISCIPLINE
ALONE WILL NOT
BE SUFFICIENT TO
MANAGE AI."

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THE FUTURE OF AI AND REGULATION

Which skills will future
lawmakers require to
understand the algorithmic
future?
We'll probably need to use AI to legislate
in the future. I see a future where we'll
actually have to rely on AI to regulate
AI. During the past 3-4 years of working
on AI, I've talked to lawmakers in all
corners of the world, and we all face the
same challenge. We must be prepared
to educate ourselves and then accept
the new methods we have learned. We
shouldn't be ashamed to admit that
we need to know more. We need to go
through this learning phase before we
consider the bigger questions around
regulation.
The future will bring many more
challenges of this nature. Lawmakers
globally will need to explore new forms
of regulation and organizations must
adapt to them, so that we can proceed to
a productive future with AI as a great ally.

The views expressed in this interview do not represent the official position of the European Commission.

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Dragoş Tudorache
Former Member of the
European Parliament –
Rapporteur on the AI Act

“Lawmakers globally will need
to explore new forms of
regulation and organizations
must adapt to them, so that we
can proceed to a productive
future with AI as a great ally. ”

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Executive Conversations

SCOTT BELSKY

Chief Strategy Officer and EVP,
Design & Emerging Products
Adobe

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CREATIVITY
IS THE NEW
PRODUCTIVITY
Scott Belsky leads corporate strategy and
development at Adobe. He is responsible for
design across the Digital Media and Digital
Experience businesses and for driving the
incubation of some of Adobe’s fastest-growing
emerging products. Prior to this role, Scott
was Adobe’s chief product officer, leading the
development of all Creative Cloud products.
Before joining Adobe in December 2017,
Scott was a venture investor at Benchmark in
San Francisco. This is Scott’s second tenure
at Adobe. He originally joined the company
after it acquired Behance in 2012. He has also
published two best-selling books, Making Ideas
Happen (2011) and The Messy Middle (2018).

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Executive Conversations

How has design evolved over
recent years?
Two things have happened in the world of
design. I think it's fair to say that the floor
has been lowered. The skill level required
to express yourself creatively has gone
down. People can now start from something
as opposed to nothing. With generative
AI, they can prompt something and start
getting something to work with.
The tools themselves have evolved to enable
easier onboarding, making them more
accessible to more people. This is really
exciting. Creativity is the new productivity.
Productivity – getting things done as fast
as possible in as great a volume as possible
– is shifting to automation. Increasingly,
organizations are promoting people based
on their creativity, ideas, and the way they
tell a story.
However, the ceiling is also rising. It is
incredible what creative professionals are
capable of now because they are exploring
the full surface area of possibilities. Hence,
the constraints are disappearing. The cycles
that creative professionals can have to
explore the surface area of possibility are
growing materially, and that is raising the
ceiling of what is possible. Those are the two
trends that I think are truly transforming the
world of design and creativity.

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Scott Belsky
Chief Strategy Officer and
EVP, Design & Emerging
Products
Adobe

Executive Conversations

ADOBE AND AI

Can you help us understand Adobe’s generative AI product
offering?
We have built our family of foundational models based on our customers'
needs. So, when you look at Photoshop, many things that were timeconsuming and might have seemed very complex in the past can now be
done “automagically” using Firefly imaging models.
We built the imaging models not only to unleash these superpowers but
also to preserve Photoshop’s unique aspect: non-destructive editing. So,
customers can come in and explore, ideate, and then try out variations of
that idea without losing precision or the ability to retrace their steps. A lot of
AI-first tools do not allow customers to pick up the chisel themselves and try
to make something they see in their mind's eye. Our customers want to be
able to do that.
That was our fundamental approach across imaging, illustration, and 3D, and
increasingly across fields such as video. And, of course, we are just getting
started. The advent of these Generative AI tools has totally transcended our
expectations, but there’s so much more to come.

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GENERATIVE AI AND THE CREATIVE PROFESSIONAL

"Thanks to Generative AI,
what is even more important
than skills is taste, intuition
and creative direction."

How must creative professionals evolve to keep pace?
We'll see creative professionals no longer feeling constrained by their
category. The idea of defining yourself as an animator, a graphic designer, or
a photographer seems antiquated in a world where you could harness your
creativity to work across any medium.
And suppose your customer or client says, “Can you take that graphic
you made and turn it into an animated version for TikTok or Instagram or
whatever?” Instead of saying, “Oh, I don’t know how,” creative professionals
are going to gradually be able to take that on themselves and execute to a
high standard using these new tools and capabilities. The skills barrier, while
still important, is much less constraining. Thanks to Generative AI, what is
even more important than skills is taste, intuition and creative direction.

Do you see generative AI as a threat to designers?
Some might say that generative AI is disruptive and impacts creative
professionals by automating their work. and that we’ll need fewer of them.
But the truth, and I think what we are seeing in our own customer base, is
different. With generative AI, the cost of exploring has decreased, so we will
see organizations coming up with new use cases.

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Think about engineers as a proxy. Over the last few decades, engineers
have become more productive every year, sometimes by multiples, owing
to technological breakthroughs and new programming languages, consoles,
and tools. Nevertheless, organizations keep hiring more engineers. Why?
Because they want to do more. Organizations always want to build more
products, personalize them, optimize them, and invest in building up their
business. The more productive they become, the more people they hire.
Similarly, with creativity, brands are realizing, “Wait, instead of just doing
one campaign, let's do five. Let's do 50 campaigns. Let's do a campaign for
every region. Let's do personalized visual experiences for every segment.”
Generative AI technology has unlocked all of these possibilities.

How is generative AI likely to change the field of design?
Ultimately, creativity is what moves us. Even if digital content can be created
at an infinite scale for nothing, and personalized to each of us, we are not
going to feel compelled to engage with it unless it moves us in some way.
Generative AI is like a consensus machine. It generates what it thinks you
would want, based on what you and others have consumed before. That
is not what moves us. If every brand floods the user with SEO content,
imagery, or video because it's cheap to create the user will be overwhelmed,
rather than inspired. The user will be left more than ever craving a story with
a human touch, written just for them.

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Executive Conversations

How can we measure
the value that humans
bring to the table?
Human creatives must
come up with innovative,
unpredictable solutions rather
than consensus outcomes.
We need people to spend
more of their time thinking,
emoting, and tapping into
their “inner humanity.” I call
that liberating our “ingenuity
per person.”
If a job typically only allows
you to spend 10% of your
time on ingenuity because
90% is required for the
methodic productivityoriented minutiae, imagine
what a difference generative
AI’s computing power can
make! It can offload much
of that for you, and you are
freed up to do more of the
deep thinking. That increases
the ingenuity per person on
your team. And if you have
more ingenuity per person,
you are going to want more
people because then you
will have collectively more
ingenuity in your organization.

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GENERATIVE AI AND THE ENTERPRISE

What is your view on the copyright concerns
surrounding generative AI?
If someone can reproduce and manipulate someone else's likeness or
style without their permission, that’s a problem (one that we’re lobbying
Congress to fix). But I also recognize that we have to let this technology
take us somewhere exciting. The worst thing would be to have overly
harsh restrictions choke off progress. So, we have to balance the
absolute need to allow progress with the need to proceed in a thoughtful
and responsible way.

How is the role of the
marketing professional likely
to evolve?

We have to let this
technology take us
somewhere exciting.
The worst thing would
be to have overly harsh
restrictions choke off
progress."

Marketing professionals need to
start experimenting and thinking
expansively about what generative
AI can do for them. The future
of the digital world is going to
be more personalized than ever
before. Marketing has not yet
been personalized to the individual
consumer, at least not at scale in any
profound way.

And that is the future. So, you have
to kind of rethink the whole marketing stack and get away from ‘macro
marketing,’ which takes weeks and weeks and meeting after meeting.
Increasingly, the world requires agile or ‘micro’ marketing, which means
marketers getting an idea from social media, say, and acting on it in
real time. These campaigns need to be launched in 60 seconds, not six
months. And to do that at scale, you need to give them tools, backed up
by new policies.

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How should CMOs look at generative AI?
I think CMOs need to awaken their minds to the role of both macro and
micro marketing. The CMO needs to recognize that this agile marketing
may be beyond their organization. They need to involve all their
stakeholders and empower them to participate. If I were a CMO, I would be
forcing my team to rethink how they generate variations of assets using
generative AI, how they experiment, how they test, and all these things.

LOOKING FORWARD

What excites you most about the future of tech and its
influence on the creative space?
Right now, there are two things about the future of technology that excite
me. The first is one thing that makes us uniquely human–ubiquitous access
to creative expression. This taps into our humanity. We are most creatively
confident when we are five years old, but we lose our creative confidence as
we get older because of the skills gap, exposure to criticism, and just the lack
of access to creative tools. Generative AI is fundamentally changing this.
The second thing is personalized experiences at scale. We all want to be
known wherever we go. We want to know how we are known, and we do not
want to feel like marketing technology or ad tech is always secretly guessing
what we might like. Generative AI and AI agents that we will encounter
across brands are going to make that possible.

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Scott Belsky
Chief Strategy Officer and EVP,
Design & Emerging Products
Adobe

"Increasingly, the world requires
agile or ‘micro’ marketing, which
means marketers getting an idea
from social media, say, and
acting on it in real time. These
campaigns need to be launched
in 60 seconds, not six months."

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Executive Conversations

CLARA SHIH

Chief Executive Officer
Salesforce AI

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A PLATFORM FOR
GENERATIVE AI
Clara Shih is the CEO of Salesforce AI. In her role,
Clara oversees artificial intelligence (AI) efforts
across the organization, including product, goto-market, growth, adoption, and ecosystem for
Salesforce’s AI customer relationship management
(CRM) platform. Clara also served on the Starbucks
board of directors for 12 years and serves as
Executive Chair of Hearsay Systems, a digital
software firm she founded in 2009 that is merging
with Yext (NYSE:YEXT).

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SALESFORCE AND AI

What led to the development of Salesforce AI?
We have been working on AI for a long time. In 2014, we
established our Salesforce AI Research Group, and in 2016,
we introduced the first predictive AI for CRM, Einstein.
Salesforce Research has been developing large language
models (LLMs) for many years. The popularity of ChatGPT
and increased demand for enterprise AI led us to productize
our generative AI applications and platform.
Agentforce, our suite of customizable autonomous agents
and low-code tools – powered by our Customer 360
applications, Data Cloud, and Salesforce Platform – makes
deploying and getting value from trusted agents easier than
ever. It’s the next generation of our Einstein AI solutions.
Every agent request runs through our Einstein Trust layer
to ensure data privacy, data security, ethical guardrails,
observability, and monitoring. We know customers don’t
want to be locked-in to a specific model, especially given the
rapid advancement and growth in model options, so we’ve
architected Agentforce to work with any model.
To help customers see value fast, we offer over 100 outof-the-box (OOTB) AI use cases and Agentforce agents,
including service agent, sales development representative
agent, commerce agent, merchandiser, buyer agent,
personal shopper, and campaign optimizer. These OOTB
agents make it easy to get started and are easy to customize.
Agentforce agents can be set up in minutes, scale easily,
and work 24/7 on any channel (Salesforce offers digital
messaging, messaging over in-app and web (MIAW), email,
and now voice natively).
Our customers and partners can easily customize and build
trusted agents on Agentforce using our low-code tools such
as agent builder, prompt builder, and model builder.

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Clara Shih
Chief Executive Officer
Salesforce AI

Executive Conversations

Can you expand on why you chose the platform approach?
Agents have to be customized and grounded in trusted data and role- and
industry-based context in order to be production-ready for the enterprise.
Salesforce has always offered a user-friendly platform for customers and
partners to easily build and customize, and from the beginning, we designed
Salesforce Platform to be used even by non-programmers.
Our customers want to customize their own AI apps, and we give them
options to do this either with no code, low code, or pro code. Rather than
every organization having to build their own data cloud, trust layer, and
API management, we make it easy by offering Salesforce Data Cloud,
the Einstein Trust Layer, and Mulesoft as part of the Salesforce Platform.
Customers and partners tell us all the time how much they appreciate how
the Einstein Trust Layer takes care of data masking, citations, audit trail,
toxicity filters, zero retention prompts, and
prompt defense to mitigate cybersecurity
risks.

From the beginning,
we designed
Salesforce Platform
to be used even by
non-programmers."

Prompt builder and agent builder allow
customers to take our out of the box (OOTB)
use cases and customize them with their
own brand voice, company policies and
procedures, and reference organizationspecific custom data. For example, an
automotive company can directly reference
specific custom fields to guide their agents,
such as the make, model, and warranty SLA.
A retail customer would have a different set of custom fields and business
processes. Customers can reference any structured or unstructured data
from across their organization, whether it is in Salesforce or an external data
lake or data warehouse such as Snowflake, Databricks, or Big Query, by using
our Data Cloud.
Agentforce is about deploying autonomous agents to help drive human
productivity. It follows all of the data-sharing rules in each Salesforce
organization and is personalized to every user. For example, if two different
sales reps within the same company ask the same question of Agentforce
– such as “What are my top sales deals this quarter?” – they will each get a
customized answer based on each individual rep’s territory, customers, and
open opportunities.

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GENERATIVE AI – A POTENTIAL ECONOMIC IMPACT OF TRILLIONS OF DOLLARS

What kind of impact do you
envisage generative AI having on
large organizations globally?

Gucci used Salesforce
AI to transform their
service representatives
into sellers."

Generative AI offers a tremendous
opportunity with a potential economic
impact of trillions of dollars from both
productivity gains and cost savings.
Agentforce customers like Wiley and
OpenTable are finding tremendous
success, increasing the number of routine support issues they can use AI
to resolve autonomously 24/7 while increasing employee engagement and
customer satisfaction.

But change cannot happen overnight. Companies need support in the form
of trusted software systems and partners who can guide them through
the transformation. In the current scenario, even the most forwardlooking customers want AI automation to allow employees to work in
more productive and efficient ways. Then, the priority shifts to reshaping
departments. For example, Gucci used Salesforce AI to transform their
service representatives into sellers. In addition to resolving customer
support problems faster, our AI tools also helped teach them how to help
customers find additional products to buy and to complete e-commerce
transactions. The third phase is enterprise transformation. Just as with the
internet, generative AI and agents will enable new pricing models, business
models, and organization models.

"Generative AI offers a tremendous
opportunity with a potential economic
impact of trillions of dollars from both
productivity gains and cost savings."

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Is there any specific industry
in which you see the most
enthusiasm?
We offer 15 different industry clouds
at Salesforce and there has been AI
demand and customer success in every
industry. For example, Santander Bank,
in the financial services industry, uses
Agentforce to visualize its international
trade trends and customer insights in
real time to guide customers towards the
right products. L’Oréal, in the consumer
products industry, uses Agentforce to
boost direct-to-consumer revenue with
AI-powered product recommendations.
Iron Mountain, an information
management and storage company,
turned to Salesforce for customer
service, and their service representatives
use Agentforce to create a connected
experience across email, chat, and
voice. Simplyhealth, a leading health
insurance provider in the UK deployed
our Agentforce for their customer
service team. They saw 90% time savings
by using generative AI to respond to
customer emails, and they were able
to resolve over one-third of their cases
using conversational AI.

"90% TIME
SAVINGS
BY USING
GENERATIVE
AI TO RESPOND
TO CUSTOMER
EMAILS."

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Which generative AI use cases do you think are the most popular?
Each industry has specific use cases. For example, in communications, customers want
organizations to address billing inquiries promptly. In consumer goods, the focus is
on crafting AI-driven personalized product descriptions and marketing campaigns.
In healthcare, it is on optimizing patient appointment scheduling and reminders in a
compliant way.
This is why we were so thrilled recently to launch over 100 new out-of-the-box
Agentforce for Industries features, now available in our Salesforce AI Use Case Library.
Customers can easily customize and deploy this new ready-to-use AI to automate
time-consuming tasks such as matching patients to clinical trials, generating proactive
maintenance alerts for industrial machinery, and
delivering government program benefits. These
use cases are tailored by role and to each of our 15
Industries clouds for accelerated time to value. They
are easily customizable with prompt builder, agent
builder, and model builder.

In customer service,
emphasis is on
delivering solutions
rapidly, addressing
questions quickly and
accurately, and closing
the case promptly."

Many companies trying to "DIY" their AI tech stack
are finding they've wasted a lot of time and money
finetuning models and building data pipelines without
much to show for their efforts. In contrast, customers
from AAA Insurance and Air India to Wyndham Hotels
& Resorts are finding rapid value in a matter of weeks
using these out-of-the-box Salesforce AI features.

Horizontally, across functions and sales, we place importance on streamlining account
research and meeting preparation, and gaining a thorough understanding of all the
open support cases and marketing engagement. In customer service, emphasis is on
delivering solutions rapidly, addressing questions quickly and accurately, and closing
the case promptly. It also enables better formulation of draft cases and incident
summaries to help service representatives to allocate their time to more strategically
significant tasks. In marketing, use cases include generating personalized emails and
campaigns, segments, landing pages, auto-populating contact forms, and rapidly
understanding insights from large-scale customer surveys.
In e-commerce, the focus is on the creation of digital storefronts, promotions, product
descriptions, and outlining e-commerce strategies. From a developer standpoint, it's
boosting productivity with AI-driven code generation and test generation.

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CHALLENGES WITH GENERATIVE AI

"When we meet with
the Gucci customer
service representatives
who are using
Agentforce Service
Agents from Salesforce,
they're so fired up."

How do you envision the future balance between human-led and
AI-led customer services?

ATM machines did not
replace tellers. There are
more tellers today than
ATMs, but now they are
personal bankers."

There's going to be a need for both. ATM
machines did not replace tellers. There are
more tellers today than ATMs, but now they
are personal bankers and focus on forging
personal relationships and upselling. AI will allow
workers to move away from repetitive tasks
to focus on doing what humans do best, which
is building relationships, unlocking creativity,
making connections, and addressing higher-order
problems. When we meet with the Gucci customer
service representatives who are using Agentforce
Service Agents from Salesforce, they're so fired
up. They feel like we are empowering them to do
the best work of their careers.

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"ORGANIZATIONS MUST TAKE
AN ETHICS-FIRST, TRUST-BASED
APPROACH TO AI PRODUCT
DEVELOPMENT."
What worries you the most about generative AI?
Any powerful new technology has a range of different applications. The
majority of them are good, but there can also be some nefarious use cases.
I think educating law enforcement professionals, government leaders, and
voters on the risks of misinformation and disinformation, including fake AIgenerated images, is of utmost importance.
Salesforce has joined the Business for America coalition supporting the
bipartisan Protect Elections from Deceptive AI Act. This legislation would
ban the use of AI to generate materially deceptive content that falsely
depicts candidates in political ads with the intention of influencing federal
elections.

How do you think organizations can create representative
and inclusive datasets?
Organizations must take an ethics-first, trust-based approach to AI product
development. Trust is the most crucial element engineered into any
Salesforce product. We have also enabled responsible AI practices across the
organization.
For example, to protect consumer and employee privacy, we disallow the
use of facial recognition AI within Salesforce products. Another aspect of
our AI acceptable use policy is that when one of our customer's customers
is using an AI agent, we require the agent to self-identify as an AI versus
masquerading as a human. This is to ensure trust and transparency remain
paramount.

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We’ve open-sourced our
trusted AI principles around
five pillars:
1. Being responsible,
safeguarding human
rights, and protecting the
data with which we're
entrusted
2. Being accountable,
seeking feedback,
and acting on it for
continuous improvement
from all stakeholders
3. Developing a transparent
user experience to guide
users through any AIdriven recommendations
4. AI is here to empower
people – not replace
them
5. AI should be inclusive

"We disallow the use of
facial recognition AI within
Salesforce products."

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What are your views on the climate impact of LLMs?
Sustainability is among our core values. LLMs expend a tremendous amount
of energy on both training and running the models. At Salesforce, we
envision that, because of climate impact, as
well as for cost and performance reasons, the
future of AI will be a combination of LLMs
and small models.

The future of AI will be
a combination of LLMs
and small models."

Currently, small language models (SLMs),
even ones that run locally on laptops, could
accomplish similar results to those that LLMs
produce. Salesforce AI Research Group is
developing these small and medium-sized
fine-tuned models, which are industry- and use-case-specific. Over time, we
will help our customers figure out the right model mix for them.

GENERATIVE AI REGULATORY LANDSCAPE

What are your thoughts on generative AI regulation?
The power of generative AI justifies strict regulation. The smartest approach
involves broadening the scope of existing laws to encompass elements
particular to AI usage. A great example is the Telephone Consumer
Protection Act (TCPA) in the US. That requires organizations to obtain
customer consent before robocalling or text messaging the consumer.
Recently, the TCPA was extended to include the use of AI-generated voices.
It makes a lot of sense to take existing laws and ensure that they are
updated to capture the new risks that AI has introduced.

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Clara Shih
Chief Executive Officer
Salesforce AI

“The power of generative AI
justifies strict regulation.”

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Executive Conversations

ANDREW NG

Founder of LandingAI and
managing general partner, AI Fund

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AI:THE NEW
ELECTRICITY
Dr. Ng is the founder of LandingAI,
which provides a visual AI platform,
and the managing general partner
of AI Fund, a venture studio that
supports entrepreneurs in building
AI companies. He is also the leader
of DeepLearning.AI, an education
technology company he founded to

provide AI training. He is the chair and co-founder
of Coursera, an open online course provider, of
which he was co-CEO until 2014. In addition, he
is an adjunct professor at Stanford University.
Prior to this, he was chief scientist at the Chinese
tech multinational Baidu and founding lead of
Google’s Google Brain deep learning project. He
sits on the board of directors of Amazon.

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LANDINGAI – THE REVOLUTION OF UNSTRUCTURED DATA ANALYSIS

What is the focus of LandingAI?
LandingAI provides a platform that makes visual AI
intuitive and accessible to use. We have already seen
the text-processing revolution with ChatGPT and
large language models (LLMs). I think we are at the
very beginning of the image-processing and analysis
revolution. And I do not mean just image generation, but
unstructured data analysis.
Until now, writing software to help computers “see”
has been difficult. Self-driving cars, for example, are
not yet reliable in detecting other objects around
them. In manufacturing, there has been a lot of work to
build valuable inspection systems. Today, cameras are
ubiquitous. Computers will be able to interpret images
with increasing accuracy. I think the image-processing
revolution might be as big as the text-processing
revolution.

Andrew Ng
Founder of LandingAI and
managing general partner,
AI Fund

Are there specific industries where image
processing can have a significant impact?
We started in manufacturing and industrial automation,
focusing on visual inspection. But now applications span
multiple industries. For example, life sciences involve
a lot of analysis of microscopic images on slides. There
are many applications in geospatial aero imagery and
retail, too. A more diverse set of industries than I would
have imagined possible a few years ago applies generalpurpose image-analysis algorithms nowadays.

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We are at the very
beginning of the
image-processing and
analysis revolution.
And I do not mean
just image generation,
but unstructured data
analysis."

Executive Conversations

HOW AI WILL BECOME AS PERVASIVE AS ELECTRICITY

Where do you believe we stand
currently in the AI development
cycle?

AI is a general-purpose Over the past 15 years, progress has been
tremendous, with deep learning making
technology, meaning
huge strides. This has allowed us to “label”
that, like electricity, it’s things. For example, given an ad, we can
useful for many things." label who is most likely to click on it. Given

a shipping route, we can label it with the
estimated fuel consumption per run. There
are so many use cases across all industries.
And in the past couple of years, generative AI has taken off as well. In
another five years, I think we'll look back at where we are now and see this
as the early stages.

You referred to AI as “the new electricity.” Can you
expand on that analogy?
AI is a general-purpose technology, meaning that, like electricity, it’s useful
for many things. If I were to ask “what is electricity for?” it would be difficult
to answer that question, because it's so pervasive – and the same goes for
AI. AI’s uses range from online advertising to analyzing medical images to
arrive at a diagnosis, to copyediting, to fact-finding. Potentially, there are
thousands of other applications. In the early days of electricity, no one
dreamed of all the things we can do with it now. With AI, the process is
underway, but it will take many years to identify and build all the use cases
to which AI can be very effectively applied.

"In the early days of electricity, no
one dreamed of all the things we can
do with it now."

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Are there industries where AI adoption is more
pronounced?
Industries that use more digital records, and to which data use is integral,
with a culture of data-driven decision-making, are able to harness AI more
effectively. Over the past 20 years, almost all industries have become
increasingly digital. Those that have progressed further with digitization
seem to be adopting AI faster. Sectors such as healthcare and financial
services are ahead in adopting AI. However, in today's digital world, I think all
industries will get there quite quickly.

Can you share any innovative AI use cases you find
particularly compelling?
Finding the best use case for AI is a bit like finding the most innovative use
of electricity – it is the basis for so much. We are working on a wide variety
of interesting use cases. For instance, AI-driven relationship mentoring and
addressing societal issues such as loneliness. AI also has immense potential
to revolutionize traditional processes.

"WE ARE WORKING ON
A WIDE VARIETY OF
INTERESTING USE CASES.
FOR INSTANCE, AI-DRIVEN
RELATIONSHIP MENTORING
AND ADDRESSING
SOCIETAL ISSUES SUCH AS
LONELINESS."

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Are there barriers to greater AI adoption?
One of the biggest is inadequate training and education. There are other
barriers, concerning data, culture, and governance. But when the team
is trained to understand AI, it can solve all of those problems. So, one of
the things I did recently was launch a course, Generative AI for everyone,
on Coursera. There are quite a few companies whose entire leadership
teams are taking that course. If the leadership, followed by the rest of the
organization, can really understand the potential of AI, that can unlock a lot
of value.

AI FOR CLIMATE ENGINEERING

What about AI's role in sustainability and climate change
mitigation?
One area we must study seriously is sunlight reduction, which is also called
climate geoengineering via solar-radiation management. If we use highaltitude stratospheric aerosol injection, that
could effectively put a parasol around the
planet to reflect sunlight and cool us down.
The science of sunlight reduction methods is
being developed. So, I think we do not fully
understand all the impacts if we were to take
this action.

Given the world's
collective inability to
reduce CO2 emissions
in the way we know
we need to, I think it
is past time to take
climate engineering
more seriously."

We are now able to train very large foundation
models to predict more accurately the effects
of stratospheric aerosol injection. Given the
world's collective inability to reduce CO2
emissions in the way we know we need to, I
think it is past time to take climate engineering
more seriously. I think AI, especially large AI
foundation models of climate, have a large role
to play in that.

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AI AND SOCIETY

How do you foresee AI impacting the labor market?
People have talked about AI replacing jobs, which is an important conversation.
However, from a business perspective, a more useful framework is task-based
analysis to figure out which tasks AI can augment or automate.

For many jobs, AI will
only automate or
augment 20-30% of
tasks. So, there's a huge
productivity boost, but
people are still required
for the remaining 70% of
the role."

Breaking down the role of, say, a call center
operator into its constituent tasks – answering
the phone, sending text messages, pulling up
customer records, mentoring less experienced
service agents – you can then systematically
figure out where to apply AI to enhance
productivity. While AI will replace those jobs
that are completely or largely automatable,
overall job replacement may be less than
people fear. For many jobs, AI will only
automate or augment 20-30% of tasks. So,
there's a huge productivity boost, but people
are still required for the remaining 70% of
the role. For most jobs, it will be only a subset
of tasks that AI can really make significant
changes in the near future.

There has been much debate about AI's broader societal
impact, with concerns voiced about misinformation and wealth
distribution. How valid are these concerns and how can we
mitigate the risks?
I think the fears of AI wiping out humanity are science-fictional. That is not going to
happen. But there are some risks to which we should pay attention. If AI-generated
media pollute our information ecosystem, for example, what are the implications for
democracy? We have to ensure that people have a sufficient understanding of AI.
The other thing we should pay attention to is whether the significant wealth that
AI will create can be fairly shared. This includes making sure we avoid discriminating
against certain subgroups, but also, more broadly, how do we make sure that
everyone benefits?

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How can society prepare for the impact of AI to prevent
societal disparities?

Because general
knowledge of AI is
not widespread, I see
many ill-thought-out
regulatory proposals,
both in the US and in
Europe."

Until legislators and citizens attain a broad
understanding of AI, governments are
vulnerable to powerful lobbying by special
interests, which is what I am seeing today,
certainly in the US, as well as some other
countries. Unfortunately, because general
knowledge of AI is not widespread, I see
many ill-thought-out regulatory proposals,
both in the US and in Europe. It is crucial that
lawmakers develop a good understanding of
AI.

What is your view on LLMs versus
small language models (SLMs)?

I think both will be important. For the most complex reasoning tasks, a
large LLM with many parameters is much more effective. But if you want a
grammar checker, then you do not need a trillion-parameter model trained
in science, philosophy, and ancient history. A SLM works just fine for this
sort of task. Also, there are a lot of use cases for SLMs that can run locally,
on-device, for reasons of privacy and security. I’d even go further and say
that there’s a strong incentive for PC manufacturers to encourage users to
upgrade their computers. AI gives a very meaningful reason for people to
upgrade their PCs now.

"For the most complex reasoning
tasks, a large LLM with many
parameters is much more effective.
But if you want a grammar checker,
then you do not need a trillionparameter model trained in science,
philosophy, and ancient history."

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THE FUTURE OF AI

Do you see a future where AI is capable of writing really
good code?
I hope we get to see artificial general intelligence (AGI) within our lifetimes.
I do not know if we will. And if we do get there, certainly, it will be able to
write code much better than today. However, for the near future, I think
more people should learn to code.
I advise high school and college students to learn to code. With AI, it's easier
and cheaper than ever before to code, and the return on investment (ROI) is
also higher. Someone who knows how to write code, set up a code prompt,
or call an LLM, can accomplish much more than someone who only knows
how to use a web user interface for an LLM. And the fact that AI can help us
with our coding has also made it easier to create more value.

What would you consider the ideal scenario for AI
development?
Firstly, I hope a lot more people receive AI training to spread the benefits of
the tech as widely as possible. Secondly, with AI as a very powerful generalpurpose technology, we need people from all industries to discover the use
cases with the highest potential for AI application, and then do the work
to build them out. A lot of media attention goes to the technology or the
companies providing AI tools, but it’s in the application of AI that is where
we will see the real success stories.

"FOR THE NEAR FUTURE, I
THINK MORE PEOPLE SHOULD
LEARN TO CODE."

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Andrew Ng
Founder of LandingAI and
managing general partner,
AI Fund

"I hope we get to see artificial
general intelligence (AGI) within
our lifetimes."

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Executive Conversations

CHEMA ALONSO

Chief Digital Officer
Telefónica

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SCALING AND
SECURING
GENERATIVE AI
Chema Alonso has sat on
Telefónica’s executive committee
since 2016. He is Chief Digital
Officer of Telefónica and CEO
of Telefónica Innovación Digital.

He oversees innovation, data, platforms, and
digital products and services, and leads the
digitalization of sales processes and customer
communication channels. In his dual role, he
also aims to promote innovation of new digital
products and services and internal efficiencies. In
striving to attain these, he harnesses Telefónica
Kernel, the organization’s AI-driven core digital
platform, with special focus on the digital home.
He holds a PhD in Computer Security from the
Universidad Rey Juan Carlos and a degree in
Technical Engineering in Computer Systems from
Universidad Politécnica de Madrid.

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DEPLOYING AND SCALING GENERATIVE AI

What has been Telefónica's experience with
AI and Gen AI?
When we created Telefónica Digital back in 2011, we
decided to form a lot of teams looking at data, creating
machine learning (ML) algorithms for internal efficiency,
network deployment, churn prediction, mobile quality,
and video recommendation, among other things. We
also created products that we still consume today. For
instance, we created Smart Steps, a technology that maps
paths around cities based on where personal devices
connect to the mobile network. This allows us, among
other things, to anticipate how people will move across
the city at specific times, where antennas are likely to
be congested, likely spots for traffic jams, and so on.
We have been selling this product to law enforcement
authorities and companies alike. This data enables them
to create emergency plans for specific dates.
In 2016, when we started our digital transformation into
Kernel, we decided that the advances in deep learning
and reinforced learning and the beginning of Gen AI
heralded the era of cognitive services. We created
Aura, a cognitive intelligence-based digital assistant for
managing our services. Aura today receives more than 37
million interactions monthly. Aura is already leveraged in
the contact center in Brazil, and as a copilot in Spain. We
have Aura integrated in TV remotes and inside various
apps.
We also have Aura in a second-screen device that
we call Movistar Home, which we are improving and
relaunching soon. We are adding Gen AI capabilities.

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Chema Alonso
Chief Digital Officer
Telefónica

Executive Conversations

So, when the first commercial large language models (LLMs) came out,
we started to work with all of them. First, we configured a copilot for our
developers at the chief digital office unit. The copilot already has a 31-32%
acceptance rate in the code that it suggests to developers, which is good. We
have started to add Gen AI capabilities in the quality assurance pipelines for
penetration testing, for security, for accessibility, etc., and as a copilot, which
is also an acceleration for us.

How do you plan to use Gen AI in the next few years?
We created an AI acceleration committee that currently focuses on creating
agents for specific tasks using Gen AI. We are not particularly looking at
training new models, but rather at specific use cases that will help us to
innovate. For instance, we are creating agents to configure routers. This
helps us review configuration security in our network devices at a global
level. We are also creating agents that review tickets from the security
operation center and double-check things on the system.
We believe Gen AI’s impact is going to be massive across industries and
functions.

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How do you scale Gen AI deployments?
We defined scaling at executive committee level. We have multiple work
streams and a governance committee that meets monthly. Our early steps
in creating a data fabric back in 2016 have helped us address the growing
demand for use cases based on ML. Another key aspect to bear in mind while
scaling is the risk of prompt injection attacks. If a user manipulates an answer
from an LLM about a product or service, that can be dangerous and lead
to legal challenges. So, we are training our people in what is possible and
the challenges around the maturity and security of the models. And a third
important challenge to consider is cost. For instance, we are looking at a lot
of use cases with TV. Imagine a user is watching a movie and pauses it to come
back to later. When they come back, after a few hours or days, they ask the
LLM to summarize the story so far, without any spoilers. And getting that right
is tricky and becomes a matter of cost due to the need to process a lot of data.

GENERATIVE AI AND OPERATIONAL CHALLENGES

As a global organization, what kind of new security
challenges does Gen AI bring?
Our first priority is to secure our own operations. Second, we focus
on how to increase cybersecurity operations, which is different.
This is about improving our technologies with AI-driven security,
improving our cybersecurity operations, and detecting new threats
and attacks from adversaries using Gen AI or other technologies.

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We are working a lot on
ML operations in the
contact center and the
operations center.
We are also reviewing
which areas of our
operations are most
vulnerable to new
threats, such as
deepfakes and Gen AIcreated phishing emails,
for spear phishing, voice
cloning, etc. We are also
using Gen AI to enhance
personal security.

How do you ensure responsible deployment of AI?
In Telefónica, we have a team that manages sustainability across the whole
organization as a distinct key performance indicator (KPI). We have clearly
defined principles on AI and responsible AI. This is sponsored directly by
our chairman. Our AI principles are baked into all of our actions on privacy,
accessibility, and sustainability.
I personally led a public campaign to try to eliminate the bias in the
translations that come from automated systems. For instance, if you enter
“world’s best tennis player,” the answer is always a male. If you ask for a
country manager, it's always a male. If you ask for a nurse, it's always a
female. We have been working to minimize such bias. The problem with
models is that it's almost impossible to discover if you are using an LLM or
a small language model (SLM) with bias. We try to eliminate every single
bias, especially when it's using Gen AI, because it’s very complex. Data
augmentation is going to be part of the answer but, to date, there is no clear
technical solution. It's a problem that is not resolved at industry level. We are
doing our best.

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How can organizations prepare
for successful use of generative
AI?
You need to be prepared on different
levels. You need to have a robust technical
strategy based on cloud and sound data,
and the rest will fall into place. Secondly,
you need to have strong support from top
management. At Telefónica, we have the
support of our chairman and CEO as well as
the entire executive committee.
Finally, you need sufficient budget. Once
you have that, you need to make sure that
your whole organization is very well trained
on Gen AI – what can and cannot be done.

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Chema Alonso
Chief Digital Officer
Telefónica

"It's almost impossible to
discover if you are using an LLM
or a small language model (SLM)
with bias."

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Executive Conversations

RICARDO GUERRA

CIO
Itaú Unibanco

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CREATING A
PLATFORM FOR
SUCCESS WITH
GENERATIVE AI
Ricardo joined the Itaú Unibanco Group in
1993. He is the chief information officer (CIO)
at Itaú Unibanco, Latin America’s biggest
bank, and has been an officer of the executive
committee at the Itaú Unibanco Group since
2021. As CIO since 2015, he is responsible for
the technology, data, and customer experience
(CX) department. He has extensive experience
in digital transformation, large-scale platform
management (including governance processes),
technical engineering, and cybersecurity. He
leads a broad technology team focused on
deep technical excellence, talent training, and
diversity. He is based in São Paulo, Brazil.

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THE ROLE OF GENERATIVE AI (GEN AI) IN THE FINANCIAL SECTOR

What is your view on the role of Gen AI in
financial services?
In financial services, Gen AI has transformed our approach
to business. With the release of ChatGPT, even non-technical
individuals began to get to grips with the technology. For the
first time in our decade-long tech modernization journey,
we did not need to persuade anyone to embrace modern
technology.
We believe there are three fundamental areas in which we
can use Gen AI. The first is enhancing existing AI models
and improving traditional machine learning (ML) models,
such as those for credit risk. The second is enabling hyperpersonalization of CX, where we're exploring real-time
client contextual relationship understanding to improve
interactions. Lastly, we aim to improve efficiency and mitigate
risks in internal processes. Until now, we have 250 use cases.

Ricardo Guerra
CIO
Itaú Unibanco

"For the first time in
our decade-long tech
modernization journey,
we did not need to
persuade anyone
to embrace modern
technology."

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How has the implementation and
customer adoption of your AVI chatbot
progressed?
AVI is a mature AI chatbot solution. We have around 20
million chats per month, representing 72% of the total
inbound client interactions. We achieve 75% resolution
for those chats.

"UNTIL
NOW, WE
HAVE
250 USE
CASES."

Our focus is on responsible AI. Gen AI sometimes
hallucinates, altering client names or balances. To
address this, we are developing various methodologies
using agents and other technologies. We launched
RED Studio with specific test methodologies for Gen
AI products to prevent unwanted changes. This studio
rigorously tests and pushes Gen AI to its limits.
AVI's architecture uses best-of-grid components and
reinforces reusability across all channels. We believe
that using Gen AI will accelerate the solution.

THE CHALLENGES ORGANIZATIONS FACE IN ADOPTING GEN AI

What are the biggest challenges in scaling Gen AI in a
large country like Brazil, and how do you harness talent
effectively for this purpose?
Firstly, adopting Gen AI requires a culture of innovation. With Gen AI, we
must actively engage the business and design teams, as they must identify
opportunities beyond mere tech adoption.
The second challenge is finding skilled AI professionals. Fortunately, our
recent tech transformation has helped us attract and develop talent internally.
There's a significant shortage of AI professionals in Brazil, even more so than
in the US. Despite this, we are successfully attracting and developing skilled
professionals who help us build our AI infrastructure and mindset.

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What are the challenges in the
adoption of Gen AI among employees?

The main challenge
is balancing
investment in Gen
AI with its potential
future impact."

Itaú is a tech-driven organization. Adoption was
easy, owing to a tech-friendly culture and regular
training. For example, Gen AI is being used for
reading law documents. It reads more than 70,000
documents every month, driving productivity.
However, choosing to build solutions around Gen
AI involves significant opportunity costs, affecting
current plans and budget pressures. The main
challenge is balancing investment in Gen AI with
its potential future impact.

THE ROLE OF HUMANS IN GEN AI DEPLOYMENT

What role do humans play in the deployment and
management of Gen AI?
CX is key, as we want to enhance personalization and better understand
the customer. Technology can provide a lot of data to indicate what the
customer wants.
For example, we launched a tool designed
to help our investment clients understand
how market movements, such as business
events and news, can affect their
portfolios. Through the same feature,
our clients can also receive possible
alternatives on how to deal with potential
issues related to them, or get in touch with
one of our specialists if they would rather
have human assistance.
We are building solutions with Copilot,
but we always have a human in the loop to
ensure that whatever we're building can
be processed and understood by humans.

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We are building
solutions with Copilot,
but we always have a
human in the loop to
ensure that whatever
we're building can
be processed and
understood by humans."

Executive Conversations

However, technology will
continue to advance, and
AI-managed processes can
scale much more quickly.
We have established
a team dedicated to
emerging technological
innovations, that studies
all new technologies such
as blockchain, crypto,
quantum solutions, and
the evolution of AI and Gen
AI. For instance, they’re
learning to work with
agents, a powerful solution
for controlling Gen AI and
advancing our projects.

THE CHALLENGES OF SUSTAINABILITY IN ADOPTING GEN AI

As a large organization deploying Gen AI, how do you
address the challenge of sustainability?
We continually optimize our AI models to improve efficiency by reducing
energy consumption, often using smaller models with reliable results.
We're learning when to use different solutions and emphasize investing
in sustainable data centers and green technologies. We're also closely
monitoring the market, and prioritizing providers that offer green solutions.
Lastly, we're trying to collaborate with other players and stakeholders for
industry-wide sustainability initiatives.
We're going to have to evolve in terms of energy sources and how we build
models. Many new companies, such as Mistral, for example, are building
solutions with different technologies and mindsets.

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THE REGULATIONS AFFECTING GEN AI

How do you perceive the role of regulation and
government control for organizations deploying Gen AI?
Governments struggle to keep up with technological advancements.
Organizations have to take on a lot of the responsibility for using and
governing AI and other technologies. However, governments must still
stay informed and implement supportive regulation. Many governments,
including Brazil’s, are forming expert committees to address this challenge.
Centralized control isn't feasible. Companies must enforce their own
controls and mindsets around responsible AI. But government can offer
guidance and support.

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KEY FACTORS FOR INTEGRATING GEN AI INTO ORGANIZATIONS

How important is robust data in the development and
performance of Gen AI?
From the 1980s to the 2000s, many businesses focused on completing projects
without considering the underlying platform, assuming technology would manage the
data. This led to widespread data disorganization. When we started organizing our
data, it became clear that modernizing our platform was essential. By redesigning our
architecture with microservices, we made it easier to
manage and integrate our data. Now, we have a data
mesh platform running on Amazon Web Services
(AWS) and have rewritten 60 percent of everything
we have.

If data isn't
centralized, organized,
clean, and wellgoverned, it's difficult
to use it effectively."

If data isn't centralized, organized, clean, and wellgoverned, it's difficult to use it effectively. If data
from legacy systems is copied into a SaaS database
incorrectly, it can lead to discrepancies due to
conceptual or technological issues, resulting in the
incorrect data being shown to customers.

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What are the key aspects of harnessing Gen AI?
Large language model (LLM)-based chatbots are susceptible to
manipulation. It's essential to have a multidisciplinary team engaged in
high-priority cases. Secondly, it’s about responsible AI. We have established
a committee to continuously review policies on areas such as data privacy,
fairness, transparency, and explicability, and we try to continuously build and
strengthen ecosystem partnerships. It's very important to connect with all
of the companies that are developing the technology, so we can learn from
each other.

How do you prepare a large organization to deploy Gen
AI?
The first aspect is culture. If you want to use Gen AI and have success in
adoption, make sure you're able to scale up. Hyper-personalization of the
customer journey will be paramount. In a large company, there are numerous
touchpoints to manage. Without a system to organize and understand
these touchpoints, you won't be able to attain the full potential of hyperpersonalization. We have more than 8,000 engineers using Gen AI, and have
produced more than 1.3 million lines of code using GitHub Copilot (the
third-largest amount of any organization in the world, and the largest bank,
according to Microsoft).
The second aspect has to do with responsible AI. Many companies are
creating solutions that can be easily manipulated, resulting in strange AI
outcomes. It is crucial to understand the risks and maintain control over your
developments.
Thirdly, it’s about keeping up with market evolution and innovations. It's
essential to have the right professionals and processes. Building the right
connections and network is also crucial. It's easy to get lost and fail to adopt
the right technologies. Ensure you have a network of connections that keeps
you in touch with the cutting edge of the sector, and with the competition.

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Ricardo Guerra
CIO
Itaú Unibanco

"Without a system to organize
and understand these
touchpoints, you won't be able
to attain the full potential of
hyper-personalization."

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Perspectives from Capgemini

Perspectives
from
Capgemini

104

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Gener(AI)ting the
Breathe
In(novation)—Uncover
future
Innovations that Matter

Perspectives from Capgemini

GENERATIVE AI FOR
MANAGEMENT
Elisa Farri and Gabriele Rosani
from The Management Lab by Capgemini Invent

h p.106

GENERATIVE AI: THE
ART OF THE POSSIBLE
Robert Engels
Head of Generative AI lab

h p.116

OPERATIONAL AI IS
CHANGING HOW WE
LOOK AT DATA
Anne Laure Thibaud and Steve Jones

h p.124

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GENERATIVE
AI FOR
MANAGEMENT
Elisa Farri
Co-Lead of The
Management Lab By
Capgemini Invent

Gabriele Rosani
Director, Content & Research
at The Management Lab By
Capgemini Invent

Elisa Farri is a vice president at Capgemini
Invent, and author of the forthcoming book
“HBR Guide to Generative AI for Managers”.
Included in the list of management thinkers
to watch by Thinkers50, Elisa is an expert
on management at the intersection of
academia and business. Through exploratory
research, thought leadership, and academic
collaborations, Elisa bridges the latest
management frameworks into practice.
A frequent contributor to the Harvard
Business Review, Elisa has extensive
experience developing and delivering
executive training at leading organizations
globally. Previously, Elisa was a researcher
at the Harvard Business School Research
Center in Paris, France.

Gabriele Rosani is a director at Capgemini
Invent, and author of the forthcoming
book “HBR Guide to Generative AI for
Managers”. Frequent contributor to the
Harvard Business Review, Gabriele has been
researching, designing, and testing new
management frameworks and tools for over
a decade. Gabriele works at the intersection
of strategy, innovation, and sustainability,
bringing new management practices to
the real world of business. An experienced
adviser to Fortune 500 companies, Gabriele
previously worked at the European
Centre for Strategic Innovation where he
discovered his passion for shaping the new
frontier of management.

More details on their new book are available at
https://www.capgemini.com/insights/research-library/hbr-guide-to-generative-ai-for-managers/

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The era of manager-AI co-thinking

T

he integration of generative AI (Gen AI) in business
operations is becoming more widespread, yet its
application in the realm of management1 is often
overlooked.

While executives recognize Gen AI's potential for their businesses’ operations,
they do not seem aware of its managerial potential in strategic thinking and
complex decision making. According to a survey conducted by the Capgemini
Research Institute, nearly all executives (96%) cite generative AI as a hot topic
of discussion in their respective boardrooms. However, Harvard Professor Karim
Lakhani found that less than 10% of executives use individual generative AI
tools in their daily tasks.
Talking with executives, the common
perception is that Gen AI can help managers
only in basic use cases (such as generating
summaries on subjects, creating briefs
on industry trends, distilling findings
from extensive studies and documents,
summarizing meetin

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</reference>

<statements>
1. Capgemini launched a dedicated GenAI Academy, upskilling more than 120,000 employees across its international practices
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.