Apidays Paris 2023 - Software and APIs for Smart, Sustainable and Sovereign Societies December 6, 7 & 8, 2023 AIvolution or AIPocalypse Cyril Vart, Fabernovel, an E&Y company ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

1. AIpocalypse or
AIvolution ?
Generative artificial intelligence
is changing jobs, and mostly
everything else.

2. The first “Plug&Play”
industrial revolution.

3. We ask our smartphones :questions:
It helps us :choose a film to watch: in the evening
It helps us make medical decisions:
We use it for our :banking:
We use it for :directions:
We use it on our daily journeys to get from A to B
AI was already part of our everyday lives

4. AI, a revolution underway. Has the time finally come (now)?
Sources: New York Times, ADN, CBSnews
(And why the answer
is probably yes)

5. Of course — deus ex machina?
In popular culture and
science-fiction, AI is often
portrayed negatively — with
conscious, defiant or evil
powers… set on ruling the
world, instilling fear and
fantasy.

6. April 2022 November 2022
March 2022 July 2022 March 2023
Evolution of Images Generation, in 1 year

7. In 1956, the Dartmouth summer
research project was organized
on Artificial Intelligence.
A brief history of Artificial Intelligence
EXPERT SYSTEMS
Each decision-making rule is
explicitly integrated into the
machine through code.
MACHINE LEARNING;
A range of techniques that
enable the machine to learn
by example
DEEP LEARNING;
Advanced
techniques using
deep neural
networks
GENERATIVE AI
Automatic
content
generation and
autonomous
learning
1950’s 1960’s 1970’s 1980’s 1990’s 2000’s 2010’s 2020’s
In 1997, IBM Deep
Blue defeated
Kasparov, the
world champion.
In 2016, AlphaGo defeated
the professional Go player
Lee Sedol.

8. Speed or acceleration ?
Hand writing recognition
Vocal recognition Image recognition
Text
understanding
Language recognition
AI vs. Human
AI above human
performances
AI below
standards
Human performance baseline
Normalized average AI systems
performance 2017 - Transformer models
2013 - Word-Vectors

9. Generation
Data collection
9
GENERATIVE AI Automatic learning and content generation
Generative AI refers to Artificial Intelligence and Machine Learning algorithms that use existing content to
generate new content as part of their learning process.
Training
Text
Images
Sound
Textual content generation
Information extraction
Image generation
Image description
Objects recognition
Vocal recognition
Voice generation
→
→
→

10. 16 years
7 years
10 years
2 months
AI: from a niche market to a mass market tool
ChatGPT, 100 million users in 2 months
ChatGPT
Initially designed for specialised
applications and niche markets, AI has
transformed into a mass consumer
product.
What was previously the domain of
experts and researchers is now
available to the general public with
several hundred million users.
WhatsApp
Internet
Netflix
(streaming)
Mobile phones
Sources: World of Statistics, CNBC, EY Fabernovel
3.5 years

11. You said Generative AI ?
It’s not all about ChatGPT
MIDJOURNEY
3D & Gaming Code
Text Image Video Sound
Bing Chat

12. The best website to find AI solutions
For every need, there's an AI to meet it (well, almost every need)

13. Cas d’usage

14. From…
The integration of lines of code
by a human so that the
machine produces a result
Natural
language
Automatic content generation
by machine self-learning
Communicate with AI using
simple sentences i.e. “prompts”
A tool which can be accessed
by all and partly free for the
general public
The need to master coding
to communicate with AI
Costly software needs installing
on specific machines
From query to deliverable, a first-ever for AI
Barrier
to entry
To…
Content
generation

15. Complex and
dangerous
soldering tasks
Automatic transcription
of meeting minutes
or medical appointments
Document
research
Radiology
analysis
AI: from technology to a job
AI
Natural language
processing
Machine Learning Spoken language Robotics Vision
Deep
learning
Text
classification
Text to speech
Industrial
robotics
Troubleshooting tools
for technicians
Speech to text
Image
recognition
Autonomous
vehicles
Predictive
analysis
Automatic
translation
Information
extraction

16. But requiring human supervision to work
Generative AI
● Ingests data
● Processes the information quickly
● Recognises shapes
● Suggests a probabilistic approach
(and sometimes aberration,
false information)
● Optimizes
Human
● Understands the context
● Abstraction / concepts
● Critical thinking
● Empathy
● Emotion
● Innovation

17. →
A change in your todo’s,
rather than mass job
destruction…

18. Will AI cause mass job
destruction?
(And why the
answer is
probably no)
The 5 observations of EY Fabernovel
which lead us to believe that
generative AI will not be a major
driving force in destroying jobs:
1. Artificial intelligence is already
at play in the workplace
2. Now white-collar workers are
creating the traction
3. AI is changing the list of
tasks rather than taking over
a job entirely
4. AI improves the employee
experience
5. AI is driving new opportunities
for companies and employers

19. How generative AI is transforming our daily work environment
Generative AI can expand our
capacities to optimise work:
by doing repetitive and
time-consuming tasks and
exploring new tasks which
were difficult to carry out
without changing the time
and resources allocated.
Note taking Summarising
information
Formalising
documents
and deliverables
Document research
and access to
information

20. And is already
part of many
work applications
Microsoft uses a new
conversational agent in Word,
Excel, PowerPoint, Outlook, Teams
and OneNote: Microsoft Copilot,
which is capable of processing files
and creating content.
Generative AI is now included in lots of
professional software. Some businesses
already use these tools on a daily basis. For
example marketing agencies use them to
produce different customised advertising
banners.
However, the current uses of existing or
future tools have not yet become
widespread. But adopting these new uses
is likely to accelerate thanks to the
firepower of some tech giants, for example
the deployment of generative AI in
Microsoft Office — which makes up for 46
% of desktop software used by corporates.
Zoom offers real-time transcription
of meetings, generating text from
the audio and automated minutes.
Since November 2022 Notion includes
a conversational feature which can
draft new content, summarise long
documents and extract key
information from disjointed notes.
Source: BDM/Statista

21. Second round: after blue-collar workers, now it’s white-collar workers’ turn
Unlike previous industrial
revolutions which affected blue-
collar jobs or lower qualified jobs,
this time round generative AI is
affecting white-collar office tasks.
But this will probably also mean
upskilling of these office jobs thanks
to the automation of certain
less fulfilling tasks repositioning
employees towards advisory tasks
involving greater expertise.

22. Middle management: women are particularly affected
A report published in August 2023 by the UN’s
International Labour Organization (ILO)
suggests that automation due to generative AI
will have a greater impact on higher income
countries, and more specifically office jobs.
5.5% of jobs in these wealthy countries are at risk,
vs. only 0.4% in lower income countries.
The impact on jobs will also affect women more,
as they make up a larger proportion of office
jobs. Indeed, the automation risk is twice as high
for women than for men, especially in higher
and middle-income countries.
Automation potential vs augmentation potential:
shares of total employment ILO

23. Evolving the tasks rather than transforming jobs
Generative AI is more likely to
transform certain tasks rather than
the jobs per se.
Generative AI will enable us to
modify the way we carry out
certain tasks. But will also allow
upskilling via new tasks requiring
human analysis, creativity and
critical thinking.
“I see all these tools as assistants for some of the
tasks, the tasks are replaced but not the jobs.
A job requires skills in human relations, politics,
and reporting which will never be automated.”
Pierre-Yves Calloc'h, Chief Digital Officer
at Pernod Ricard, during an interview with EY
Fabernovel for this study “AIpocalypse
or AIvolution?” (2023)

24. How will my job be affected?
5 questions to consider to see whether AI will steal my job
1. Can my job be easily split up into
simple and well-defined steps?
2. Is my job made up of several
repetitive tasks?
3. Does my job require creative,
critical or strategic thinking?
Yes No
Yes No
Yes No
4. Does one of my roles require
analysing results produced from
data?
5. Does my job require the ability
to perceive and respond to
emotions?
Yes No
Yes No
Job certified
at risk

25. Generative AI will have a moderate impact for a large proportion of jobs
Low or no impact
Example: piano
tuner, wine merchant,
carpenter…
High impact for jobs
depending on the nature
of their daily tasks.
Example: notary clerk,
data scientist, marketing
consultant…
Moderate impact on some tasks
for the majority of professions whose
core taks
will not change.
Example: teacher, salesperson,
telephone operator, financial analyst,
lawyer…
Source: Orders of magnitude estimated by EY Fabernovel
Generative AI changes impacting jobs
Number
of
jobs
impacted

26. A job is merely a list of
tasks that the
employee masters
Main tasks which
define her job:
1. Review financial data and prepare
annual and monthly reports.
2. Determine long term financial forecasts.
3. Analyse market trends to identify
investment opportunities.
4. Work with management teams
to develop financial strategies.
5. Monitor financial performance
and point out discrepancies vs. forecasts.
6. Study economic and sector regulations to assess their
impact on company business.
7. Submit financial recommendations to management.
Her job
Let’s use Victoria,
a financial analyst
as an example→

27. Tasks where generative AI can be used
Changes in the tasks of Victoria’s job
Generative AI can simplify a certain
number of tasks and save time for other
higher value-added assignments.
Her job will migrate towards tasks
which require intrinsically human
skills such as critical thinking
and communication.
Review financial data and prepare annual
and monthly reports.
Determine long-term financial
forecasts.
Analyse market trends to identify
investment opportunities.
Study economic and sector regulations to
assess their impact on company business.
Work with management teams
to develop financial strategies.
Study economic and sector regulations to
assess their impact on company business.
Submit financial
recommendations
to management.

28. How will Generative AI impact the future of enterprises?
Generative AI will bring significant transformations at almost every level
Supply
HR
IT
Finance
Marketing
CRM
Sales
Today
Medium
term
Long
term
Design products
Write product
descriptions
Create
purchase orders
Scan CVs
Preselect
candidates
Write job descriptions
Respond to
candidates' questions
Write employee
evaluations
Generate, fix,
comment on code
Summarize files
Document the
code
Detect spam
Suggest system
performance
improvements
Check consistency
Provide insights
Benchmark costs
Identify potential
savings
Identify attractive
markets for expansion
Brainstorm
marketing ideas
Create
advertisements
Personalize
advertisements,
newsletters, etc.
Create a fully
individualized customer
journey
Analyze customer
options
Respond to
customer complaints
Proactively
approach inactive
customers
Call cold prospects
Customize sales
pitches
Offer
promotions
Support
customers
Create product videos
Create training material

29. New professional opportunities
as new jobs emerge
Head of AI
and Automation:
Will determine the strategy, implementation
and development of AI and automation
initiatives within the company, identifying
opportunities where these technologies can
improve and optimise existing tasks and
processes working hand-in-hand with
company teams.
Responsible AI use Manager:
This manager is responsible for developing,
implementing and adopting responsible use
of AI systems within the company, whilst
taking into account issues such as the
company’s Code of Ethics, data
transparency, reliability and traceability of AI
generated data and content. They can also
measure the psychological impact of the use
of AI on individuals.
Specialist in Art valuation and AI digital content:
This professional values, selects and recognises the value of works of art
generated by AI, and is also capable of detecting and analysing
deepfake content. As a digital art conservationist, they organise art exhibitions
for works created by AI tools, whilst ensuring their authenticity. Given their
in-depth knowledge of image and video generation mechanisms, they are
also responsible for checking that the digital content is authentic, often aided
by AI tools, to protect the public from misleading information.
Knowledge base expert:
To take advantage of full potential available from generative AI within a
company, Knowledge base experts are needed. Generative AI is based on
processing existing data, therefore internal governance rules and
processes are essential for listing, centralising and updating the vast
quantities of available information.

30. 30
To harness the
potential of
generative AI, you
must know how
to :speak to the
machine!:

31. →
You said predictive ?

32. Companies are already significantly assisted by generative AI
In the short-term, the impact
of generative AI will occur
mainly via “internal”
innovations which improve
what we already do. But we
will probably also see
companies appearing whose
business model is entirely
based on generative AI
technologies.
Zelda in an infinite Open World
For example, in the video game sector, generative AI could be used to create
new game levels in real-time, offering an unlimited “open world” experience.
Advertising agency using generative creation
An advertising company could use generative AI to create customised
visual content (models, photos, paintings…), thereby reducing costs
and design time.
Investment funds based on AI
In the areas of decision-making, AI could minimise discrepancies and
increase efficiency, opening the way for more lucrative business models.
Specialised companies and ESN 2.0 with expertise in generative AI
are emerging, offering services varying from training to maintenance.
Supporting job transformation will also be crucial, involving the
development of training modules to acquire the new skills needed to work
with this technology.

33. The near future will see job verticalization and
enhanced IT sectors
● The requirements of generative AI in IT infrastructure, namely as regards
hardware, the cloud and infrastructure, strengthen companies in this
industry such as Nvidia.
● General AI solutions such as those offered by the digital giants can be both
costly and risky. We see companies such as Mistral and IMOK emerge, who
specialise in job verticalization and generative applications
for specific sectors.
● The ecosystem of design, installation, and maintenance is currently
creating new jobs. Roles such as “Prompt Engineer”, “AI Integrator”
and “Generative Experience Designer” are emerging to solve ergonomic
or user experience issues and rationalise queries in the field of generative
AI within organisations.
● A boost in productivity for freelance jobs such as lawyers, consultants
and graphic designers. AI also opens new opportunities in training,
promoting the acculturation of emerging technologies.
In the medium-term,
generative AI will increase the
demand for advanced
infrastructures, boost service
jobs thanks to specialised
solutions, and create a need for
experts within companies.
AI is not only a technological
breakthrough, but it will also
stimulate a new way of
defining the needs and roles in
different sectors.

34. The long-term future will see generative AI and quantum computing
used together opening up opportunities for advanced business
applications
Building
synthetic brains
Autonomous
space probes
AI trained to help
handicapped
children
Manikins to
assist firemen
Artificial
intelligence
motivator
The future combination of generative AI and
quantum computing promises major advance
with higher computing powers. These two
technologies used together will enable the
creation of highly efficient AI assistants, more
advanced business applications and machines
capable of operating totally autonomously. They
will adapt their work to fit the context, making
business systems more agile and responsive. For
example, in the future could we develop new
vaccines in the space of a couple of days when
faced with a new virus?
Furthermore, progress in building synthetic brains
using quantum computing could challenge our
conceptions of creativity and intelligence, whilst
spurring important ethical issues regarding the
control of such advanced technologies.
The end of
Moore’s law

35. →
Social, environmental and
business issues should
guide the use
of generative AI, now.

36. Will companies opt for the rebound effect or the 4-day week?
Generative AI will improve
employee productivity, saving time
durably provided there is an
internal conversation about the
correct ways of using this time,
implementing tools and setting up
training programmes.
The time gained will drive
companies to make important
structural and strategic decisions:
will they adopt the Jevons paradox
(rebound effect) or decide to
embrace the 4-day week?
Regardless of the rebound
effect of increased
productivity, there is huge
potential for greater added
value which companies
must decide how to share
out fairly between
employees, clients,
investors, and the planet…
Now is the time to ask the
right questions.
Indeed, the Jevons paradox implies
that the use of this technological
breakthrough may lead to increased
use to stretch employee efficiency
even further encouraging them to
complete even more tasks in the
same amount of time.
On the other hand, companies may
also choose to reallocate this time to
promote a better work-life balance
to improve staff retention and
promote employee well-being.

37. The big reward is better skills, the danger is downskilling
Thanks to artificial intelligence the time
spent on repetitive tasks has now been
freed up, this time can now be used to
maximise reskilling and upskilling.
Companies can now choose to use the
time saved to accelerate the
development of their employees’ skills,
company wide.
This new model placing the employees as
learners at the centre of the process has
significant resource and cost implications,
requiring initial investments in training
programmes and advanced teaching tools.
The long-term impacts for businesses are
positive: a more skilled and more versatile
workforce. Employees will be better equipped
to face the challenges posed by the market,
to innovate and, all in all to contribute to the
company’s success.

38. Social impact
The digital divide
may widen
Are we moving towards employment
polarisation?
As more and more tasks are automated, will
middle-skilled jobs gradually disappear?
This trend could lead to a society where the winners
will be those who use these technologies with ease
while the others will be pushed aside, as their skills
are made obsolete. The danger lies in the fact that
not everyone will benefit fairly from this revolution.
If we do not succeed in training and upskilling
everyone faced by these new technologies, the risk
is that we create a profoundly unequal society,
where only a select few will reap the benefits of AI .
“Employment polarisation is a real risk. AI will destroy
middle-class jobs and managers with added-value roles
will reap the benefits at the expense of lower paid, less
skilled employees. AI will decimate middle managers if
they are not retrained.”
Arno Pons
Delegate General
Digital New Deal Think Tank
“The greatest risk is that not everyone benefits
from these transformations and that we do not have
the means to upskill everyone.”
Pierre Deheunynck,
Chairman of the Board
of France compétences,

39. Whilst the technological feats of
generative AI are indeed impressive, their
environmental impact is by no means
meagre due to the huge amount of
energy, water and electronic components
such as graphic cards, that they consume.
There is little or no transparency from AI
companies as regards their energy
consumption so the calculation of their
model's carbon footprint cannot be easily
benchmarked.
The environmental impact of these
models is far from neutral
Environmental impact
Sources, research papers: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, ESTIMATING THE CARBON FOOTPRINT
OF BLOOM, Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models, Carbon Emissions and Large
Neural Network Training
However, independent studies have done so and have published
consistent outcomes:
● 700,000 litres of fresh water to cool down servers and 552 tonnes
of carbon emissions just for training ChatGPT3, which is the
equivalent of 205 return flights between Paris and New-York
● plus the daily impact of users’ daily queries must be added:
500ml of water for every 20–50 questions on ChatGPT3, whilst
the carbon impact has yet to be calculated
● plus regular retraining with new data.
One solution could be that each company creates its own specific
uses, more selective databases and language models
to replace these greedy general AI applications.

40. Generative AI may intensify
cyber-risks
Generative AI technologies will
increase the risks of cyber crime in
many areas: creating content for
phishing e-mails, manipulating
models by poisoning data which
alter the algorithms and produce
false data, violation of personal
data, creating deepfakes, theft of
intellectual property or malicious
use of content generated for
phishing, amongst others.
Business impact
To prepare for these new risks and threats , several
steps need to be taken but will probably not provide
sufficient protection:
● Review or adapt cybersecurity processes
and protocols
● Strengthen authentication to avoid new cases
of identity theft.
● Review backdoor and front door protection
measures to deal more efficiently with password
hacking by generative AI technologies
● Constantly retrain staff on the new risks faced
by the company’s daily use of AI.

41. 1
Generative AI…
2
Will change employment tasks but
will not massively wipe out jobs.
Will evolve organisations in the long-term.
Costs, complexity, and the inertia of large
corporates to efficiently deploy strongly
impacting new technologies being the main
reasons, much like structural digital projects
which take three to five years to complete.
3
Stretches beyond the mere technical
deployment, impacting companies’
strategy, their economic model and their
organisation. Now is the time to ask the
right questions and reflect on how AI should
be integrated into governance.
4
Requires a systemic approach involving several
business lines working together rather than
embarking on isolated initiatives. An approach
which does not require radical change, simply
because the most relevant and impacting uses
are still to be identified.
5
Makes way for upskilling.
The benefits of generative AI make investments in
employee training possible so as to develop their
expertise, increase their engagement and offer
new opportunities both for them and their
employer. Talents who can boast proficient use of
AI tools and technologies and AI skills will have a
strong competitive advantage for recruitment in
tomorrow’s world.

42. Knowledge
Democratize access to
knowledge with few
questions, enabling an
understanding of the realm
of possibilities and fostering
a mindset of continuous
learning.
Time saving
Save a considerable
amount of time and focus
on delivering value and
quality service to the
customer.
Self-improvement
Allows us to practice and
self-assess by simulating
questions on the issues
encountered.
Hyper-personalisation
Enables the creation and
display of content and
solutions that precisely
address the needs expressed
by the user.
Keep in mind
The benefits of Generative Artificial Intelligence

43. EY | Building a better working world
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Enabled by data and technology, diverse EY teams in over 150 countries provide trust
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Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask
better questions to find new answers for the complex issues facing our world today.
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of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young
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Information about how EY collects and uses personal data and a description of the
rights individuals have under data protection legislation are available via ey.com/privacy.
EY member firms do not practice law where prohibited by local laws. For more
information about our organization, please visit ey.com.
© 2023 EY Fabernovel – All rights reserved.
SCORE France N° 2023-084 - ED None
Credits: Midjourney/@hGabha, Netflix, Adam Nadel/AP Images, Universal
This publication has been prepared for general informational purposes only and is not
intended to be relied upon as accounting, tax, legal or other professional advice. For
specific questions, please contact your advisors. ey.com/fr
About
EY Fabernovel
EY Fabernovel is a leading international
provider of consulting services for strategic
transformation and the creation of innovative
services.
Fabernovel was founded in 2003 by Stephane
Distinguin, and became EY Fabernovel on 5
July 2022, following a merger with EY
Consulting to become the leader in the
convergence of digital and ecological
transitions in Europe.
The multidisciplinary teams include
developers, designers, creatives, analysts, data
engineers, marketing specialists bring their
convictions and solutions across the entire
digital value chain, from the consultancy stage
through to the completion of products for
daily use, and on to marketing campaigns and
promotion of transformation strategies.
The team: Stephane Distinguin, Robin
Bruniaux, Benjamin Fallot, Cyril Vart, Diego
Ferri, Joachim Martin, Yi Cao & Marine Chatras.
External contributors: Pierre Deheunynck,
Arno Pons, Pierre-Yves Calloc'h.

44. Thank
you!