WEMO24-Compiled Report-Interactive compressed (002) 4154-7882-8116 v.1 - Flipbook - Page 59
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Generative AI technology is mind blowing but not limitless,
despite its rapid progress.
2. Even though building demonstrators is easy, scaling is hard.
It requires quality data, integration with core IT, high levels
of performance and change management.
3. High-value use cases are relatively few in number and
require more than just a multi-agent based, multimodal
large language model (LLM) to unlock value.
4. Gen AI can create tremendous value in the energy field,
especially as it relates to energy transition.
Philippe Cordier
Vice President and Chief Data Scientist,
Capgemini Invent
Philippe leads the AI and Data Engineering practice
as well the Capgemini Invent Research and
Innovation Lab. Before joining Capgemini, he has
worked for major multi-energies companies for
more than 15 years.
Introduction
Even though Gen AI gained widespread public attention with the
release of ChatGPT in 2022, its origins date back to the 1950s
and extend far beyond language models in all their variations. It
truly began to take off in the mid-2010s with the development
of GANs, the first technology used to create deepfakes.
In 2023, there has been a bit of science fiction around the
potential of the technology, be it in terms of impact on the
global economy or the job market. After a year of frantic
exploration, companies realized that scaling Gen AI is hard.
It’s time for for them to reset their approach with respect to
Gen AI and remember the hard lessons learned from digital
and AI transformation: steer by value; upskill people; set up
the technology architecture; ensure data quality; and commit
to the ethical and responsible development and use of Gen AI.
Moreover, the transformation brought by Gen AI should not be
considered in isolation but alongside broader AI transformation
efforts, with a strong emphasis on change management. Only
by integrating these elements can the full value potential be
realized, enabling deep process re-engineering and enhancing
organizational efficiency.
In the energy industry, particularly in the context of the
energy transition, Gen AI has a crucial role to play. It can act
as a catalyst for discovery of e-fuels, contribute to the design
of new batteries or wind turbines, advance synthetic biology,
and provide augmented insights from multiple data sources to
support better-informed decision-making.
WEMO 2024
The Realistic Case for
Generative AI
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