#EnergyTalks – AI in Energy: Hype VS Real Impact

News

07/01/2026

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Modified on 07/01/2026

Innovation

In this eighth episode of #EnergyTalks, we head to VivaTech, where Louisa Garaud Tiar speaks with Michel Lutz, Chief Data Officer of TotalEnergies and Head of Data & AI within the Digital Factory, and Sophie Mouligneau, Head of the Start-up Program at TotalEnergies On, to discuss how AI is already creating value across the entire energy value chain.

AI, already embedded in the DNA of the energy sector

Contrary to common belief, AI does not represent a complete break; the use of data and numerical methods is embedded in the DNA of energy-related professions.

Historically, activities-particularly in geosciences and subsurface analysis-have long relied on the processing of massive volumes of data, as well as on modeling and numerical simulation techniques. TotalEnergies began leveraging these approaches as early as the 1990s to enhance the exploration and production of energy resources.

From experimentation to large-scale transformation

AI is already used across all energy production and distribution processes. In the electricity and renewables sector, it enables the analysis of satellite images to identify optimal locations for solar panels, as well as the detection of anomalies and the prediction of output thanks to the thousands of data points collected from solar and wind assets.

The real shift occurs when moving from isolated use cases to solutions deployed at scale: what starts as a technical project becomes a company-wide transformation initiative, requiring the engagement of employees, the exposure of data, and seamless integration into existing systems.

A widespread adoption across roles and functions

Artificial intelligence is no longer a niche topic reserved for a few experts. It is now emerging as a cross-functional tool, with the potential to impact all employees.

This evolution is driven by a dual dynamic: on the one hand, highly specialized solutions continue to develop to meet the needs of technical and engineering roles; on the other, AI is spreading widely through increasingly accessible tools, enabling everyone to imagine their own use cases and transform their day-to-day work.

Anticipating this shift, TotalEnergies chose early on to deploy generative AI tools at scale across its workforce, making it a lever for both performance and creativity.

Data, AI and people: the essential trio for creating value

As AI models become increasingly accessible, their effectiveness depends above all on the ecosystem in which they are integrated. Value relies on a balance between data quality, technological performance and team expertise.

Human involvement remains central: it provides the contextual understanding, domain expertise and interpretation needed to fully leverage these tools.

Major energy and sovereignty challenges

The development of AI raises significant challenges, particularly in terms of energy consumption. Today, AI is estimated to account for around 3% of global energy consumption, a figure that could rise to approximately 5-6% by 2030, according to TotalEnergies.

At the same time, data has emerged as a central issue for technological sovereignty. Its security has become strategic: the value created by AI now depends on the ability to manage the entire data lifecycle-from collection and storage to access conditions and protection. In industry, data has thus become a key lever for improving performance and accelerating digital transformation.

The key role of infrastructure: the example of Pangea

In a context where models are becoming widely accessible, data infrastructures play a critical role.

Pangea is TotalEnergies' supercomputer, designed to store very large volumes of data and perform massive computations to address energy-related challenges.

It is used for:

• numerical simulation in oil and gas;
• modeling of renewable assets, such as solar farms;
• advanced AI methods, including deep learning;
• hybrid approaches combining AI and simulation to integrate physical models.

Towards a new generation of AI

A structural shift is underway in the development of artificial intelligence: the rise of increasingly specialized agents designed to address specific use cases, moving away from generalized approaches.

At the same time, these solutions are reaching a new level of maturity. Initially used to detect anomalies or anticipate production losses-already key applications in the energy sector-they are now advancing further by being able to analyze and explain the root causes of incidents. This evolution, often referred to as causal AI, is gradually becoming a benchmark for improving operational performance.

This approach marks a shift from a reactive to a proactive model: no longer simply responding to alerts, but identifying why issues occur and acting directly at the source.

In this context, value creation also depends on the ability to effectively orchestrate these specialized agents-an essential challenge for TotalEnergies, which has made AI a central pillar of its technological ambition.

Watch the replay of this #EnergyTalk