Artificial Intelligence drives the energy transition: 16 use cases

How can artificial intelligence transform an energy sector that must simultaneously optimize consumption, reduce carbon emissions, develop renewable energies, and guarantee a sustainable system?
Artificial intelligence is now being deployed in large companies, which integrate it into network management, data usage, and power generation. From generative models to predictive algorithms, from computing centers to storage platforms, applications are multiplying and profoundly reshaping the energy industry.
In a world where every kilowatt-hour counts, where the carbon footprint of human activities threatens environmental stability, artificial intelligence presents itself both as a tool and as a structuring technology. But this revolution is not limited to theory: it is already underway in reality, with concrete projects reshaping the global energy industry.

Algorithms Serving Infrastructure: Artificial Intelligence Optimizes Heat, Storage, and Resources

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1 - When AI Becomes a Pillar of the Energy Future
At ENGIE, artificial intelligence is not a gadget but a strategic pillar. At Viva Technology 2025, the company presented more than 26 energy innovations based on data and artificial models. The idea is clear: to make energy data a resource in its own right, on par with electricity itself, and to use algorithms to streamline its exploitation.

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2 - Heat, Cooling, Data: AI Rethinks Our Urban Networks
The digital platform NEMO illustrates this ambition by integrating artificial intelligence. Covering the entire lifecycle of heating and cooling networks, it enables optimization from the design stage and better management during operation. The addition of generative capabilities stimulates tenders and reduces primary consumption by 3 to 5%, a major gain at the scale of a city.

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3 - AI Extends Battery Life
With BESS Advanced Analytics, artificial intelligence improves electricity storage. Thanks to predictive models on the state of charge and imbalance detection, it extends battery life and prevents costly losses. This is a concrete example of the impact of artificial intelligence on energy efficiency and sustainable transition.

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4 - From CO₂ to Wastewater: AI Invents Circular Energy
CryoCollect technology takes innovation even further. It captures and liquefies CO₂, transforming it into a valuable resource, while TreaTech converts industrial wastewater into syngas. These innovations show how technological solutions reinvent waste into renewable energy, contributing to a circular energy system.

Other major energy players, in Europe as well as in the Gulf, are following the same path, massively integrating artificial intelligence into their systems. But AI is not limited to reorganizing physical assets: it also transforms the internal organization of energy companies, from back office to skills management.

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When Artificial Intelligence Becomes the Copilot of Energy Giants

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5 - An AI Competence Center
Repsol illustrates well the rise of generative artificial intelligence in the daily life of large energy companies. Its Generative AI Competence Center, created in 2023, already oversees more than 400 AI use cases on a digital portfolio of 670 projects. The experimental study conducted on 550 employees showed gains of +121 minutes per week per employee, +16% quality, and strong engagement. Here, artificial intelligence optimizes the use of human resources and accelerates the energy transition by reducing the time required for support tasks.

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6 - No More Reporting, Enter Energy Optimization
At National Grid, the British electricity network operator, the integration of Microsoft Copilot and migration to Azure have automated the production of financial, legal, and regulatory reports. This frees teams to focus on real energy management: balancing supply and demand, integrating renewable energy, and reducing the network’s carbon emissions.

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7 - E.ON Bets on AI to Tame Data
E.ON, for its part, faces a massive influx of energy data linked to decentralized production in Germany. Its 80,000 employees now use Copilot for Microsoft 365 to automate writing, document research, and meeting transcription. The impact is clear: better data management, time optimization, and an increased ability to innovate in sustainable energy development.

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8 - An AI Lab to Boost Trading, Maintenance, and Customer Satisfaction
Uniper follows the same logic with its AI Lab created in partnership with Microsoft. Objective: use generative models to improve energy trading, facility maintenance, and customer satisfaction. As early as 2024, the company observed an +80% increase in productivity in its audits thanks to automation generated by Copilot.

These internal gains are decisive, but artificial intelligence also changes the way companies manage their customers and networks on a daily basis.

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Reinventing Customer Relations and Networks Through Artificial Intelligence

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9 - A Virtual Assistant to Reinvent Energy Customer Relations
AWS has developed an Energy Virtual Assistant that illustrates the real use of generative models for utilities. Capable of handling thousands of requests related to billing, consumption, and network incidents 24/7, it reduces costs and increases customer satisfaction. But the impact goes beyond traditional customer service: the assistant helps relieve call centers, collect more accurate data on energy usage, and identify real-time consumption trends. This information then feeds predictive models that help network operators better balance demand and supply, a central issue for integrating more intermittent renewable energies. Designed as a digital copilot, the tool accelerates the transition to smarter, more responsive, and more sustainable networks.

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10 - Energy Resilience: AI Strengthens Infrastructure
In Germany, EnBW deployed solutions from Futurice and VIER Copilot. Result: –20% in information search time and –60 seconds in call processing time. But the initiative goes further: by harnessing the power of algorithms, EnBW reduces the cognitive load of agents, who can focus on more complex and higher-value interactions. Customers benefit from shorter response times, but also from a smoother, more personalized experience. In addition, the data aggregated by AI allows EnBW to identify demand peaks related to network incidents, anticipate repairs, and strengthen infrastructure resilience. This optimization of contact points directly contributes to the overall efficiency of the energy system.

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11 - Octopus Entrusts Its Emails to AI: A Winning Bet
At Octopus Energy, artificial intelligence already processes one-third of customer emails, equivalent to the work of 250 employees, with a satisfaction rate of 80% compared to 65% for human responses (Business Insider, 2023). This use case illustrates AI’s ability to absorb a massive workload while improving perceived service quality. For Octopus, the impact goes beyond productivity gains: human teams are redirected to solving complex problems, while AI continuously learns to refine its responses based on customer profiles and their energy consumption behaviors. At the same time, automation reduces the company’s indirect carbon footprint by decreasing the need for material and software resources to manage support. This hybrid model, where AI handles most repetitive tasks, is becoming a strategic lever to support the rapid growth of distributed renewable energy.

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12 - When Copilot Raises Social and Ethical Questions
Finally, EDF illustrates the other side: the deployment of Copilot in 2024 sparked debates on data security, psychosocial risk management, and organizational impact. Here, artificial intelligence also raises social and ethical issues, reminding us that the energy transition must remain human-centered.

Beyond customers and the back office, artificial intelligence is establishing itself in the design of large projects and in the development of global energy networks. Artificial intelligence is becoming a lever for innovation in the design of major projects and the exploration of new resources.

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Designing the Energy Future with Generative Artificial Intelligence

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13 - ENERGYai, an AI Agent for Massive Geological Analysis
ADNOC and AIQ unveiled ENERGYai, an agentic artificial intelligence system capable of processing petabytes of data from 80 years of geological history. Result: +70% accuracy in seismic interpretation and a reduction in CO₂ storage project planning from 1–2 years to just a few weeks. A striking example of generative technology serving the real energy industry.

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14 - Lumi™ Revolutionizes Energy Exploration
SLB (Schlumberger) launched in 2024 the Lumi™ platform, which unifies energy data from subsurface to surface. The integration of generative models enables accelerated optimization of power generation systems and better network management. The innovation lies in Lumi™’s ability to cross-analyze billions of geological, technical, and operational data points to propose optimized scenarios for exploration and exploitation. Concretely, AI reduces the time needed to analyze an energy field from several months to a few days, while lowering error margins. It thus helps secure CO₂ storage projects, improve yields of existing wells, and guide investments toward more sustainable projects. For SLB, Lumi™ is also a competitiveness tool: it allows data analysis to be standardized, transparency with regulators to be reinforced, and the transition to a low-carbon energy mix to be accelerated.

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15 - Generative AI Serving Offshore Wind
Ørsted uses generative artificial intelligence to respond to offshore wind tenders, a field where speed and quality of submissions are decisive in winning strategic projects. Thanks to AI, the company saves hundreds of hours of manual work in preparing technical and financial documents. Generative models also improve the accuracy of cost forecasts, the simulation of energy scenarios, and the integration of regulatory constraints. AI is also used to model maritime conditions, anticipate environmental impacts, and adapt engineering solutions accordingly. Result: Ørsted strengthens its competitiveness in a highly competitive market while accelerating the deployment of renewable capacity. This case clearly shows that artificial intelligence is not limited to optimizing existing systems, but becomes a catalyst for developing critical infrastructure for the energy transition faster and more efficiently.

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16 - An Industrial Copilot for Leaner, Faster Factories
Finally, Schneider Electric launched in 2025 an Industrial Copilot developed with Microsoft. Integrated into the EcoStruxure platform, this copilot reduces engineering time by 50%, accelerates automation, and enables 20% energy savings in certain factories. The impact is immediate: better resource use, reduced carbon footprint, and acceleration of sustainable development.

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A clear conclusion emerges: artificial intelligence has become an essential lever of the energy transition. It enables better data exploitation, network optimization, improved energy efficiency, reduced carbon emissions, and boosted renewable energy development.
These innovations are not just promises: they are already producing measurable real-world results. But they also raise governance questions: how to control the footprint of computing centers, how to manage water consumption for cooling, how to calibrate artificial intelligence models so they serve the sustainable transition rather than hinder it.
The real question now is: how to optimize an “energy budget of artificial intelligence” per model, per network, per system to ensure that the net impact on the environment, resources, and society is positive?