Energy
AI accelerates electricity demand, prompting a new wave of grid adaptation and investment
Press contact:Florence LievreTel.: +33 1 47 54 50 71E-mail: [email protected] AI accelerates electricity demand, prompting a new wave of grid adaptation and investment Nearly 80% of utilities expect more extreme and volatile demand patternsAround one in five data center power requests may never materialize, distorting forecastsAt the same time, AI is expected by 60% of utilities to play a growing role in improving grid efficiency and unlocking operational gains, but only few have imp
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Press contact: Florence Lievre Tel.: +33 1 47 54 50 71 E-mail: [email protected] AI accelerates electricity demand, prompting a new wave of grid adaptation and investment Paris, June 25, 2026 – The rapid expansion of AI-driven data centers is not only increasing electricity demand, but making it significantly harder to predict, challenging how power systems are planned and delivered. A large majority of electricity executives expect more extreme and less predictable demand spikes, while more than three quarters say they struggle to forecast future needs accurately, according to the Capgemini Research Institute's latest report, AI meets the grid: shaping the data center power play . The research, which surveyed over 600 senior electricity executives from organizations with annual revenue exceeding $500 million, highlights how power systems are entering a new phase as AI workloads become increasingly unpredictable. According to the report, forecasting has become significantly harder, but AI is also part of the solution with a majority of executives expecting AI to unlock significant efficiency and operational gains. A new era of volatile and uncertain electricity demand Beyond growth, the bigger challenge is uncertainty. Utilities are increasingly planning for demand that may never materialize. The report highlights a growing disconnect between projected and actual demand: a majority (67%) of electricity executives refer to "phantom" data-center load requests, with around two in ten (19%) of them never materializing, distorting forecasts and increasing the risk of both over- and under-investment. This forecasting uncertainty creates a significant capital allocation dilemma. Utilities must decide not only how much capacity to invest in, but where and when to prioritize grid modernization investments to support future demand while avoiding stranded assets. For hyperscalers, the challenge is equally acute, requiring major infrastructure decisions to be made against a backdrop of uncertain demand forecasts, grid availability and connection timelines. Furthermore, over three-quarters (77%) of utilities are forecasting difficulties predicting future demand accurately, as consumption patterns from AI become less stable and more difficult to model. As a result, they expect demand variability to emerge as a major system challenge, requiring new approaches to planning and operations.