Energy providers are flying blind thanks to unpredictable AI data center demands
Research from Capgemini has found that uncertainty, speed constraints, and rising system complexity are leaving firms struggling to predict future consumption
Three-quarters of utility firms are struggling to forecast energy demands due to rapid data center infrastructure expansion.
A new study from Capgemini Research Institute suggests that eight-in-ten utilities predict more extreme and volatile demand patterns. A key factor here is that consumption patterns from AI are less stable and more difficult to model.
The biggest problem is uncertainty, the study noted, with utilities increasingly forced to plan for demand that may never materialize.
Two-thirds of electricity executives refer to “phantom” data center load requests - with 19% of these never materializing.
This forecasting uncertainty creates real problems with capital allocation, Capgemini warned, with utilities having to decide not only how much capacity to invest in, but where and when to prioritize grid modernization investments to support future demand.
Hyperscalers are feeling the pinch
Things are just as bad for hyperscalers, which need to make major infrastructure decisions against a backdrop of uncertain demand forecasts, grid availability, and connection timelines.
The problem is made all the harder by the geographic concentration of data centers, which places significant strain on local grids.
More than half of electricity executives are finding that load concentration is a major obstacle to reliable service, while large clusters of high-density facilities are creating localized bottlenecks that affect system stability and investment planning.
“AI is transforming electricity systems far beyond demand growth. It is exposing structural constraints in grid capacity, planning, and power availability, while making demand more dynamic and harder to predict,” said Claire Gauthier, global head of energy & utilities at Capgemini.
“The challenge is no longer only how much power is needed, but whether it can be delivered reliably, where and when it is required. Utilities have a defining role to play as system orchestrators, leveraging AI-enabled insights to balance grid and customer-owned resources, accelerate deliverable capacity, and enable the next phase of data-center growth.”
What’s driving energy consumption?
The projected increase in electricity consumption is largely down to AI training and inferencing, according to Capgemini.
Training and inference processes currently account for around 25% of data center energy consumption, yet this is projected to reach 60% within the next three to five years.
The consultancy noted that this not only places significant strain on data center infrastructure, but also leaves other IT workloads on the sidelines as enterprises prioritize AI-related activities.
Data centers are increasingly shifting from backup-only approaches toward primary behind-the-meter (BTM) and near-site solutions. Nearly three-in-ten say they already deploy on-site power solutions and 39% plan to add on-site/BTM within the next couple of years.
Operating independently
Looking ahead, the majority (86%) of respondents see the ability to operate independently from the grid as a competitive advantage.
Around three-quarters of utilities and data center executives told researchers they were looking to try and establish a diversified energy mix to ensure reliability and long-term resilience.
Part of this lies in the fact that renewable energy alone cannot provide enough continuous power at scale for large data centers and AI workloads.
Battery energy storage systems (BESS) are emerging as a possible solution, with nuclear Small Modular Reactors (SMRs) expected to take time to deploy.
More than two-thirds of electricity and data center executives globally are looking to natural gas as a near‑term, transitional solution until renewable energy and storage technologies can scale.
"For both energy providers and data-center operators, the key challenge is no longer only scaling capacity, but doing so under uncertainty, speed constraints, and rising system complexity,” said Gauthier.
“Success will depend on the ability to align infrastructure investment, energy sourcing, and AI-enabled operations to manage both the scale and volatility of demand, while balancing reliability, cost, and sustainability.”
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