Capgemini: Utilities Cannot Predict the Energy Demand of AI

New research from Capgemini reveals that the vast majority of utilities are unable to accurately forecast the energy demand created by the rapid expansion of AI-driven data centres.

The report found that 77% of utilities are struggling to predict this demand.

Electricity consumption from AI training and inferencing is set to rise from 25% to 60% of total data centre electricity use within three to five years.

This shift is already displacing other IT workloads and forcing energy providers to rethink how they plan and operate the grid.

Rethinking grid planning for an ai-driven future

Claire Gauthier, Global Head of Energy and Utilities at Capgemini, says the energy sector is being reshaped by AI in two directions at once.

"AI is reshaping the energy landscape, both accelerating electricity demand and creating new opportunities to improve how energy systems are planned, managed and optimised," she says.

Claire adds that the task ahead goes beyond simply adopting AI tools.

"The challenge facing the industry extends beyond adopting AI. It is embedding intelligence into the way organisations operate, enabling them to continuously adapt, make better capital allocation decisions and respond more effectively to an increasingly volatile environment," she says.

Unpredictable workloads pose a system-wide challenge

Forecasting has become especially difficult because AI workloads do not follow predictable consumption patterns.

A company training a new foundation model might see a multi-week spike in compute demand.

This can then be followed by a sudden drop to a baseline level that fluctuates depending on real-time user queries.

Utilities now expect this demand variability to become one of the biggest challenges for grid planning and operations.

Will AI make grids more reliable?

Capgemini surveyed 600 electricity executives for the report.

The majority see AI as a way to strengthen grid planning and reliability rather than just a source of new demand.

Six in ten expect advanced AI analytics to deliver improvements of more than 10% in failure reduction, operational productivity and outage prevention and restoration.

However, only 45% of utilities are currently using AI for grid optimisation.

Just 16% of electricity organisations have implemented advanced AI-driven approaches to optimise power flows.

"For the first time, organisations have the opportunity to optimise the energy value chain end to end by bringing together engineering expertise, operational technology, digital capabilities and AI within a single operating model," Claire says.

"Success will depend not only on technology adoption, but on the ability to orchestrate complex systems, modernise infrastructure including the grid, leverage ecosystem partnerships and make data-driven investment decisions," she adds.

Diversifying the energy mix to support data centres

To keep pace with AI demand without compromising reliability, data centres are increasingly moving away from relying on renewables alone.

They are now investing heavily in a diversified energy mix, with 86% of operators viewing the ability to operate off-grid as a major competitive advantage.

Both tech firms and utilities are funding battery storage systems to help bridge any gaps in supply.

Gerhard Salge, CTO of Hitachi Energy, says flexibility is essential for data centres.

"First of all, you need to have complementary power delivery from, for example, solar, wind or hydro," he says.

"Then, you can play with the profiles of which complement each other and exchange power when one is high and the other might be low. The more complementary your sources of energy are, the better you can balance those changes out," Gerhard explains.

"Storage can also help to fill in gaps when you have an oversupply," he adds.

Filling these gaps in clean energy supply gives electricity executives greater flexibility to manage unpredictable power shortages.

"Those organisations that build operational agility into the core of their business will be best placed to create long-term value while strengthening resilience, competitiveness and sustainability," Claire concludes.