White paper: How can the energy sector leverage AI to deliver consumer benefits?

The Government has set out its ambition for the UK to lead in AI through the AI Opportunities Action Plan. This ambition has now turned to the energy sector, with DESNZ’s Review of AI Deployment in Electricity Networks underway, and an AI Strategy expected in Autumn 2026. Last month (March 2026), the Government took a significant step forward in enabling both data and AI, with the publication of the Energy Digitalisation Framework, which details plans for a digitalisation coordination function and data domain coordinators.1 Regulators are looking ahead too, working to balance innovation with safety and trust. Ofgem has established regulatory sandboxes, launched an AI Reg Lab, and published guidance on the ethical use of AI.

At the same time, investment is strengthening the data infrastructure needed for AI. This includes £571 million for data and digitisation under Ofgem’s upcoming price review for transmission, RIIO-T3. Projects supported by the regulator’s Strategic Innovation Fund (SIF) and Network Innovation Allowance (NIA) are moving from pilots to wide-scale deployment, showing how AI is delivering measurable value. Indeed, NESO estimates that predictive maintenance alone could save the UK economy around £5.5 billion by 2030.2

Where AI is already delivering value

This paper is structured around how energy moves through the system, revealing that the most immediate and high-impact use cases are in forecasting and asset optimisation, where AI is mature, the data exists, and the potential to improve system efficiency is immediate

In generation, AI is improving how assets are designed, sited, operated and maintained. It is already enhancing the performance of wind, one of the UK’s most important renewable sources. Wind farms lose output due to the “wake effect”, where the turbines in front reduce wind speed for those behind. AI-enabled control of turbines collectively, across sites, is expected to increase energy production by 3-5%, enough to power around one million UK households.3 Beyond existing assets, AI is accelerating innovation by helping develop emerging technologies such as batteries, nuclear and green hydrogen.

Forecasting is another mature and high-impact application. As renewable generation increases, supply becomes more variable and weather-dependent, making accurate forecasting critical to maintaining system balance. AI models can process large volumes of weather, satellite and operational data to improve near-real-time predictions. By using AI to forecast solar, NESO has already saved around 300,000 tonnes of CO₂ and around £30 million per year in reduced balancing costs.4 Improved forecasting also strengthens system flexibility by enabling earlier activation of storage and demand-side response. In gas networks, AI-driven monitoring is improving methane leak detection, reducing emissions and operational costs.

AI is also beginning to support control room decision-making by managing greater system complexity and updating dispatch decisions in near real time.5 Over time, this could enable more autonomous and resilient networks, including self-healing grids that detect and respond to faults automatically. “Smart substations,” such as those being trialled by UK Power Networks, are already beginning to demonstrate this intelligence at the network edge.6

At the consumer level, AI is a tool to enable the shift from passive consumption to active participation, which can boost system flexibility. However, this hinges on automation. Through dynamic and smart tariffs, AI can automate when energy is used, stored or exported. Small actions at scale can have systematic impact, as demonstrated when AI was used to managed electric vehicle charging across 13,000 customers, reducing peak demand by 42% and saving households around £650 per year.7 AI is also enhancing customer service and inclusion, from improving response times to identifying vulnerable households more effectively, enabling more targeted support.

Barriers and solutions

Despite these numerous uses, we argue that an outcomes-based approach is essential to ensure deployment is driven by system value. For example, challenges, such as the congested grid connection queue are primarily structural and administrative, meaning AI is not necessarily to most effective tool to resolve them. More broadly, several barriers must be addressed if we are to successfully use AI in the energy system for the benefit of consumers. Despite the introduction of a “presumed open” approach to data, access to high-quality, interoperable and real-time data remains limited. Trust and understanding, particularly in relation to data sharing, privacy and cybersecurity concerns, are barriers to adoption. Finally, high energy costs and skills shortages pose risks.

Addressing these barriers will be critical to scaling AI across the energy system. With the right policy, regulatory and data foundations in place, AI has the potential to play a central role in delivering a more efficient, resilient and consumer-focused energy system. Amongst techUK’s recommendations, this includes accelerating architectural oversight of energy data and digitalised services to ensure a shared technical approach across the sector; improving interoperability by advancing the GB Common Information Model; and strengthening AI assurance through a central hub for testing and verifying algorithms, with an emphasis on outcomes‑based assurance and human‑in‑the‑loop safeguards.

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