In 2025, Britain paid over £1bn to turn off wind farms when the electricity grid could not handle the excess supply. At the same time, gas fired power stations were often brought online elsewhere to meet demand, driving up consumer prices and undermining emissions targets. As a high net importer of energy from Europe, losing renewable power generated domestically highlights a serious weakness in the UK’s energy infrastructure, particularly the grid bottlenecks and connection delays that continue to hold back the UK’s renewable transition. These issues are explored further in our analysis of how North Sea oil and gas can support the UK’s renewables transition. To what extent is Artificial Intelligence (AI) the solution to this problem?

Rising electricity demand and the role of AI

Demand for electricity is increasing, driven by factors such as the rising uptake in electric vehicles, increased use of air conditioning and surging demand from data centres, which is forecast to increase by over 50% in Europe by 2030. A significant contributing factor to this is the growing use of AI. Paradoxically, the energy industry is looking more to AI to to manage demand on the grid and optimise service to consumers. This needs to be coupled with further infrastructure investment and reform of existing grid infrastructure.

AI is increasingly being used to improve the accuracy of weather forecasts, which helps energy providers to forecast supply more effectively. This is significant because better forecasting enables energy to be deployed strategically. For example, electric vehicle owners are already offered incentives to charge their vehicles during periods of low demand and, increasingly, to allow their batteries to absorb excess capacity from the grid to redistribute later. Similarly, peer-to-peer trading allows homeowners with solar panels to sell their surplus energy within local networks. AI can also be used to support high energy users, such as data centres and manufacturing production plants, by enabling them to schedule operations during periods of peak renewable generation, such as sunny or windy conditions, maximising efficiency and reducing strain on the grid.

For the UK, this challenge is particularly important. Heavy reliance on imported energy undermines energy security and leaves the country vulnerable to price volatility caused by fluctuations in demand or unforeseen geopolitical shocks, such as the Russian invasion of Ukraine which resulted in a significant contraction in available oil and gas supplies to European markets. At the same time, the shift to electric vehicles and growth of data centres create new dependencies on critical minerals. Securing access to these resources must be a government priority, given the dominance of China and the USA in global supply chains.

How AI is optimising energy supply and distribution

There are opportunities, too. Maximising efficiency also makes pricing more predictable, allowing for more confidence in investment decisions for energy-intensive businesses, as well as improving the circumstances for end consumers. AI is also used to predict supply chain issues including the impact on energy infrastructure. Traditional forecasting relies on historic data, but this becomes less reliable when responding to unforeseen circumstances.

Alongside government reform of the existing UK energy grid, the industry is focused on maximising the use of energy output and reducing inefficiencies. Once these foundations are in place, energy producers can look to expand their output capacity more effectively, and with greater confidence in the available infrastructure. Predictive maintenance is already revolutionising the industry through its use of real time data to monitor the operational performance of equipment and identify potential failures before they occur. Alongside insights from more accurate forecasting, maintenance can also be scheduled during periods of lower demand, minimising disruption and optimising grid stability. This also gives rise to significant cost savings as maintenance can be performed at a stage where the costs of doing so are minor when compared to a full overhaul of the infrastructure that may be required if the equipment fails.

Last year, 98% of energy companies planned to create AI-specific roles, yet more than half believed their existing workforce lacked the necessary skills.

The energy industry is in the midst of a revolutionary operational change, and business leaders must reassess workforce composition, invest in upskilling and identify the AI tools that will drive future competitiveness.

How we can help your energy and natural resources business

RSM is a leading audit, tax and consulting adviser to mid-market business leaders. We have extensive experience in the energy and natural resources industry, working with clients in sectors spanning oil and gas, renewables and cleantech and mining and metals.

To discuss the impact of AI on your energy operations, please get in touch with David Hough or your usual RSM contact.

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