Partner Insight: From load to lever - AI's mispriced role in the energy transition

AI has become one of the defining investment themes. Semiconductor companies; data-centre operators; and suppliers of power, cooling and electrification equipment have been among the clearest beneficiaries of the infrastructure buildout.

For professional investors, the next opportunity may extend beyond that infrastructure: AI could also help address some of the energy constraints its growth is intensifying.

Data centres—which support AI as well as other digital services—have a rapidly expanding electricity footprint. Global data centre electricity consumption grew by approximately 12% annually between 2017 and 2024, reaching around 415 terawatt hours.^[1] In its base case, the International Energy Agency (IEA) projects that total demand will more than double to roughly 945 terawatt hours by 2030.^[2]

The pressure is most acute where data centres are concentrated and networks must accommodate large new loads whilst maintaining reliable and affordable power.

Yet, focusing only on AI as a source of demand overlooks its potential to reduce energy waste, shift consumption, unlock existing grid capacity and integrate clean power.

For pension schemes assessing long-term transition opportunities, these are four practical levers: reduce, shift, unlock and optimise.

Reduce: making existing assets more efficient

The first lever applies AI to data centres, buildings and industrial operations. Real-time operating data can improve the performance of motors, pumps, compressors and heating and cooling systems.

AI-enabled controls, digital twins and predictive maintenance can translate operating data into decisions that lower energy intensity, costs and emissions. This is particularly relevant in energy-intensive industries, where even modest efficiency gains can have a meaningful financial impact.

Buildings offer another significant opportunity. By forecasting occupancy, weather, equipment performance, electricity prices and grid conditions, intelligent management systems can adjust heating, ventilation, air conditioning and lighting more precisely. For investors, the value may sit in the software and service layer that turns connected physical assets into more efficient ones.

Shift: turning flexibility into a resource

Efficiency reduces the load; flexibility changes when it reaches the grid.

Virtual power plants illustrate the opportunity. These platforms aggregate distributed assets, including batteries, electric-vehicle chargers, smart thermostats and flexible commercial loads, so they can respond collectively to changing grid conditions.

AI can forecast available flexibility and select resources without materially disrupting customers. This could allow households and businesses to earn revenues or achieve savings by supporting the grid whilst helping system operators manage peak demand without relying solely on new generation capacity.

Unlock: finding capacity within today's network

Network expansion remains essential but is capital-intensive and time-consuming. AI can complement it by helping utilities use existing infrastructure more effectively.

Dynamic line ratings use sensors and local weather data to estimate how much electricity a transmission line can safely carry at a particular time. The IEA estimates that these systems can typically make 20–30% more capacity available than fixed ratings for around 90% of the time. AI can help operators forecast and use that additional headroom more effectively.^[3]

Grid analytics and interconnection software can also identify congestion and connect new demand or renewable generation with fewer unnecessary upgrades.

Optimise: increasing the value of clean power

Optimise focuses on improving how clean power is forecast, stored and dispatched. Unlike Shift, which changes when flexible demand reaches the grid, this lever is primarily supply-side: it helps operators manage variable renewable generation and the resources used to balance it.

AI-enhanced forecasting can improve reserve planning and storage decisions, helping more low-carbon power reach consumers. Even incremental reductions in renewable curtailment could improve project economics.

Identifying credible transition leaders

The framework is a screen, not a claim that every AI application is a transition solution. Investors should ask whether a use case has customer adoption; creates measurable financial value and a credible transition outcome; and is material enough to affect an issuer's growth, margins or capital allocation.

Relevant evidence may include software and service revenues, capacity under management, orders, margins and customer adoption. This can help distinguish businesses monetising genuine transition capabilities from those relying primarily on an AI narrative.

Much of the market's attention remains fixed on chips, data centres and the equipment required to power them. The less obvious opportunity may lie with providers of industrial efficiency, building controls, grid technology, storage and flexibility that help the energy system absorb growth more efficiently.

AI's expansion will undeniably add to electricity demand. But applications delivering measurable energy-system outcomes could also reduce consumption, release grid capacity and improve the value of clean power. For long-term investors, recognising that dual role may reveal transition opportunities beyond the first wave of the AI trade.

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