The UK energy sector is moving through one of the most consequential periods of technological change in its history. From distribution networks to energy retailers to transmission operators, AI is no longer regarded as an experimental add-on or a distant ambition, writes Duncan Bain, Senior Energy Advisor at SAS.
Duncan Bain
According to EXL’s 2025 Enterprise AI Study, 96% of surveyed UK energy and infrastructure organisations report that they feel it is at least ‘very important’ that they are actively scaling AI programmes in the next year. That level of alignment signals something profound: the industry knows that the next frontier of resilience, efficiency and customer value will be unlocked through intelligent systems.
But with such rapid growth comes an equally pressing challenge. Without a coordinated national approach, AI risks being adopted in ways that are inconsistent, uneven and potentially unsafe at scale. This is why the call for a national AI energy efficiency strategy resonates so strongly.
Achieving shared evaluation standards
Despite the enthusiasm around AI, we must acknowledge a fundamental truth: the energy sector’s data foundations remain deeply uneven. Many organisations are still working with systems that have been bolted together over decades, unprepared for the complexity of modern machine learning. On top of this, the way organisations format, record and govern their data varies dramatically.
Even if every organisation in the sector were to achieve perfect data readiness, another critical gap remains: the absence of shared evaluation standards for AI systems. Today, every organisation approaches AI validation differently – and many lack the specialist expertise to assess the behaviour of frontier AI systems in detail.
This patchwork of approaches is not sustainable as AI becomes more autonomous and more deeply embedded in core operational processes. If we expect AI to play a role in balancing the grid, managing fluctuating demand, optimising generation and supporting customers, then we must be certain that these systems behave predictably and safely. That confidence is difficult to achieve without a shared approach.
The future of scaling AI
The industry needs a common set of criteria to assess accuracy, robustness, transparency and risk, otherwise we risk creating a landscape where organisations will race forward at different speeds using technologies that haven’t been evaluated with the same level of scrutiny.
The solution isn’t more regulation for its own sake; it’s collaboration and alignment. As AI grows more sophisticated, the sector must move beyond individual company frameworks and establish a shared foundation that enables everyone to operate with confidence.
One of the most important shifts we need to make as an industry is to stop equating ‘scaling AI’ with deploying as many models as possible. True progress does not come from volume. It comes from purpose. It comes from asking, very deliberately: What problem are we trying to solve, and is AI the right tool to solve it?
AI can already deliver meaningful impact across the energy system, often in ways that feel almost intuitive once you see them in action. It gives operators the ability to anticipate asset failures long before they disrupt service, and it brings a level of forecasting accuracy that traditional models often struggle to match in complex, high-volume environments.
Fostering meaningful innovation
But none of this matters unless AI is deployed thoughtfully. The energy sector is under significant pressure – from ageing infrastructure and fluctuating wholesale prices to the rapid growth of renewables and the economic realities facing customers. AI must be applied deliberately, focused on solving these challenges rather than adding complexity for its own sake.
The future of AI in energy is not about building bigger models. It is about building better outcomes, aligning technological capability with the real needs of customers and the grid, and ensuring AI systems are reliable, appropriately explainable, and grounded in strong data foundations. A national AI energy efficiency strategy would provide a shared blueprint for responsible AI use while enabling the safe integration of frontier technologies that benefit both the grid and customers.
The UK energy sector has all the ingredients to become a global leader in responsible AI adoption, but what it needs now is coordination. Unified AI standards will not hold the industry back; they will unlock its full potential. And if we get this right, AI won’t just reshape the energy sector; it will power a new era of resilience, sustainability and innovation.
Learn more: SAS: Data and AI Solutions | SAS UK
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