AI adoption is rising rapidly across the energy and utilities sector, yet many initiatives remain stuck in pilot phases and fail to deliver measurable value at scale, writes Mark Simpson, Co-Founder, WeBuild-AI .
The UK government’s Clean Energy AI review highlights poor data observability and fragmented systems as key barriers to successful AI adoption. As organisations look to use AI across critical infrastructure, success depends on both model capability and on the quality of the operational foundations that support it.
Many energy and utility organisations are attempting to deploy AI on technology estates that were never designed to support AI-driven operations. Vast amounts of information are generated across generation assets, networks, field operations and customer systems, but much of it remains difficult to access, integrate and operationalise.
Without connected, reliable data, organisations risk introducing AI into environments where it lacks the context needed to support complex operational decisions, leading to slow progress, limited business value and growing scepticism about AI’s value.
The challenges of out-of-the-box AI
While today’s AI models are becoming increasingly sophisticated, most have been trained on broad public datasets rather than the highly specialised information that underpins energy operations. As a result, they often lack the contextual understanding needed to navigate industry-specific processes, engineering environments and operational workflows.
Gartner predicts that by 2027, more than half of enterprise GenAI models will be domain-specific, reflecting growing demand for AI solutions that can deliver meaningful outcomes in specialised environments.
This shift is particularly relevant for energy and utilities. Unlike sectors with large volumes of publicly available information, much of the industry’s expertise exists within proprietary engineering records, asset databases, operational systems and decades of institutional knowledge. Generic models struggle to reflect the realities of energy operations because the information that matters most is often unavailable to them.
Treating AI as a plug-and-play technology risks creating a gap between what a model can generate and what operational teams can confidently use. For organisations responsible for critical infrastructure, that gap can quickly become a barrier to adoption.
As organisations move beyond experimentation, fragmented technology environments continue to create integration and scalability challenges. Even where valuable data exists, inconsistent systems, siloed information and complex architectures can make it difficult to embed AI into everyday operational processes.
To unlock value, organisations must first strengthen the foundations that support them by improving data accessibility, connecting critical systems and modernising technology architectures to support deployment at scale.
Moving beyond experimentation
To move beyond pilots, organisations need AI systems that reflect the reality of their operations. While general-purpose models can provide a useful starting point, lasting value comes from grounding AI in proprietary operational knowledge, business processes and asset data.
When models are connected to the information that employees use every day, they are far better positioned to support decision-making, accelerate workflows and deliver outcomes that align with operational requirements. This creates a stronger foundation for adoption than relying solely on generic models trained on external information.
However, success is not simply about building more specialised models. Organisations must also identify high-value use cases linked to clear business outcomes. Rather than pursuing broad, organisation-wide deployments, organisations should focus on addressing specific operational challenges, whether that means streamlining engineering documentation, supporting regulatory reporting, improving access to asset information or accelerating knowledge retrieval for field teams.
By combining targeted use cases with AI systems built around operational knowledge, organisations can deliver measurable value, build organisational confidence and create a stronger foundation for wider adoption over time.
Governance matters as much as model performance
As AI becomes more deeply embedded in operational decision-making, governance will become just as important as model capability. For organisations operating highly regulated infrastructure, trust cannot be an afterthought and AI must meet strict requirements around resilience, safety, security and accountability.
Whether supporting maintenance decisions, outage management or network operations, organisations need visibility into how recommendations are generated before they can act on them. Understanding the data sources behind outputs, the logic used to produce them and the ability to verify decisions are all essential for responsible adoption.
Auditability and explainability are now operational requirements. Without them, organisations will struggle to scale AI beyond isolated pilots because decision-makers will lack the confidence needed to rely on AI in critical environments.
Strong governance frameworks provide the oversight needed to validate outputs, maintain accountability and ensure human decision-makers remain firmly in control. Just as importantly, they help build the confidence and trust required to scale AI into everyday operations.
Turning AI ambition into measurable value
Real progress in energy and utilities will come from building systems that understand the realities of critical infrastructure, operational complexity and industry-specific challenges. Organisations that succeed will be those that combine domain expertise, trusted operational data and strong governance to apply AI safely and effectively at scale.
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