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Many UK energy companies are experimenting with artificial intelligence (AI), from forecasting wind output to automating operational processes. Yet too many initiatives remain stuck in proof-of-concept (PoC) mode.
Demand is rising rapidly. Ofgem has warned that around 140 proposed UK data-centre projects could require roughly 50GW of electricity, exceeding Britain’s current peak demand. Meanwhile, renewable expansion and electrification are increasing pressure on grid infrastructure.
The challenge isn’t building models. It’s operationalising AI at scale in a sector dealing with ageing infrastructure, accelerating electrification, and ambitious decarbonisation targets.
In this environment, applied AI must help energy companies deliver both system resilience and net-zero progress.
Applied AI represents the shift from experimentation to execution. In energy, that means:
“We need to stop thinking about building an AI for a task and start thinking about employing an AI to take on a role - like a junior engineer or analyst,” says Marissa Beaty, Product Consultant at Colibri Digital.
Applied AI is already delivering value across the energy value chain.
“Applied AI is helping utilities move from reactive to predictive operations. It’s anticipating, and at times capable of resolving failures, balancing supply and demand more accurately, and ultimately improving reliability.” says Marissa.
Despite strong interest, many organisations encounter similar barriers when moving from pilot to production.
Many AI proofs of concept explore technical possibilities without defining measurable outcomes. Without clear metrics, such as improved forecasting accuracy or reduced outage minutes, projects struggle to justify further investment.
“The projects that succeed are the ones that start with a clear operational question, not with the algorithm. When energy companies define the metric or inefficiency they want to improve, whether that’s dispatch planning or maintenance scheduling, AI becomes much easier to scale.” explained Marissa Beaty, Product Consultant.
Energy systems rely heavily on legacy infrastructure and fragmented operational systems.
Organisations often face integration debt, including:
Without modern data pipelines and integration frameworks, AI models cannot reliably support operational decisions.
Marissa comments: “Most utilities don’t lack data; they lack connected data. The real challenge is integrating operational systems, asset data, and external inputs like weather or market signals so AI models can actually support real-time decisions.”
AI requires reliable, high-volume operational data. In energy environments, this includes:
Predictive maintenance illustrates the opportunity: AI-driven systems can slash operational costs by up to 30% while boosting equipment availability by 20%. However, integrating these models into operational systems remains complex.
“AI can detect early warning signs in asset performance that engineers would struggle to spot manually. But that only works when the underlying data pipelines and telemetry systems are reliable.” says Marissa.
AI deployment in energy is subject to increasing regulatory scrutiny. Ofgem is exploring regulatory frameworks and sandboxes that allow companies to test AI applications safely, while protecting consumers and ensuring system resilience.
Organisations must therefore ensure AI systems are explainable, auditable, monitored for bias and drift, and supported by human oversight.
Marissa says: “Energy companies operate critical national infrastructure. That means AI governance isn’t optional; transparency, monitoring, and human oversight need to be built into the lifecycle from the start.”
Turning AI experiments into operational capability requires a structured approach.
AI initiatives should begin with a defined operational objective, such as:
Colibri advocates a proof-of-value approach where we identify the decision AI will support and the measurable improvement it should deliver.
AI is only as strong as the data behind it. Energy organisations need data platforms capable of integrating:
Strong governance, data lineage, and security controls are essential.
“The organisations getting real value from AI are the ones that invested, or are willing to invest, in modern data platforms first. Without that foundation, even the best models struggle to make an impact.” says Marissa.
Trust is critical when deploying AI in critical infrastructure. Adhering to best practices can really support your organisation to nail governance from the very beginning. Best practices include:
Scaling AI requires ongoing monitoring and lifecycle management. AI Ops enables organisations to monitor model performance, detect model drift, automatically retrain models, and link AI outputs to operational performance metrics.
“AI systems need to be managed like digital employees - reviewed regularly, retrained when necessary, and retired when they no longer add value,” says Marissa Beaty.
Turning AI from a promising experiment into a practical energy capability requires more than strong algorithms. Colibri’s approach combines engineering rigour, industry expertise, and measurable outcomes.
We don’t deliver AI experiments. We deliver applied AI that moves the needle.
Several trends will shape the next phase of applied AI in energy.
Marissa predicts: “The next phase of AI in energy will be about orchestration. We’ll see AI systems coordinating data, assets, and decisions across the grid rather than operating in isolation.”
Applied AI is no longer a future ambition for the energy sector; it’s becoming a baseline capability. The organisations that succeed will combine technical innovation with operational discipline, strong governance, and clear business outcomes.
At Colibri Digital, we help energy organisations move beyond experiments and operationalise AI safely, sustainably, and at scale.
If you’re ready to turn AI into real business value, we’re ready to help. Speak to our team today.
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