How a UK energy company kickstarted its AI journey

Situation

A leading UK energy company had recently been acquired by a global energy tech conglomerate. After several years of stagnation, this unlocked much-needed investment capacity and a bold growth ambition: 10x growth in three years.

While the UK energy sector continued its structurally positive transition towards renewables, achieving this ambition would require a fundamental step-change in both the operating model and market positioning.

This created a timely opportunity to explore how AI could play a meaningful role. But leadership views were fragmented. Some saw AI mainly as a way to speed up tasks. Others focused on workforce impact. A smaller group connected AI to customer value or competitive advantage in a fast-changing energy market.

The real question was whether AI could become a genuine advantage, or whether it would turn into yet another distraction for an already stretched leadership team.

Approach

Rygur ran a full-day AI strategy workshop for the leadership team to address this question.

1. Step one- demystify AI: The session started by cutting through hype, over-claiming, and doom-mongering. The team explored how AI has evolved over the past 50 years and where it is likely heading. A clear explanation of large language models, generative AI, and more established forms of machine learning helped leaders build a grounded understanding of capabilities, limitations, and risks.

2. Step two- AI and the energy sector: The team then examined how AI is already reshaping the energy sector. While pilots and productivity tools existed across the organisation, there was no shared view on AI’s implications for long-term growth, decarbonisation, or changing customer expectations. Using an external, impartial perspective and recent market projections, the group aligned on one key insight: the growing use of AI will increase electricity demand, directly through data centres and indirectly through EVs and electrification, creating a structural tailwind for clean energy providers.

3. Step three:- applications across the business: The team explored AI opportunities using an unconstrained “art of the possible” lens across three horizons:

   1. Retooling the organisation to improve productivity in everyday tasks

   2. Reengineering core processes with an AI-first approach for step-change efficiency and effectiveness

   3. Reimagining the future business model

Ideas covered demand shaping, operational efficiency, and new service models across energy forecasting, asset performance, customer propositions, risk management, and grid interaction. Workforce impact was explicitly discussed, including how automation, augmentation, and decision support could change roles, skills, and career paths across the organisation.

“This has been a cathartic experience. We discussed so many ideas today, looking at how AI could help achieve our business goals. While our growth ambition is aggressive, we have never had a better chance to succeed.” — CEO

4. Step four- grounding ambition in reality: Finally, ideas were grounded against the organisation’s current maturity. A short AI maturity assessment showed a relatively low starting point, with several foundational capabilities needing attention before scaling. AI usage varied widely across teams, from early adopters to those yet to start.

Initiatives were prioritised based on materiality and criticality, balancing short-term quick wins, medium-term must-haves, and longer-term strategic bets.

Results

The workshop aligned the leadership team and surfaced the need for a clear AI vision to set investment appetite, risk tolerance, and a coherent roadmap tied directly to the growth ambition.

Key takeaways

• Foundational capabilities are essential, including governance for prioritisation, monitoring, and risk management

• Some use cases can launch immediately, but data quality and core technology need upgrading for more advanced applications

• An AI use-case factory is critical to build momentum and invite participation across the organisation

• Workforce impact emerged as a major theme, with clear implications for role redesign, skills, career progression, and organisational structure

• Planning for the future workforce needs to start now, given the long lead times for skill development and role change

• AI can materially support growth across sales and marketing effectiveness, customer insight, experience design, service evolution, flexible pricing, asset optimisation, and broader value-chain activity

• Efficiency gains from AI could free up capacity and funding to reinvest in the programme itself

• Doing nothing was not an option. Continued experimentation matters, but this was recognised as a chance to build a truly AI-enabled operating model rather than a collection of pilots

The shift was practical. AI moved from being a set of tools to a strategic enabler of what the company cares about most: growth, customer value, and operational efficiency.

If you want to move beyond scattered pilots and understand how AI can genuinely support growth and competitiveness in your sector, get in touch. We help boards and executive teams design practical, focused AI programmes that cut through noise, concentrate investment, and deliver measurable outcomes.