The recent Womens Utilities Network event on AI in water and energy brought together a packed room of practitioners, policy voices and technology leaders for a refreshingly practical conversation about what AI actually means for our sectors.

Hosted by Accenture and chaired by WUN Co-Founder & Director Jo Butlin, the evening combined a keynote from Nick Tate, Managing Director for Talent & Organisation at Accenture, followed by a panel discussion spanning water, energy systems, policy and data leadership.

What emerged was not a debate about tools or trends, but a far more interesting question: how do people, organisations and cultures need to change if AI is going to deliver real value rather than noise?

This blog was written using AI, informed by the transcript of the event recording. As the discussion centred on practical application rather than abstract debate, using AI to produce this summary felt both relevant and intentional.

Keynote: the human + AI organisation

Nick Tate’s keynote set the tone early by reframing AI as a people issue rather than a technology one. Drawing on Accenture’s global Human + AI Impact Initiative, he made a simple but uncomfortable point: AI capability is moving faster than organisations’ ability to absorb it.

One slide in particular landed with the room. Despite billions being invested in AI, only around a third of executives believe their organisations are scaling real value from it. The gaps are not technical. They are human: skills, trust, adoption and leadership.

Nick described a future where leaders will be “the last generation to manage a purely human workforce”, with organisations increasingly made up of people working alongside AI agents and digital labour. Yet his emphasis was not on replacement, but on responsibility.

“Anything that can be connected, can be measured,” he said, arguing that this makes human qualities like judgement, empathy, curiosity and courage more important, not less.

Perhaps the most quoted line of the evening was also the simplest: hands on keys. AI capability does not come from strategy decks or proof-of-concepts run in isolation. It comes from people actually using the tools, learning through doing and being allowed to experiment safely.

Nick closed with a clear warning and an encouragement. The warning was that organisations cannot bolt AI on at the end of existing processes. The encouragement was that those who invest in people, not just platforms, are seeing dramatically higher returns.

Panel discussion: from abstraction to application

The panel brought the conversation firmly into the realities of regulated, complex systems.

The discussion was chaired by Jo Butlin and featured:

• Melina Persson, Client Director for the UK Water Sector at Microsoft
• Dr Roya Ahmadi, Innovation Programme Manager at NESO
• Isabella Darin, Policy Manager at Energy UK
• Andrea Sulzenbacher, Managing Director for AI & Data at Accenture

Leadership, culture and capability

If the keynote set the direction, the panel grounded it firmly in organisational reality. A clear theme emerged early on: many organisations are still treating AI as something to be delivered rather than something to be learned. The panel repeatedly returned to the idea that AI cannot succeed in silos or behind closed doors. Where progress is stalling, it is rarely because the technology is lacking, but because people have not been brought with it.

The conversation reframed leadership as participation rather than oversight. AI adoption was described less as a transformation programme and more as a capability shift that requires leaders to model curiosity, admit uncertainty and learn alongside their teams. The most effective organisations are not those running the most sophisticated pilots, but those enabling widespread, everyday use. In that sense, culture was presented not as a soft issue, but as the primary delivery mechanism for value.

Regulation, data and trust

In a regulated environment like water and energy, the panel was clear that trust is non-negotiable. AI cannot be treated as a generic productivity tool when it sits alongside critical national infrastructure and sensitive consumer data. Rather than arguing for speed, the discussion focused on sequencing. Starting where AI can augment human decision-making, reduce operational friction and improve visibility was seen as both pragmatic and necessary.

Data emerged as the quiet constant underpinning every AI ambition. Issues of ownership, access, quality and interoperability were recognised as structural challenges that cannot be solved by technology alone. Trust, the panel suggested, is built incrementally: through clear governance, well-defined guardrails and consistent behaviour over time. The absence of absolute certainty was not framed as a blocker, but as something organisations must learn to manage consciously and transparently.

 Personal productivity and everyday use

Where the discussion became most tangible was in the shift from strategy to day-to-day experience. AI’s most immediate impact, the panel suggested, is not in large-scale transformation but in the accumulation of small gains: reducing administrative burden, helping people start difficult tasks and freeing up cognitive space for better judgement.

These uses were not presented as trivial. On the contrary, they were framed as foundational. Everyday interaction with AI builds confidence, capability and discernment. It also surfaces an important truth: the value of AI is less about the quality of its answers and more about the quality of the questions people learn to ask. In that sense, personal productivity is not an end point but a gateway, allowing individuals and organisations to develop the habits needed for more complex and higher-risk applications over time.

Key Takeaways

For those in the room, the event did not offer neat answers or a single recommended path. Instead, it surfaced a set of grounded takeaways that feel especially relevant for water and energy:

• AI value depends more on people and culture than on technology
• Leaders cannot outsource curiosity or understanding
• Data quality and governance are prerequisites, not afterthoughts
• Trust is built through transparency, guardrails and lived experience
• The most effective starting point is often personal productivity, not large-scale transformation

As Jo Butlin reflected towards the end of the evening, the most important shift may not be technological at all. It is moving from asking “do we have the right tool?” to asking “are we enabling our people to learn?”

The full discussion includes far more nuance, challenge and practical examples than any summary can capture. If you want to hear the detail behind these themes and the discussions that made the conversation so valuable, the recording is well worth your time.

Thank you to WUN Advocate Julia Stichling for collaborating with an AI tool to co-create this AI-generated blog, using the event transcript.