From hype to value: Expert views on AI impact

At a recent panel event, we hosted, leaders from government, industry, and technology to explore this very challenge. In front of a cross-sector crowd of business leaders, I was joined on stage by Carl Dalby, Group Head of AI & Digital at the Nuclear Decommissioning Authority; Karen Mcluskie, Deputy Director at the Department for Business and Trade; Chris Astall, Chief Product and Technology Officer at the RAC; and Gillian Magee (MAPM), Senior Director, Programme Delivery at AstraZeneca.

Their reflections offer a timely perspective on what it takes to move from potential to performance in the age of the intelligent enterprise. Here are three takeaways from the panel session:

Capture real economic value through strategic workforce transformation

AI has given us all access to a “gift of comprehension”. This capability, when embedded into workflows, promises exponential gains in productivity and efficiency. And yet many organisations are still waiting for the promised transformation to materialise. Why the lag?

Our panel discussed how a large language model-based AI solution might save three hours of work at £100 per hour – a theoretical or potential economic value of £300. But unless organisations make deliberate decisions about how to capture that value (through workforce redesign, process reengineering, or new performance expectations), it remains just that: theoretical.

This is because realising value from AI requires more than the deployment of tools. It demands hard management choices – about structure, incentives, and outcomes. As one panellist noted, “It’s not just about convenience. It’s about realisable economic value.”

Take action: Map out meaningful KPIs to unlock economic value:

1. Build an AI value tree: Create a hierarchical model to quantify ROI and total cost of ownership for each AI solution. This helps stakeholders identify and prioritise high-impact use cases.
2. Model efficiency and investment by department: Break down financial and operational metrics by business unit to compare potential gains, resource needs, and build-vs-buy investment decisions.
3. Demonstrate business value through cross-functional collaboration: Include business stakeholders in AI squads to shape use cases around real operational needs. This ensures solutions are relevant, accelerates adoption, and clearly links AI investments to measurable outcomes like cost savings, productivity gains, and strategic impact.

Drive strategic leadership and cross-boardroom alignment for tangible business impact

Another barrier to realising AI’s value lies in the boardroom. Despite the headlines, many organisations still treat AI as an operational or IT concern. But as our panel made clear, AI is not a back-office issue – it is a strategic priority that demands attention from every member of the C-suite given its potential to disrupt business models and ways of working.

A recent technology review that we conducted in partnership the UK Government, found that while the UK ranks third globally in AI research, it lags significantly in adoption. One reason? A lack of strategic leadership. Too many leaders lack the generational diversity and technical fluency to challenge assumptions and steer AI initiatives effectively.

The organisations that are succeeding are those where leadership is aligned, curious, and willing to engage deeply with the implications of AI. As one speaker put it, “You can’t wait and see. You have to crane your neck and look into the horizon.”

Take action: Develop an AI manifesto to sync leadership priorities

1. Align around a unified AI vision: Develop a cross-functional AI manifesto that sets a clear direction for responsible innovation across all AI domains, ensuring consistency and clarity from the boardroom to the front line.
2. Embed governance into strategy: Prioritise ethical AI use by integrating transparency, explainability, and responsible data practices into enterprise-wide decision-making and risk frameworks.
3. Mobilise human-AI collaboration for impact: Champion initiatives that combine AI with human expertise to accelerate transformation, enhance productivity, and deliver measurable business value.

Invest in human-centred AI adoption to unlock organisational potential

Perhaps the most powerful insight from the panel was that AI is not, at its core, a technology challenge. It is a human challenge.

Adoption is not about “upskilling” – a term one speaker rejected as implying current skills are inadequate. Instead, it’s about cross-skilling and new-skilling – recognising that AI augments human capability, rather than replacing it. It’s about embedding AI into the fabric of work.

This requires investment – in training, in systems engineering, and in cultural change. Yet, as was pointed out, the UK is currently spending 40 percent less on training than it did 30 years ago. That gap must be closed if we are to build the AI-literate workforce the future demands.

Beyond training, what’s needed is curiosity. The organisations making progress are those that encourage experimentation, that give people space to play, and that treat failure as a learning opportunity. As one panellist said, “It’s not the answers that matter – it’s the questions we’re asking.”

Take action: Root AI ethics in your roadmap to augment tech advancement with human enrichment 

1. Safeguard human impact to protect trust: Design AI systems that uphold fairness, transparency, and accountability to prevent bias and unintended harm. This not only protects individuals but also strengthens organisational reputation, reduces regulatory risk, and builds stakeholder confidence.
2. Empower human capability to drive strategic focus: Use AI to automate repetitive tasks and free up talent for creative, strategic work. This human-AI synergy boosts productivity, enhances employee engagement, and enables organisations to focus on innovation and growth.
3. Design for sustainable adoption to future-proof the organisation: Prioritise human wellbeing in AI development to foster trust and long-term acceptance. This helps avoid resistance, ensures regulatory alignment, and positions the organisation for resilient, scalable innovation.

An incredible time to be a leader

AI is not just another wave of digital transformation. It is a fundamental shift in how knowledge is created, accessed, and applied. And it is happening at a pace that challenges traditional models of leadership, governance, and change.

The good news? We’ve been here before. From electricity to the internet, every major technological leap has required us to rethink how we work, lead, and learn. AI is no different – except in its speed and scale.

As the panel concluded, this is a moment of opportunity. A moment to reimagine, not just optimise. A moment to lead with purpose, not just react to hype.

And if we do, the next three to five years won’t just be transformative. They’ll be extraordinary.