The shift we should be measuring
AI leadership is shifting. It is no longer defined by access to technology. It is defined by something far harder to build and even harder to measure: the human ability to adapt at the same speed as the systems we are deploying.
As one of this year’s AI 100 UK Leaders, I had the privilege of standing alongside people building extraordinary technologies. What became clear is this. The greatest barrier to adoption now is not capability on the machine side. It is capability on the human side.
And unlike previous technological revolutions, we do not have 20, 50 or 100 years for society to adjust. We have months, sometimes weeks.
“We have been through major technological revolutions before so what will we do differently this time to ensure progress isn’t delayed?”
Across sectors, the pattern is consistent.
Yet human adoption lags.
Recent IBM research shows that 66 per cent of UK enterprises are already experiencing significant AI-driven productivity improvements, yet many still say they have not tapped AI’s full potential, highlighting the need for workforce transformation and AI skills. This readiness gap slows progress in:
In short, the bottleneck is no longer the technology. It’s us.
Traditional approaches to learning and development typically measure confidence, participation, and satisfaction. These are useful engagement signals, but they do not tell us how someone will think or act when the context shifts or the stakes are high. They give leaders no real visibility of who can be trusted to work with AI in a way that protects outcomes, customers and reputation.
As AI moves inside workflows, this distinction becomes critical.
We need the ability to distinguish between:
Between:
This is the cognitive shift required for responsible and effective AI use across government, industry, and public services.
Over the past year, I began exploring a simple idea:
If readiness is the barrier, what does readiness look like in the mind?
If we could observe that moment reliably, we could better understand:
These questions sit at the heart of digital transformation, digital inclusion and responsible AI governance.
This led me into my own work on whether AI itself could help us detect these cognitive transitions.
Generative AI has a distinctive property. It can hold a structured, topic-agnostic conversation. When paired with the right methodology, these conversations can reveal patterns in how (and whether) people:
This work evolved into something I later named Schema Shift Analytics, a patent pending, AI driven approach that uses generative AI conversation as the medium for detecting signals of cognitive readiness at scale. In simple terms, the conversation generates the data, and the method focuses on how patterns in those responses change as understanding deepens.
The broader principle behind it matters. Ironically, Artificial Intelligence may be able to help us see aspects of Human Capability that were previously invisible.
This has potential implications for:
Everywhere we look, AI is entering environments where human judgment, ethics and adaptability matter.
In all these settings, responsible adoption depends on people who can adapt, reason, and apply judgment alongside intelligent systems.
The question for leaders is no longer: “Do we have the tools?”
It is: “Are our people ready for AI?”
AI leadership in 2026 demands three things:
This is not about selling tools or promoting solutions. It is about recognising that the success of AI for Good, digital transformation, public service outcomes and workforce innovation now rests on the intersection of human judgment and machine intelligence.
We have an opportunity to learn from previous revolutions and to make different choices this time.
Not just deploying the technology, but developing the readiness to use it wisely, together.
Read More Workforce & AI Skills
November 2025
November 2025