Most organisations approach AI in the same order. Define the strategy, select the tools, roll out training, then measure adoption.
It looks logical on paper. In practice, it often produces strategies written by people who have never used the technology in their own work, and training that teaches features rather than new ways of working.
Understanding comes from doing, not from reading about it.
I saw this first-hand. Like many people working in technology, I had completed the courses, worked through Microsoft Learn and studied towards AI certifications. All of it was useful. None of it changed how I worked.
What changed was redesigning a real workflow. Coordination across a large organisation was consuming a disproportionate share of my time: requests arriving through multiple channels, information to chase, deadlines to track and stakeholders to update. Rather than asking how to do that work faster, I asked whether it needed to be done the same way at all.
Using SharePoint Lists and Power Automate, I rebuilt it as a single communications management board and automated the repetitive steps. It was not sophisticated technology. But it made the relationship between a business problem, a process and the technology that could improve it far clearer than any module had.
What this means for leaders
That shift, from learning about AI to working differently with it, is the one most organisations are trying to create at scale. Three things help.
- Start with real work, not use-case lists. Ask teams where time, effort and judgement are being spent today, and which of those processes deserve to be redesigned rather than simply accelerated.
- Make experimentation safe and structured. People need permission, clear guardrails and time to try, fail and adjust. Without governance, experimentation becomes risk. Without room to experiment, governance becomes a barrier.
- Let leaders go first. Executives who have redesigned part of their own work speak about AI with credibility and set realistic expectations for everyone else.
Strategy informed by experience is more grounded, more credible and far easier to adopt.
This is the thinking behind AI Through My Lens. Each perspective looks at what happens when AI moves beyond the demo and into real work, real organisations and real decisions, through five lenses: Creation, Connectivity, Capability, Culture and Control.
Because the question that matters is not simply what AI can do. It is what value it will create, what it could cost, and how we will know.