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Human One | Dimension 03: Organisations and Operating Models When I start working with a new organisation on a transformation programme, I ask the same question early on: where are you creating space for this to happen? I get the same look every time: puzzled, occasionally a little defensive, as if the question itself were the problem. So I draw two circles. The first is large, and it represents everything the people in the organisation, or more specifically the function or b

Human One Insights │ Dimension 05: Psychology, Biology and Wellbeing Last week I described a feeling. The feeling that sits somewhere between the stomach and the chest when AI comes up in a meeting and you are not sure whether your understanding is current. The quiet calculation you run when another tech layoff is announced. The half-second pause before you answer an AI question in a room full of people. Many of you recognised it. This week I want to explain it. Because the f

There is a feeling that a lot of knowledge workers are carrying right now and for a huge majority of them, they are not sharing it out loud. It sits somewhere between the stomach and the chest. It shows up when another large tech layoff is announced and you find yourself doing the quiet calculation: how far away is that from my organisation, my team, my role? It shows up in the half-second pause before you answer an AI question in a meeting or at a dinner, that internal flick

I have spent nearly thirty years in and around business and IT transformation. Long enough to have watched several technology waves reshape industries, and long enough to recognise a repeating pattern. I am nearing the end of my AI Executive Program at Oxford uni, this week reviewing the UK Government's AI 2030 Scenarios report. It dawned on me that we are in the early stages of a cycle familiar to anyone who lived through the large enterprise platform era (think ERP or Share

Last week I wrote about platform decisions. This week: when your AI system makes an error, perpetuates a bias, or falls out of compliance, is the WHAT or the WHO responsible? You step into a lift every day without thinking about it. You are not trusting the lift. You are trusting everything behind it. The engineers who designed it, the company that maintains it, the regulator who certified the building. The moment you cannot answer who is behind it, you take the stairs. We ha

Five weeks ago I started asking a different question about AI. Not which tools to deploy, but what kind of organisation do you need to become to make AI really work? Week by week the answer has gotten more specific. This week I want to go one level further out, because the decisions that will shape your AI future are not just internal, they start with a choice most organisations are already making, but without realising it. Innovation used to live inside the firm. R&D teams b

Last week I wrote that we cannot yet fully define the problem AI will solve, and that the ecosystem assembling around AI matters as much as the tool itself. Both ideas point to the same truth: we are building the plane while flying it. As Klaus Schwab put it in 2016: "The Fourth Industrial Revolution will change not only what we do but also who we are." On one level that means AI diagnosing cancer, gene editing, brain-computer interfaces. But there is another dimension inside

Last week I wrote about the internal conditions organisations need to execute AI innovation: SPOC - the systems, people, structure and culture that either enable or block it. But before any of that work can land, there is another question, one that most organisations skip entirely: How well do you understand the ecosystem your AI strategy is sitting inside? Kodak invented the digital camera in 1975. They held the patents, they had the engineers, they had the brand. But digita

Last week I said AI investments fail when routines don't change. This week: how to tell, before you start, whether your organisation is even built to make that change. I've been working through a framework using four dimensions SPOC: (Not Single Point of Contact), but Systems, People, Organisation, Culture: and applying it specifically to AI. It's a simple four-part diagnostic, but when you run a leadership team through it, the gaps surface fast. Here's what it reveals in pra

Most Artificial Intelligence projects don't fail because the technology doesn't work. They fail because the organisation doesn't change around it. I've been studying this in detail lately, exploring how innovation theory applies specifically to AI, and one idea from the academic literature keeps hitting closest to the reality I see with clients “Organisations are bundles of routines.” Approval workflows. Decision rights. How work moves between people. What gets measured. Thes