For years, organisations have treated change as something with a beginning, a middle, and an end. You’ve been there - a transformation programme is launched, a roadmap is created, people are trained, and eventually, the organisation is declared “ there ”. That mental model is now obsolete. Not because leaders are failing to manage change properly, but because the conditions that made episodic change viable no longer exist. AI has simply exposed what was already true: volatili

Employee training hasn’t failed because of AI. It’s failed because AI has exposed how fragile most training models already were. For years, employee training has followed a familiar pattern: Set a company-wide goal Design learning for all roles and functions Prioritise virtual delivery for scale Motivate participation through campaigns and incentives Brief managers so they can support the rollout Encourage employees to apply learning through projects In principle, this sounds

January 2026 feels like a time to make sure we’re all clear on the language we’re using around AI. AI is no longer experimental, subsidised, or safely abstract. We have started to embed it in real workflows, make real decisions about use cases, and allocate real budgets. And yet, most People Leaders still use Predictive AI and Generative AI as if they mean the same thing. They don’t. That confusion was survivable in 2025.It becomes risky in 2026. Because Predictive and Gen

Most teams still treat prompting as “ typing with better outcomes .”That was harmless when GenAI was a novelty.It becomes very expensive in 2026. Because prompting isn’t a writing habit. It ’s a capability .And in 2026, capability becomes a cost driver. The shift isn’t philosophical.It ’s economic, and it’s already moving faster than most leadership teams realise. Economic Shift The first wave of AI adoption was subsidised. Flat of fixed subscriptions. Unlimited usage ( kind

Most organisations are sprinting into AI adoption based on the AI-hype narrative. That leads to a flurry of experimentation without purpose, testing without clear success metrics, and all of it happening in silos that limit communication of outcomes. It looks productive. It feels progressive. In reality, it’s neither. Because AI isn’t failing due to lack of effort. It’s failing because no one’s translating what’s actually happening. Teams are building vertical capability with

I posted about AI literacy versus AI Hacks this morning. Like all posts, they are the tip of the iceberg, there is always more to be said, shared and explored. What I shared was based on how I currently understand these concepts. It’s rooted in my own use of GenAI tools, and also in the many conversations I’ve had with peers who are experimenting in different ways. Definitions: Literacy vs Hacks Let me define how I see it: AI literacy is the longer road. It’s about choosing