| Over the last two years, part of my week has been immersed in the role of Newsroom AI Programme Manager. I’ve had a front-row seat to product and technology life cycles in action. Bringing cross-functional teams together meant understanding how each operated — and guiding the shifts required when collaboration at pace became essential.I gained rare insight into both the technical and the human challenges of changing ways of working. What I learned is that in a traditional product life cycle, there are clear testing and iterating phases. Space is built in for experimentation, feedback, and refinement. Humans rarely get the equivalent.There is often no intentional space to un-learn, re-learn, and explore new habits and behaviours. No protected environment in which to practise thinking differently before performance is expected. Instead, we are asked to move forward — quickly — and when the pressure rises, it is no wonder we revert to familiar behaviours and established patterns.And beneath that reversion sits something deeper — not a skills gap, but an identity shift happening at unprecedented scale and pace because of AI. We Do Not Just Shed Skills We are training people. We are equipping them with new tools. We are running workshops on prompting, automation, and efficiency. But what happens to the old skills? We do not simply discard expertise and grow something new overnight. Identity does not update at the same speed as software. It is a process — typically non-linear and changeable. It is a process that requires space, psychological safety, and support to explore what this new world looks and feels like. Because it is not just about learning new skills. It is about understanding who we are becoming. If the expectation is to learn X, Y, Z quickly, questions naturally arise such as: What does that mean for the expertise I built my career on? Where does my value now sit? Where do I belong? These questions are rarely spoken aloud — but they are present, and they need to be heard. The Gap We Don’t Talk About Enough Mindset and critical “human skills” is a hot topic right now. And while that is important, we often underestimate the complexity of the human condition. With so much focus on building AI skills quickly, I think we have forgotten how long it takes to build a craft — to nurture mastery. Humans are deeply capable. The challenge is not capability — it is adaptability at speed. Research across motivational psychology – in particular, Amy Edmondson’s work on psychological safety, shows that sustainable behavioural change depends on ‘autonomy, competence, and environments where people feel safe to experiment and learn.’ Without these conditions, resistance is not a flaw — it is a predictable response. When we think about this in the context of belonging, there is a gap in support between where we are and where we need to be. Supporting teams to adapt at pace, ironically, requires slowing down momentarily and giving them the opportunity to catch up intellectually and emotionally. “To move from traditional management to managing in a VUCA world requires adopting a new mindset,” Edmondson says in Dynamic Teaming. “Today’s world requires cross-functional and dynamic teaming. It’s your job—the leader’s job—to move your team and organization toward that mindset.” — Amy Edmondson, Harvard Business School Professor VUCA: volatile, uncertain, complex, and ambiguous. AI Readiness Requires Human Readiness The world is moving at pace — that I think we are all aware of. There is not unlimited time to hold space for prolonged resistance. But we need to invite strategic space — designed intentionally — to acknowledge and plan for the very real friction that arises when identity is disrupted.A traditional change strategy can become redundant within weeks if it is not built with a design-and-iterate approach: Test. Listen. Refine. Repeat. Adoption with AI is more complex than most technology implementations — not simply because it evolves rapidly, but because it touches something deeply personal: professional identity.In the panel discussion last week at Charter‘s Leading with AI NYC, Massella Dukuly, Melanie Rosenwasser of Dropbox and Heather Stefanski of McKinsey & Company spoke about the concept of incubators and bootcamps where teams step away from day-to-day pressures to un-learn and re-learn together — supporting one another through the behaviours and habits that need to shift. The more we can create these types of supportive spaces to shift paradigms, the more we can accelerate sustainable adoption and reduce hidden resistance before it hardens into disengagement. A Human-Centred Approach Starts Earlier Than We Think A truly human-centred approach starts with the human.It begins with self-trust. With clarity of purpose. First, the why: – Why does this matter? – Why now? – Why for me? – Why for the organisation? Then the what: – What is changing? – What stays? Then the how: – How will this be implemented? – How will I be supported? – How does this impact my role and future? Too often, we jump straight to the what and the how. We deploy tools, run training, and circulate guidance. But if we are not rooted in the why — for ourselves as much as for the organisation — adoption is impacted. A couple years ago, I shared a simple saying: “Return to your roots. Get clear on your why. Then untangle the how.”That feels more relevant than ever now. The unseen blockers to adoption are rarely about intelligence or willingness. They are about identity, belonging, and readiness. And if we want sustainable AI adoption, we need to design not only for systems’ readiness — but for human readiness too. |
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A Simple Message of Hope.
When I sat down to finish my newsletter earlier today, the topic I had originally chosen felt disconnected
