Dr Anita Devi | FRSA CC ALB dr. h.c. #GGA✨
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@Butterflycolour
Follower of Jesus ✝️ Advocating 4 #Justice & serving the #Lost / #Lonely #TeamADL CEO❣️🙏⚔️🛡️🌛☀️ @SEND_Leaders @SENCOcoach @S4L_iHub @SENDportal @JoyChampions
A new year, a moment to pause. We’re starting 2026 rooted in what matters, #inclusion, and meaningful change. Exciting things ahead. Back in the office January 5. Until then, rest well. Happy New Year!
“Experts” & “expertise” can be very dangerous to change interventions. In leading change, it’s better to think like an explorer than an expert. See the graphic below. An “expert” way of thinking can become a loop where knowing a lot turns into strong certainty. That leads us to mainly look for information that backs up what we already know, which then makes us feel confirmed. Experts can become oriented toward being right & getting affirmed, which can make their thinking narrower & more self-sealing over time. An “explorer” way of thinking is a loop focused on learning. It starts with being humble enough to admit we might not have the full picture, then asking questions, staying curious & trying to find out more - so new information keeps shaping our views & change practice over time. One of the greatest dangers in change experts (especially prevalent in external change experts coming into an organisation) is bias. Common biases are: - Confirmation bias: we search for information/evidence that supports what we already think & overlook anything that contradicts this. - “Solutioneering”: We jump quickly to a preferred intervention (new structure, operating model, digital tool etc) before fully understanding the local context & constraints. - Authority bias: we can give extra weight to the opinion of the most senior person (or the loudest “expert”) & discount what others (especially people closer to the work) are seeing or can contribute. - Overconfidence effect: we can be too sure we’ve got this under control, so we plan as if the future is predictable & leave too little room for learning & adaptation. - One-size-fits-all / template bias: we over-apply what worked elsewhere (reusing change models, templates & assumptions) even when culture, incentives, capability or demand patterns differ. - Case-study trap: We lean too heavily on successful past engagements & familiar sectors (“this looks just like Y”) & under-sample what is unique about this organisation. In a relatively stable world, expert-led change can deliver results. But as AI accelerates the pace of disruption, the edge shifts from having the answers to staying open to better ones. The most effective change leaders will be those who keep their curiosity switched on, run experiments, learn quickly & humbly adapt when the evidence changes. In other words, the future belongs to explorers - because in an AI-shaped world, agility is likely to beat expert ability when it comes to change. For experts/explorers see Joey Davis: joeydavis.me/posts/unlockin… For more on biases, see the review by @grahamkmann of the work of Rolf Dobelli: grahammann.net/book-notes/the… Graphic adapted from one by @anujmagazine.
