Leading change ideas for the AI era
Just when we thought we were getting quite good at technology adoption, along came AI and many of us now feel like we are back to the drawing board (that’s half the fun in my view!).
The cloud era (~2010 onwards) transformed technology from a tool into a platform, requiring organisations to manage continuous change and help people adopt new ways of working. It taught us a lot about turning technology into meaningful change. We learned that deployment didn’t guarantee adoption, usage didn’t necessarily mean people had changed how they worked, and an ever-evolving end state complicated the success story. This era saw a set of change management approaches developed for continuous technology adoption that were focused on helping people use new tools, work differently and achieve value from change (many of us in the industry refer to this as the ‘playbook’). Change leaders earned their stripes in this era too, and the profession became a much more recognised part of effective transformation. Those lessons still matter…
AI simply gives the playbook some new problems to solve.
The Change Management Institute’s 2026 Futures of Change Research reaches a similar conclusion: the foundations of change remain steady, while how they show up is evolving as change becomes faster and less certain. The technology keeps changing. People can use the same tool in remarkably different ways, and AI can now participate in work involving analysis, interpretation, judgement and creation.
McKinsey describes generative AI transformation as different from previous enterprise software projects, with employees increasingly experimenting with and redesigning their own ways of working. We helped move in this direction during the cloud era. AI seems to have taken the dial and kept turning. For change leaders, this raises some interesting questions worth exploring:
“What happens when technology becomes part of the thinking itself?”
“Is teaching people how to use AI enough?”
“How should success be measured when usage tells so little about the quality or value of that use?”
That last question is particularly interesting. Microsoft Research found that greater confidence in GenAI was associated with less critical-thinking effort, while greater confidence in a person’s own ability was associated with more. Suddenly, counting how often someone opens Copilot feels a little less informative.
What does this mean for leading change?
For change practitioners, those questions reach into familiar parts of the playbook. Impact assessments need to look beyond changes to roles, processes, systems and skills to understand which parts of the thinking are moving to AI, where judgement sits and what people need to remain capable of doing themselves. Training and enablement need to help people decide when to trust AI, when to challenge it and when their own expertise needs to take over. Someone saying “AI probably isn’t right for this” may be demonstrating exactly the judgement we want the change to develop.
Measurement needs to stretch too. Two people can use the same AI tool equally often while developing very different habits and capabilities. Time saved can create useful capacity, but we still need to understand what happens to it and whether it improves an outcome the organisation values. A Human-Centric AI approach brings these choices into the change strategy: what should people become better able to do because AI is present, and how will we know? The answers shape what we assess, how we support people and what we count as success.
The cloud playbook doesn’t need replacing, but a few pages could definitely do with a rewrite.
Read the full article
The Cloud Playbook Isn’t Enough for AI Adoption explores these shifts in more detail, with research, examples and practical questions for change leaders.
Written by Jo Grubb
Human Shift Advisory - 2026
A note on AI use in this article: For this article, AI helped with image creation, research discovery, pressure-testing ideas and editing. The thinking, synthesis, interpretation and conclusions remained human. Industry experience and expertise shaped the ideas and determined what to use, challenge, change or leave behind. This reflects the philosophy behind Human-Centric AI, which is the deliberate use of AI to strengthen what makes us human.