Learn how leaders can turn AI ambition into adoption by putting people at the centre of change.
This guide shares insights from Ambition to Action: Leading AI Adoption at Scale, a Leaders’ Forum convened by Corndel and Imperial Executive & Professional Education. It explores why AI transformation is moving faster than previous waves of change, why adoption often stalls, and what leaders can do to build capability that changes how work gets done.
Inside, you’ll find practical lessons from Microsoft and others on scaling AI adoption: from using ADKAR to diagnose barriers, to building role-based learning, embedding champions, and measuring outcomes instead of activity.
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AI ambition is not the same as AI adoption
Pilots, licences and training do not automatically change how people work. This guide shows how to build the awareness, motivation and support needed to make AI adoption stick.
Adoption needs more than training
When AI stalls, more courses may not fix it. Leaders need to diagnose the real barrier, then choose the right intervention.
Learning has to sit close to the work
Role-specific learning, practical experimentation and one-to-one coaching help people apply AI to real workflows.
Scaling AI needs a new operating model
Successful AI adoption combines central strategy, standards and security with local champions who build momentum across the business.
- Why AI transformation is moving in weeks and months, not years
- The leadership tension created by top-down, middle-out and bottom-up AI adoption
- How Microsoft uses ADKAR to build the conditions for change before training
- Why role-based learning beats generic AI theory
- How to use the Cynefin framework to decide what to automate, augment or leave human
- The three risks leaders need to watch: learning, isolation and homogeneity
- Eight practical actions to start making AI adoption stick now
Download your copy of the 2026 AI Adoption Report
Enter your details to receive your copy of the guide.
