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AI Adoption
Why AI programmes stall — and the organisational conditions that let them scale.
Research
An evolving body of applied research on AI adoption, change management, psychological safety and governance — written for executives.
Research Overview
These pages are where the work gets examined. Not thought leadership for its own sake — but a disciplined record of what seems to hold up, what quietly fails, and what deserves closer study.
The current work sits across four streams. Each is being written up as a short executive briefing, with a long-form white paper in preparation.

Streams
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Why AI programmes stall — and the organisational conditions that let them scale.
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Change as a leadership discipline: what senior teams have to do that no framework can do for them.
03
The link between candour, learning velocity and the outcomes leaders actually want.
04
Governance that enables — a working model for responsible AI at the operating level.
Framework
Four layers — intent, governance, capability and adoption — used to structure conversations with executives from the first workshop through to steady-state operations.
Intent
Where AI creates real business value, and where it doesn't.
Governance
The rules and rituals that make responsible AI operational.
Capability
The people, data and platform work required to deliver.
Adoption
How the organisation absorbs and sustains the change.
Every failed AI programme I've seen was a governance failure dressed up as a technology failure.
Executive Summary
A long-form briefing bringing together the four research streams. In preparation — sign up to be notified when it's published.
Future Publications
Psychological safety as a delivery metric
In preparationThe quiet economics of AI governance
In preparationDiscovery for founders with limited runway
In preparationEnterprise change without theatre
In preparation