AI adoption field guide
A practical guide to adopting AI, drawn from real engagements. Eight short chapters, each about five minutes to read.
This guide is for the person leading AI adoption without a dedicated AI team. Each chapter covers one area: what usually goes wrong, what good looks like, the first three moves, and how to tell it is working.
Not sure where to start? The self-assessment covers the same eight areas and shows which ones need attention first.
Readiness has very little to do with AI
Why the first work is on data, access, ownership, and process.
Start with the problem, not the tool
Choosing what AI is for, and how you will know it worked. (In progress)
Getting people to use it
Closing the gap between having access and changing how work gets done. (In progress)
AI tests your data governance
What the tools can read, and whether you trust the access model behind them. (In progress)
What your agents and connectors can reach
Containing anything that can act on your behalf. (In progress)
Beyond copy and paste
Turning a workflow that lives in someone's head into one that runs. (In progress)
Choosing models on purpose
Matching the model to the step, and counting the real cost. (In progress)
AI access as a continuity risk
What happens to the work if a model or tool is not there tomorrow. (In progress)