Radical Geek field guide
Radical Geek's Guide to Measuring AI Adoption And Impact
A practical guide for leaders who need to measure whether AI is genuinely changing work and improving outcomes.
Why this guide
Most organisations begin with the numbers that are easy to obtain: licences, training sessions, prompt counts and tool usage. Those figures show activity. They do not show whether delivery improved or whether the organisation simply added another cost centre.
This guide provides a practical measurement model for the harder questions: which workflows changed, where time moved, whether quality held, what happened to risk, how people’s roles changed, and whether the economics make sense.
The useful question is not how often people opened an AI tool. It is what changed in the work, what improved, what became riskier, and whether the result was worth the cost.
What you will leave with
A guide built to be used.
- 01
Separate tool activity from evidence that work and outcomes have actually changed.
- 02
Measure process impact, quality, risk, people impact and AI economics together.
- 03
Build a baseline before making claims about speed, cost or productivity.
- 04
Create a scorecard leaders and engineering teams can use without gaming the numbers.
Inside the guide
The working ground it covers.
- Adoption signals versus outcome measures
- Workflow and process impact
- Quality, risk and control indicators
- People impact and changes in engineering work
- Agentic delegation and machine-speed delivery
- AI economics, baselines and practical scorecards