AI adoption and governance
How to measure AI adoption beyond licences and logins
Track changed work, reliable use and business outcomes rather than activity that looks good in a dashboard.
Define the adopted behaviour
Name the user group, trigger, changed steps, expected output and old activity that should reduce or stop. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. If the target behaviour is vague, usage data will be easy to celebrate and hard to interpret. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Build a measure chain
Connect access and training to repeated use, successful completion, quality, time, cost and the final business outcome. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Use leading indicators to manage adoption without confusing them with realised value. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Segment the users
Compare teams, roles, locations, tenure and workflow types to find where the design or support is failing. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. An average can hide enthusiastic experts and a majority who cannot fit the tool into real work. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Measure friction and confidence
Ask how much correction, searching, prompting, waiting and work-around behaviour users experience. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Low use may reflect a poor workflow rather than resistance or insufficient communication. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Track exceptions and fallbacks
Monitor when people abandon the tool, return to the old process or escalate an output and why. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Exception data often reveals the operating changes required for safe scale. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Use measures to make decisions
Agree thresholds for improving, expanding, pausing or retiring the use case and review them with owners. This matters because measure AI adoption decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Adoption reporting should change the portfolio, not become a permanent description of activity. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
What to carry into the work
- If the target behaviour is vague, usage data will be easy to celebrate and hard to interpret.
- Use leading indicators to manage adoption without confusing them with realised value.
- An average can hide enthusiastic experts and a majority who cannot fit the tool into real work.
- Low use may reflect a poor workflow rather than resistance or insufficient communication.