Craig Stanley
Home / Blog / Notes

Why decisions, not adoption

Adoption figures say how many people use an AI tool. They don't say whether work got better. Measuring decisions does.

10 October 2026 · 2 min read · Craig Stanley
In short, explained

Lots of people using a new tool doesn't mean it's helping. It's better to check whether people are making better choices because of it.

Most AI programmes measure how many people use the tools. That's easy to count but doesn't show whether anything improved. Counting decisions, how many, how fast and how good, shows whether the tools are paying off.

Adoption metrics measure activity, not value. Anchor AI programmes on a decision inventory and measure volume, cycle time, quality and cost per decision before and after. Adoption then becomes a leading indicator rather than the goal.

Most AI programmes I've seen report the same numbers: licences assigned, monthly active users, prompts per user. Those numbers go up, the dashboard turns green, and nobody can say what changed about the work.

Adoption measures activity. It tells you people opened the tool. It doesn't tell you whether a case was closed faster, a payment was checked better or a customer got an answer sooner.

Decisions are countable

A decision has a volume, a time to make, a cost and an outcome. You can count all four before and after a change. If a model now scores routine expense claims, you can measure how many claims went through, how long they took, how many were wrong, and what each cost to handle.

That's a measure a finance director recognises.

What changes when you measure decisions

You start with the work instead of the tool. The first question becomes "which decisions cost us the most?" rather than "who hasn't used Copilot yet?"

You stop chasing usage for its own sake. A team that uses an AI tool for one high-volume decision and nothing else may be getting more value than a team that uses it for everything a little.

Risk gets clearer too. A tool that drafts emails and a tool that approves payments have the same adoption numbers and very different risks. Decision mapping shows the difference.

Adoption still matters

It's a leading indicator. If nobody uses the tool, nothing improves. But it's a means, not the goal. Put decisions at the centre and treat adoption as one of the things that gets you there.

Which decision did your team make this week that nobody wrote down?

A question to take awayWhich decision did your team make this week that nobody wrote down?

About me

Craig Stanley

Microsoft AI consultant and technical architect, based in Whitley Bay. Over the last few years I've delivered Microsoft 365 Copilot, Copilot Studio agents, Azure AI Foundry work and governance for UK public sector and financial services organisations.

What interests me is the decision underneath the tool: what it costs, what it risks, and whether a small, transparent model can make it better. I write the methods up here and on Substack so anyone can use them.

Find me