Every finance leader I talk to wants to know the same thing: what percentage of my team is using AI?
It's the wrong question.
Here's the pattern I see. A team reports high adoption. Everyone's on the tool. But look at what they're using it for, and most of it is drafting emails and summarizing meetings. Useful, but not changing any finance outcome that matters. Meanwhile the one person who built a workflow to pull cash flow forward is getting more from AI than the rest of the team put together.
Adoption rates tell you almost nothing. They tell you how many people opened a tool. They don't tell you whether the work got better, faster, or more accurate. They don't tell you whether you made money or saved it. They're a feel-good number that makes it sound like something is happening.
What matters is output tied to a business objective, at finance-grade accuracy.
That's it. Not adoption. Output.
The metric that counts is simple. What work moved because of AI? What got faster, cheaper, or more accurate? What decision can you make now that you couldn't make before? Can you prove it in the numbers?
If you can't answer those questions, your adoption rate is just a vanity metric. It feels like progress. It isn't.
Stop measuring adoption. Start measuring outcome. The work will tell you what's happening.