I sat in a review meeting last month where the AI team presented their model's accuracy metrics. 94.3% precision. 91.7% recall. F1 score of 0.93. The slides were thorough. The numbers were impressive. Then I asked: "How many people are actually using this?" Silence. Nobody in the room knew.
The Accuracy Obsession
Technical teams measure what they know how to measure. Model accuracy is quantifiable, benchmarkable, and improvable. You can spend months tuning parameters and see the numbers tick up. It feels like progress.
But accuracy is a necessary condition, not a sufficient one. A model that's 99% accurate and used by 5% of the target team is delivering less value than a model that's 85% accurate and used by 80%.
89%
of enterprise AI teams track model accuracy as their primary metric
Source: O'Reilly AI Adoption Survey, 2024
The metrics that actually predict whether an enterprise AI investment delivers value are all adoption metrics:
Usage frequency. How often do people use it? Daily, weekly, occasionally, never?
Task completion. When people start an AI-assisted workflow, do they finish it? Or do they abandon halfway through and do it manually?
Voluntary vs mandated use. Are people choosing to use it because it helps, or because they were told to?
Return usage. Do people who try it once come back? First-use numbers are vanity metrics. Return usage is the signal.
Time to independent use. How long before a new user can use the tool without support? The faster this is, the more scalable the adoption.
Why This Matters
Accuracy improves incrementally. Adoption is binary at the individual level: someone either uses the tool or they don't. And the factors that drive adoption, trust, workflow fit, perceived value, ease of use, are almost entirely outside the model's performance.
I've seen AI systems with moderate accuracy achieve high adoption because they fit seamlessly into workflows and solved a real pain point. And I've seen technically superior systems with near-zero adoption because they required too many steps, produced outputs in the wrong format, or made people feel like they were being replaced.
The message for AI programme leaders: spend at least as much time measuring and improving adoption as you spend measuring and improving accuracy. The model is a tool. Adoption is the outcome.
Next time your AI team presents model metrics, ask the adoption question first. "How many people are using this daily?" If they can't answer, you're measuring the wrong thing.