The adoption illusion
Enterprise dashboards light up with GenAI usage data: daily active users, prompt volume, tool activations. Leadership sees rising curves and concludes the transformation is working.
But usage is not capability. A team can run hundreds of prompts a week and still produce outputs that require heavy rework, miss verification steps, or leak sensitive context into public models.
What capability actually looks like
Capability means a person can reliably move from intent to a work product that is accurate, verified, and safe to share, without a colleague needing to catch errors downstream.
It spans five measurable dimensions:
- Prompt Design: Can the person frame a request that produces useful output on the first or second attempt?
- Output Verification: Do they check facts, logic, and tone before forwarding?
- Iteration & Refinement: Can they steer a multi-turn conversation toward a better result?
- Data Handling: Do they know what should and shouldn't enter a model?
- Workflow Integration: Can they embed GenAI into an existing process without disrupting handoffs?
Why the gap matters
When capability lags behind adoption, three things happen:
- Quality variance: Two people on the same team produce dramatically different outputs from the same tool.
- Invisible risk: Unverified outputs reach clients, boards, or regulators without anyone flagging them.
- Enablement waste: Generic training gets delivered, completion rates look healthy, but on-the-job behavior doesn't change.
Measuring the right thing
The shift from adoption metrics to capability metrics is not complicated, but it does require scenario-based assessment, placing people in realistic work situations and observing what they actually do, not what they say they can do.
That is the premise behind diagnostic-first approaches: baseline real behavior, identify where the gaps concentrate, and then target enablement where it will move the needle.
Adoption tells you who has the tool. Capability tells you who can use it well.