Hey! Most AI adoption measurement I’ve seen relies on telemetry like commits, tickets, or license usage. That works when you own the systems. We don’t. We’re ~400 people across 100+ client projects, and connecting to client Jira or repos would need approval from each client. So we measure the human side instead: skills, how quickly people pick up new tools, anxiety about AI, and what blocks them. A few things we’ve noticed so far:
- Self-reported usage almost certainly runs higher than real usage, but the trend over time still seems meaningful.
- The blockers people name (unclear client rules, no time to learn) are things telemetry would never show.
- Running the results through Claude has been more useful than dashboards. Asking “which teams are stuck and why” beats scrolling charts. Where I’m unsure is how much to trust self-reporting on its own. Has anyone validated survey answers against real usage data? How far off was it? Disclosure: we’re building a tool around this approach, so I’m interested in where it breaks. Many thanks! submitted by /u/Expert-Birthday7928
Originally posted by u/Expert-Birthday7928 on r/ArtificialInteligence
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