For Team Leads
The adoption metrics are most useful as a coaching tool — a way to see where AI is landing and where your team needs help.
As a team lead you can use the AI Adoption Score and the underlying per-user, per-model, and per-project signals to understand how AI coding is spreading across your group. The goal is to find opportunities to help, not to produce a leaderboard.
What to look for
- •Spot power users — people getting deep, frequent value who can share modes and patterns with the rest of the team.
- •Find teammates who would benefit from enablement — low coverage or shallow usage usually means a setup gap, not a people problem.
- •Track the trend over time — the timeline view matters more than any single day, especially after onboarding or a new project starts.
- •Justify investment — a rising score and broader coverage give you concrete evidence when discussing seats, models, or training.
Use it to coach, not to rank
Treat the score as a signal about enablement and workflow, not a performance ranking of individuals. A low number for one teammate is an invitation to pair, share a useful agent mode, or fix a configuration gap — not a metric to grade them on. Used this way, adoption data tends to rise on its own because people are getting genuine help.
Remember the score only becomes meaningful after at least 3 days of usage and is drawn from real MegaBrain Gateway activity, so give new teammates a few days before reading too much into their numbers.