Copilot Adoption Analytics
Interprets GitHub Copilot adoption and acceptance-rate telemetry for a synthetic team and recommends concrete next steps, extending the GHCP Adoption Telemetry project.
Deterministic metrics + AI narrative — synthetic team data, not live telemetry
Adoption rate (active users ÷ seats) and acceptance rate (lines accepted ÷ lines suggested) are GitHub's own published Copilot usage metric definitions, computed deterministically from a fixed weekly record for the team you select — an AI model never invents these numbers. Only the narrative read, likely driver, and recommended actions underneath are AI-generated. The four team profiles are synthetic, illustrative examples of common adoption patterns, not a real engineering org's telemetry.
How it works
Pick one of four synthetic teams to see its weekly seats, active users, suggestions shown/accepted, adoption rate, acceptance rate, and trend — computed the same way GitHub's own Copilot usage metrics ↗ are defined — then ask the adoption advisor agent to interpret what the numbers mean and what to do next.
Try it
Select a synthetic team to see its adoption metrics.
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Pick a team above to begin.
Why this exists
This extends the GHCP Adoption Telemetry project record's theme of turning raw usage signals into evidence-based adoption decisions: the same adoption-rate and acceptance-rate definitions that project's Dataverse telemetry captured, applied here to a synthetic team so anyone can see what a healthy — or stalled — rollout looks like and what a team lead should try next.
Production checklist
- Adoption rate and acceptance rate use GitHub's own published metric definitions.
- Trend classification compares the first half of the window to the second half, computed, not guessed.
- The AI layer only narrates the already-computed summary; it cannot alter any number.
- Dedicated Foundry prompt agent, versioned and isolated from the portfolio chatbot.
- Automated backend tests cover a high-adoption and a low-adoption team case.