Every engineering leader using GitHub Copilot Enterprise is being asked the same question right now: is it actually working? Seat costs are real. Leadership wants ROI data. But GitHub's built-in usage reports live in a separate admin console, disconnected from your sprint delivery data in Azure DevOps. Your team ends up copying numbers between tools to make a case that should be obvious. This post shows how to get Copilot adoption metrics directly inside Azure DevOps — where your engineering workflow already lives.
What metrics actually matter for Copilot ROI
There are three categories worth tracking:
Adoption rate
What percentage of licensed users are actively using Copilot? A seat that sits unused is pure cost. Tracking this per team tells you where rollout is stalling and where it's landing well.
Acceptance rate
Of the suggestions Copilot makes, how many do developers actually keep? Low acceptance rate can mean the model isn't calibrated for your codebase, or developers haven't built the habit of reviewing suggestions. High acceptance rate is your strongest ROI signal.
Lines of code assisted
A rough proxy for time saved. Not perfect, but when trended over time it shows whether Copilot is becoming a core part of how your team writes code or sitting idle.
Why Azure DevOps teams have a visibility gap
GitHub Enterprise provides usage data through its API and admin portal, but it's separate from:
- Your sprint board
- Your cycle time and delivery metrics
- Your team structure in ADO
That means the people making decisions about Copilot licensing (engineering managers, CTOs) are looking at data in a completely different context than where they see delivery performance. The question "is Copilot helping us ship faster?" can't be answered by either system alone.
Tracking Copilot metrics inside Azure DevOps
The Copilot AI Metrics extension for Azure DevOps pulls GitHub Copilot usage data into an ADO hub — so your engineering metrics are in one place. It shows:
- Active Copilot users vs licensed seats per team
- Suggestion acceptance rate trended over time
- Lines assisted per developer
- Breakdown by language and editor
No separate dashboard login. No CSV exports. The data sits alongside your sprint health and delivery signals in the same tool your team already uses every day.
How to set it up
Data populates from the GitHub Copilot API within a few minutes. No infrastructure required.
- Install the extension from the Azure DevOps Marketplace (free)
- Open the Copilot AI Metrics hub inside your ADO project
- Connect your GitHub Enterprise organization via the configuration tab
- Select the team you want to track
What to do with the data
Once you have consistent tracking:
- Monthly review: Share acceptance rate and active user % with leadership as your Copilot ROI evidence
- Team comparison: Identify which teams have high adoption and ask what they're doing differently — then replicate it
- Onboarding signal: New developers joining a team should show increasing acceptance rate over their first 30 days as they build the Copilot habit
- License audit: Any licensed seat with zero activity for 30+ days is a candidate for reallocation
The bottom line
GitHub Copilot is a significant budget line for most enterprise engineering teams. Tracking its impact shouldn't require a data engineering project. If your team is already in Azure DevOps, your Copilot metrics should be there too.