The AI Return dashboard tracks adoption and impact of AI-assisted engineering workflows. It helps leadership evaluate whether AI usage translates into measurable execution improvements rather than just adoption headlines.
What it is for
Monitor AI tooling adoption by team — which teams are actually using AI tools day-to-day?
Compare delivery patterns before and after AI tool adoption.
Identify where AI contributes most to speed or quality so enablement is focused.
Data sources
AI Return uses signals from the AI tools you've integrated:
GitHub Copilot — usage metrics and completion telemetry.
Claude Code — sessions, tokens, and command activity ingested via OTLP.
Other AI tools as their integrations come online.
If a tool isn't connected, its panel on this dashboard stays empty.
Typical questions this dashboard answers
Which teams are getting measurable value from AI?
Are AI-enabled teams reducing cycle time or review latency compared to peers?
Where should enablement and training be focused next quarter?
Best for
Engineering enablement leads.
Managers evaluating workflow modernization.
Leadership tracking AI return on investment.
Related articles
GitHub Copilot integration
Claude Code (OTLP) integration
Strategic Overview