Hivel AI Impact: Overview & Insights
Overview
Hivel's AI Impact view draws on data from your connected AI tools - Cursor, GitHub Copilot, and Claude - to show how much of your shipped code is AI-assisted versus human-written. It surfaces patterns and trends over time, helping teams understand adoption and measure its effect on developer productivity.
What you'll see
AI vs. human contribution, based on signals from integrated tools
Aggregated trends for a clearer team-level view
Why teams use this view
AI tool dashboards tell you how much AI is being used. The AI Impact screen helps you connect that usage to what actually matters - how it's influencing cycle time, throughput, review time, and other engineering metrics.
Instead of tracking adoption in isolation, teams can start to see whether increased AI usage is translating into meaningful improvements across the board.
User adoption categories
To help you quickly understand adoption at the individual level, Hivel groups users into categories based on AI usage within the selected time range.
Inactive: 0%
Occasional: 1–30%
Regular: 31–70%
Power: 71–100%
Trends over time
The screen also highlights how adoption changes over time.
Team AI usage trends: understand which teams are leading adoption and where additional enablement may help.
Developer distribution trends: see how usage is distributed across developers and how it shifts over time.
A note on Code Telemetry
For teams with Code Telemetry enabled, Hivel also processes commit-level data to independently verify and enrich AI classification signals. If commit details have not been processed for a given PR, telemetry collection for that PR may be incomplete, which can affect classification accuracy. This is separate from the integration-based signals described above and is typically relevant for teams running advanced pipeline configurations.
Common questions on Code Telemetry
Why might Hivel show lower AI usage than other dashboards?
In most cases, discrepancies are caused by one of the following:
Unmerged pull requests (only merged code is included).
Processing delays for very large PRs.
Historic data has not been synced, which can limit comparisons.
How the confidence score is summarized
Code blocks within a PR are evaluated individually.
At the PR level, the confidence score generally scales with the amount of code in the PR. In practice, larger PRs tend to yield higher confidence, while smaller PRs tend to yield lower confidence.
Conclusion
Hivel's AI Impact screen provides a practical, production-focused way to understand AI usage and impact across teams and developers - so you can scale AI tooling with clarity and confidence.
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