Hivel AI Impact Screen: Overview & Insights
Overview
Hivel’s AI Impact helps give teams a clear and practical signal to reason about AI usage over time. It is designed to help teams see patterns and trends in shipped code.
While tools like Cursor and Copilot track AI usage (e.g., code suggestions or completions), they don’t measure whether the AI-generated code actually reaches production. In contrast, Hivel’s Code Telemetry tracks AI-generated code that is merged and shipped to production, providing a real-world measure of AI adoption and its impact on developer productivity.
AI Impact
The AI Impact view provides a directional measure of AI contribution by analyzing code changes and classifying code blocks as AI-generated or human-written.
What you’ll see
AI vs. human classification for code at a PR level
Confidence score for the classification
Aggregated trends for a clearer team-level view
Why teams use this view
Many AI dashboards measure tool activity. Hivel goes a step further by measuring production outcomes by focusing on code that has moved through review and been merged.
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%
Moderate: 31–70%
Heavy: 71–100%
By default, the analysis covers the last 2 days, with an option to run a historical sync to understand past trends.
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.
Common questions
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.
Commits details have not been processed for some reason, which blocks telemetry collection.
Historic data has not been synced, which can limit comparisons.
How the confidence score is summarized
Code blocks within a PR are analyzed 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 across teams and developers, so you can scale AI tooling with clarity and confidence.
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