AI Intensity is the share of PRs merged on a given day whose commits were authored on AI-active days. It's the workload-level adoption signal — and the X-axis of the impact scatter plots that pair AI Intensity against Cycle Time, Lead Time, and Throughput.
What it measures
For each calendar day in the period, the share of that day's merged PRs that were classified as AI-assisted.
How Leanmote calculates it
ai_intensity(day) = ai_assisted_prs_merged_on(day) / total_prs_merged_on(day)
Reported as a percentage 0–100%.
Days with zero merged PRs are excluded from the average.
The three scatter plots
AI Governance shows AI Intensity on the X-axis with three different Y-axes, one chart each:
vs Cycle Time — does higher AI Intensity correlate with shorter task cycle time?
vs Lead Time — does higher AI Intensity correlate with shorter Lead Time for Changes?
vs Throughput — does higher AI Intensity correlate with higher daily PR throughput?
Each point on these charts is a calendar day. Trend lines are fitted; correlation coefficients are reported when statistically meaningful.
How to interpret it
Strong negative slope on Cycle Time and Lead Time — high-AI-intensity days are faster. Encouraging.
Strong positive slope on Throughput — high-AI-intensity days ship more PRs. Encouraging.
No clear pattern (cloud of points) — AI Intensity isn't a strong predictor of delivery on this team. Investigate per-team or per-repo to see if a sub-segment shows a pattern.
Reverse slopes — AI usage on this team correlates with slower or less-frequent delivery. Worth a serious look at how AI is being used.
What to do about it
Investigate the highest-AI-intensity days that performed worst. They're often outliers with a specific cause.
Don't draw conclusions from short windows. Quarterly is the minimum reasonable view.
Pair with team-by-team breakdowns. Org-wide patterns sometimes hide opposite team-level patterns.
Related metrics
AI-assisted PRs
AI Lead Time Comparison
Cycle Time
Throughput