AI Adoption Metrics Calculator
Compute adoption KPIs from your numbers: sessions per dev, cost per PR, DAU per seat.
adoption — 15 of 24 developers actively using AI tools, at $75.00/dev/month and $8.18 per merged PR.
| Sessions / dev / week | 12.0 |
| Cost / merged PR | $8.18 |
| AI-assisted PR share | 36.4% |
Shaded bands are illustrative ranges for teams 6–12 months into adoption — labeled honestly because no public per-team benchmark dataset exists. Your trend over time matters more than the band.
How it works
Most teams describe their AI adoption in adjectives — "pretty good uptake", "people seem to use it" — because nobody has done the twenty minutes of division required to get numbers. This calculator does the division. Enter six values you can pull from existing systems — team size, active AI users, weekly sessions, monthly spend, merged PRs, and AI-assisted PRs — and it computes the five KPIs that make adoption discussable: adoption rate, sessions per developer per week, cost per developer, cost per merged PR, and AI-assisted PR share. Everything runs locally, and the result exports as a markdown snapshot for your monthly report.
The metrics are chosen to answer three distinct questions. Is the team actually using the tools? Adoption rate and sessions per developer answer that — the first measures breadth, the second depth, and they diverge in interesting ways: high adoption with low session depth means people tried it and drifted away, while the inverse means a power user core is carrying the average. What is it costing? Cost per developer normalizes spend for headcount changes. Is it connected to output? Cost per merged PR and AI-assisted PR share tie spend to the thing engineering teams actually ship.
The benchmark bands behind each marker are labeled illustrative, deliberately. No credible public dataset of per-team AI adoption KPIs exists yet, and pretending otherwise would be the kind of dishonesty that makes dashboards useless. The bands sketch plausible ranges for teams six to twelve months into deliberate adoption; your real benchmark is your own previous month.
That is also the intended workflow: run this monthly with consistent definitions, copy the markdown snapshot into your engineering report, and watch trends rather than absolutes. The inputs that teams struggle to source — per-developer activity and per-session spend — are exactly what FORG's usage tracking captures automatically, which turns this from a quarterly estimation exercise into a five-minute monthly ritual. Pair it with the spend report generator for the finance-facing version and the readiness assessment when deciding where to invest next.
Frequently asked questions
Which AI adoption metric matters most for an engineering team?
Start with adoption rate — active users over team size — because every other number is noise if only a fifth of the team uses the tools. Once adoption stabilizes above roughly half the team, shift attention to cost per merged PR, which connects spend to shipped output better than raw monthly spend does. Sessions per developer per week is the best early leading indicator, since habitual use precedes measurable output effects.
What counts as an 'active user' and a 'session'?
Define both before measuring and keep the definitions stable. A common choice: an active user is anyone with at least one AI session in the measurement month, and a session is a distinct working interaction — one agent run, one sustained chat thread — rather than a single API call. The absolute definition matters less than consistency, because the value of these metrics is in the month-over-month trend, not the snapshot.
Are the benchmark bands real industry data?
No, and the tool labels them as illustrative on the page. There is no credible public dataset of per-team AI adoption KPIs yet — vendor case studies are selection-biased and survey data is self-reported. The bands sketch plausible ranges for teams six to twelve months into deliberate adoption so your marker has context, but the honest comparison is against your own previous month, which is exactly what the exportable snapshot enables.
How should I interpret cost per merged PR?
As a connection between spend and output, not a verdict on value. A rising cost per PR can mean waste — runaway agent loops, oversized contexts — or it can mean the team is pointing AI at harder work. Read it alongside AI-assisted PR share: rising share with stable cost per PR is the healthy pattern. When cost per PR spikes without a share increase, that is the signal to look at per-session spend for anomalies.
How do I get these input numbers reliably every month?
Team size and merged PRs come from your HR system and git host with no ambiguity. Active users, sessions and spend require usage tracking on the AI tooling itself — provider dashboards give org-level spend but rarely per-developer activity, which is the gap purpose-built tracking like FORG fills. AI-assisted PR share is the softest input; a PR label or template checkbox applied consistently is usually accurate enough for trend purposes.
FORG tracks this automatically across every agent session — live cost attribution, budgets, and alerts.
Start tracking with FORG