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The AI ROI report (Pro, admin-only) answers the question every engineering leader gets asked: “what are we actually getting from AI coding tools?” It fuses two sources you already have in DevPerform:
  1. What your engineers report — three survey questions from the AI impact set: weekly time saved by AI tools, confidence in AI-generated code quality, and the share of work that’s substantially agent-written. Responses aggregate at team level with the same anonymity suppression as every other survey result.
  2. What the code shows — AI-authored share of merged PRs and AI-vs-human rework, from commit-trailer detection on your git history. No agents or daemons on developer machines.

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The math, transparently

  • Estimated savings = median reported hours saved per week × active contributors × a loaded hourly cost you enter (median, so one enthusiastic answer can’t inflate the number; the top “16h+” bucket is deliberately capped at 20h — half a work week — in the math. Surveys sent on the older 5-bucket scale keep their original 10h cap).
  • Your hourly-cost and AI-spend inputs live only in your browser — DevPerform never stores compensation data.
  • Entering monthly AI spend adds a net-ROI line and cost per AI-assisted PR.
  • Telemetry is shown alongside the self-reported figure, never multiplied into it — self-reports are perception data, telemetry is a lower bound, and the report says so on the page.

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What published research says

The report includes a collapsible summary of peer-reviewed and published studies on AI coding productivity — results genuinely range from negative (a 2025 randomized trial with experienced open-source maintainers found tasks took 19% longer with AI) to strongly positive (a 2023 controlled experiment measured 55.8% faster completion on a scoped task), with large field experiments landing in between (+26% across ~4,900 developers). Each study is linked with its scope caveats. That spread is exactly why the report leads with what your engineers report, corroborated by your git telemetry, instead of applying a universal multiplier. Some tools compute AI ROI by tracking individual developers before and after AI adoption. DevPerform doesn’t — adoption and impact are measured for teams only, by design. See the AI Impact views for team-level cohort and trend analysis.