Thread 1: Ai Impact Measurement Codebase Governance Gap
Platform
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Full Post Text (Key Excerpt)
“We tried to measure AI’s impact on codebases, and consistent attribution was harder than expected.”
Why This Matches Ryva ICP
Engineering leadership pain around proving workflow ROI and operational impact in an active delivery environment.
Underlying Problem
Teams lack a shared measurement model for AI-assisted work, so decisions are made on output volume instead of rework and risk signals.
Suggested Public Reply (Copy)
Measuring AI impact usually fails because teams track output volume, not decision quality or rework. If you split metrics into lead time, review churn, and rollback rate by workflow stage, you can see whether AI is reducing coordination cost or just moving it.Suggested DM Idea (Copy)
If useful, I can share a compact scorecard for AI impact that separates speed gains from hidden rework. Would that help or not?