[Story]: Outcome correlation layer — tie AI adoption to delivery metrics

## Summary
Build a correlation layer joining AI adoption/acceptance data against delivery outcome metrics — cycle time, change-failure-rate, and PR throughput. Every serious competitor in this space headlines outcomes rather than raw acceptance rate, precisely because acceptance rate alone is gameable and inconsistent across vendors (per the epic's core finding and story 6's normalizer caveats).

## Intent / Source of truth
2026-08-13 backlog consolidation, competitive positioning review (DX, Jellyfish, LinearB, Faros all lead with outcomes, not acceptance rate).

## Scope
- [ ] Join adoption/acceptance data (from stories 2-6) against cycle time, change-failure-rate, and PR throughput metrics
- [ ] Present as a correlation, not a causal claim, unless a rigorous methodology is in place to support causal language
- [ ] Dashboard/report view surfacing the joined data per team

## Out of scope
- Sourcing delivery-metrics data itself if it doesn't already exist in a connectable system — this story assumes cycle-time/CFR/PR-throughput data is available from an existing source (e.g. the org's CI/VCS); building that ingestion from scratch is a prerequisite to flag, not silently assume away
- Any causal-inference modeling beyond straightforward correlation, unless separately scoped

## Verification
A joined dashboard view for at least one real team showing adoption/acceptance trends alongside cycle-time/CFR/PR-throughput trends over the same time window.

## Risk assessment
Overclaiming causation from correlation would be a credibility risk for an AI-governance vendor — keep language and visualization honestly correlational.

## AI Usage Declaration
Drafted with AI assistance during the 2026-08-13 cross-repo backlog consolidation. A human owns intent, verification and consequences; acceptance criteria must be verified against the live system before this is closed.