How We Cut Regression Time ~50% with AI Test Generation in 60 Days

Most AI testing pilots fail for predictable reasons: no baseline metrics, scope too wide, no human review gate, and no CI owner. The teams that win treat AI like engineering change, not a tool purchase.

The 60-day pilot pattern

1. Pick one journey — e.g. onboarding, checkout, or advisor workflow—not the whole portfolio.
2. Baseline week 1 — manual regression hours, escaped defects, flaky rate, coverage on critical paths.
3. Integrate in CI — generated tests must run on every PR with clear failure ownership.
4. Human review gate — testers approve prompts, assertions, and data fixtures before merge.
5. Measure bi-weekly — coverage %, regression duration, escape rate; adjust scope, not vanity metrics.

Results we've seen

In recent engagements (wealth management and SaaS), teams moved regression coverage from roughly 1% to 40% on in-scope journeys within ~60 days, while cutting manual regression effort materially. Severity-1 escapes dropped when triage analytics were paired with generation—not generation alone.

When AI testing fails

• No executive sponsor—pilot dies when the champion rotates
• Security not engaged until week 4
• 100% auto-merge of AI output (false positives erode trust)
• Success defined as "tool installed" instead of cycle-time or escape metrics

Next step

Score your readiness with our AI QA Readiness Scorecard, then book a discovery call to scope a 30-day pilot.