Free Lesson
Validate Business Impact of AI Features
Part of The AI Evaluation Handbook
60 min
Jan 30, 2026 3:00 PM
Virtual (Zoom)
In this video
What you'll learn
Connect AI quality to outcomes people care about
Build a metric chain from AI output quality to a North Star proxy, then to a business metric (retention, conversion).
Tell whether your AI metric is decision-worthy
Learn 3 ways to test if quality moves the North Star: A/B tests, rollout comparisons, and directional evidence.
Forecast AI feature impact before you invest months of work
Turn the chain into a quick forecast so you can set rollout gates, prioritize work, and avoid over-optimizing.
Learn what to do when AI fails to link to business impact
Common reasons quality improves but the business does not, and how to diagnose where the link failed.
Why this topic matters
AI features rarely improve revenue or retention directly. AI Quality changes show up first in the output, then in user behavior, and only later in business impact. That indirect path is easy to misread, so teams can end up celebrating AI "wins" that do not actually move the business. This lesson shows practical ways to test whether your quality improvements actually drive results.
You'll learn from
Shane Butler
Principal Data Scientist, AI Evaluations at Ontra
Previously at Stripe, Nextdoor, PwC
