Validate Business Impact of AI Features

Hosted by Shane Butler

240 students

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

Shane Butler is a Principal Data Scientist at Ontra, where he leads evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. His current work focuses on practical, end-to-end methods for evaluating AI features in production. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Previously at Stripe, Nextdoor, PwC