Why do most AI teams only ask "is this actually working for the business?" after it's too late? When should you start connecting evals to business impact and how do you actually do it?

Using the same medical insurance chatbot from the last episode, we show how to bridge the gap between model metrics and the outcomes your leadership actually cares about. We introduce the Eval-to-Impact Stack: a three-layer framework that connects eval metrics, product metrics, and business metrics. 

• More details are available in our Substack post: From AI Evals to Business Impact

• Interested in AI Evals and Analytics Playbook course? Here is an exclusive discount for our listeners

00:00 – Introduction & Recap of Episode 2
00:53 – Why Teams Ask the Business Impact Question Too Late
01:38 – The Stat: 95% of Enterprise AI Pilots Fail
01:58 – The Translation Problem: Model Metrics vs. Business Metrics
02:38 – Why Evals Get Labeled as Overhead (And How to Fix It)
03:16 – The Eval-to-Impact Stack: Three Layers Explained
05:00 – Applying the Framework: Insurance Chatbot Walkthrough
07:13 – Work Backwards from Business Goals, Not Forward from Metrics
08:05 – The Cross-Functional Superpower: Speaking Both Languages
08:25 – Closing: "Build the Product Right" vs. "Build the Right Product"

Stella Liu: https://www.linkedin.com/in/wenxingl/
Amy Chen: https://www.linkedin.com/in/amy17519/

More about AI Evals and Analytics -- https://ai-evals.org/

We (Stella & Amy) created the AI Evaluation & Analytics Playbook, a practical framework that helps teams ship production-ready, trustworthy AI systems.

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