In this episode, Jeff Gothelf and I break down:

1. How OKRs expose bad leadership assumptions
2. Why most OKRs don’t actually measure success
3. How to use OKRs to push back—without career risk
4. Why customer behavior, not feature launches, should define success
5. How product discovery should be continuous, not a one-time study
6. Why designers, PMs, and AI teams need OKRs that track learning, not just output
7. How to create a culture of continuous learning—where teams challenge assumptions, test ideas, and adapt fast

Because if your OKRs aren’t helping you challenge bad decisions, they’re useless.

0:00:00 Trailer

0:00:40 Introduction of Jeff Gothelf

0:01:49 Traditional user research vs. product discovery

0:02:53 Why traditional research is unsustainable

0:04:22 Emergence of product discovery

0:05:47 Canvases for extracting assumptions

0:06:56 Why shared understanding in teams is important

0:07:36 Lean UX Canvas

0:08:57 Evolution to Lean Product Canvas

0:11:19 OKRs and his latest book "Who Does What by How Much"

0:13:54 Why did they write the book?

0:15:19 Common Pitfall: Writing key results as task lists

0:16:18 "Who does what by how much" framework

0:17:35 Behavior change as measure of success

0:18:14 Reconciling OKRs with AI prototypes

0:19:19 Why being deliberate with AI implementation is important

0:19:50 Customer-centric OKRs for an email client using AI

0:21:25 Using AI to deliver a real user need

0:23:13 Thoughts on experimentation in the current environment

0:24:23 What's stopping us from embracing experimentation

0:26:29 Using OKRs to challenge leadership decisions

0:27:40 Framing course corrections with objective evidence

0:29:11 How leaders can make a safe environment

0:30:20 Starting a career in the age of AI

0:32:32 Tips for emerging consultants

0:34:28 Fear of sharing intellectual property

0:36:22 Advice for aspiring serial book writers

0:39:23 Closing