A company Jeff Patton worked with this year shipped a glossy new release to 2.65 million customers. AI helped them get it out on time. It didn't break. There were no bugs. Nobody liked it.

Jeff, author of User Story Mapping, joins me to talk about dual track development. He says the book needs a second edition: it described a discovery team and a delivery team, but after 10 years of coaching he sees two tracks and one team, with engineers and testers pulled up into discovery.

Jeff asks the whole team, on a scale of 1 to 5, how confident they are that people will see a feature and want to try it. If one says 3 and another says 5, ask what they'd need to learn to be sure. Keep using is the hard one to test: release something usable to a small group, and in Jeff's words, "don't build it at scale."

We also get into release to learn versus release to earn, and what I've seen in client GitHub repos since they adopted AI: a lot more output, but the share of work going toward customer results is flat or down. Jeff on asking AI what customers think: "the AI is not your customer."

His homework: do an opportunity canvas for the feature you're working on right now. If you can't say who it's for, what problem it solves and how you'll measure it, stop building it.

Chapters

00:00 A release customers did not like

00:21 Meet Jeff Patton

04:54 Discovery and delivery together

07:33 Two tracks within one team

09:46 Expose the beliefs behind a feature

12:52 Where does the team's confidence differ?

13:58 A usable release that teaches you something

16:03 Learning about technical risk

17:16 Ship to learn, earn, or burn

23:24 AI output and customer outcomes

27:40 AI opinion and actual customer evidence

33:18 An exercise for the next feature

34:46 Where to learn more from Jeff

Find Jeff: https://jpattonassociates.com/