Jeff Gothelf

I was at a family event recently and ran into two cousins.

Last Tuesday I posted on LinkedIn that project managers don’t exist.

Last November I made the argument that AI was making Lean UX foundational.

With the inevitable onslaught of executive directives to transform into an “AI-first” company and “integrate AI” into our ways of working, teams are now taking on their regular day-to-day product work while also trying to shift their processes and methods…

Earlier this summer, HBR published the results of a multi-year field study by Anne-Sophie Mayer, Elmira van den Broek and Tomislav Karačić that followed people who had to explain and defend AI-made decisions.

In the webinar Josh Seiden and I ran on September 10 about what product management looks like in 2027, the top-voted question came from a product manager who had clearly been waiting for their opportunity to ask us this.

The promise of AI, at least for now, has been greater efficiency, a freedom from boring redundant tasks and the ability to free up time to do “more important” work.

Albertson’s, the US grocery chain, told the Wall Street Journal in August 2026 that shoppers who used its AI shopping assistant spend 10% more when using the conversational search bot and up to 26% more when they use the robust version of their assistant.

I take every August off.

This is the second summer in a row that I’m handing you a clip show.

Nearly every customer interview technique rests on the idea that the person across from you has already done the thing you are asking about.

This week’s blog post was inspired by the webinar we did last week with Barry O’Reilly.

A note about the scope of this article: this post is about the product feedback loop that PMs and their teams collect and act on.

Almost every prioritization framework we use (RICE, weighted shortest job first, the humble value-versus-effort quadrant) was built to protect what was once the scarcest resource of all, engineering time.

Before you read this post I wanted to note that I do not cover Objectives in this article.

That MIT stat is going around again about how some enormous share of enterprise AI pilots never make it past proof of concept.

We work with a lot of enterprise level teams who are eager to deploy and integrate AI tools and tech into their teams.