AI Product Management 2 Years In

Marty Cagan reflects on two years of generative AI's impact on product management, distinguishing between adding AI to products and using AI to change how products are built. He admits his earlier predictions were too optimistic, since many announced changes have not yet materialized. He argues that the product manager role becomes more essential, not less, with AI-powered products, and that the impact of AI on product management is harder to measure than on engineering or design. He closes by framing a set of open questions about discovery, skills, creativity, and innovation that the industry is still working through, while suggesting the field now better understands how to pose them.

01Key takeaways

• Separate adding generative AI to your product from using AI to change how your product team works.
• Faster code or design generation does not automatically produce better product outcomes.
• Strong product judgment combined with AI tools is powerful, but giving the tools to people without that foundation is risky.
• Ask how AI changes discovery, which PM skills matter, and whether it drives optimization or innovation.
• Expect the enabling technology to keep shifting, so treat current conclusions as provisional.

02Key sections

Reflecting on earlier predictions

Cagan revisits his 2023 predictions and concludes he was too optimistic about how quickly change would arrive. Despite many announcements, substantive change has been slower than hoped.

Why the PM role matters more

He restates his earlier argument that nearly all product managers will need to be AI product managers and that the role grows harder with AI-powered products.

Scoping the discussion

Cagan notes that most commentary focuses on delivery team product owners or feature team PMs, while his interest lies in empowered product teams. He also observes that AI's effects on engineering and design are easier to discuss than its effects on product management.

Open questions for product model companies

He lists unresolved questions about how AI changes discovery, which PM skills matter, what work gets complemented or replaced, its effect on creativity, and whether it drives optimization or innovation.

Where things stand

Cagan concludes that progress is real but early, and that the field now has a better sense of the right questions even if answers remain elusive.

03From the post

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04Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.