What AI means for your product strategy

Paul Adams, Chief Product Officer of Intercom, shares stories of public-speaking failure, his time at Google and Facebook, and lessons on shipping to learn. The conversation centers on how AI should reshape product strategy, drawing on Intercom's all-in pivot toward AI-driven customer support (Fin), plus practical frameworks like differentiation vs. table stakes, swinging the pendulum, and product-market story fit.

01Key learnings

• Before redesigning a product for AI, start from the core premise: why people use it and what problem it solves, then map what AI can fully replace, partially do, or augment.
• Treat AI as a strategic shift rather than a feature; Intercom ripped up its strategy and rebuilt from first principles after the ChatGPT launch.
• Build real machine learning depth on the team, but spread AI skills across product teams instead of isolating AI work in a specialist group.
• Make time to learn AI deliberately by blocking focused time for reading and hands-on experimentation, rather than relying on extra hours.
• Keep pricing simple; resist adding tiers, add-ons, and extra pricing dimensions, since they quickly make bills confusing for customers.
• Balance differentiation with table stakes on the roadmap, and recognize startups must build enough basics to let customers actually switch from incumbents.
• Ensure your product story is as clear as the product itself; great products can fail when the market narrative is convoluted or wrong.
• Read counterarguments and skeptical perspectives to temper optimism when forming strong convictions about emerging technology.

02Frameworks mentioned

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