AI Product Management: Lessons from the Trenches

Managing AI products is like herding cats that can suddenly become superintelligent or completely forget how to meow. After two years of building AI-first products, here's what I've learned about the unique challenges of AI PM work.

First, your success metrics need to evolve. Traditional conversion funnels don't capture the nuanced ways users interact with AI features. You need to measure confidence scores, iteration rates, and user trust – metrics that didn't exist in the pre-AI product world.

Second, user research becomes exponentially more important. Users can't tell you what they want from AI because they don't know what's possible. You have to observe behavior, not just listen to feedback. Watch how they prompt, how they iterate, where they get stuck.

Finally, embrace the uncertainty. AI products will surprise you – both positively and negatively. Build systems that can handle edge cases gracefully, and always have a human-in-the-loop fallback. Your users will thank you when the AI inevitably does something unexpected.