AI and product management

Marily Nika, a product lead at Meta with prior Google experience and creator of a popular Maven course on AI and product management, discusses how product managers can understand and apply AI. The conversation covers using ChatGPT in daily PM work, how models are trained, when AI is and isn't the right solution, and how PMs can start building AI skills.

01Key learnings

• Avoid the shiny object trap: only add AI when there is a real, well-defined pain point worth solving, and identify the problem before choosing an implementation.
• Don't use AI for an MVP; fake the AI behavior with a clickable prototype and test desirability with users first.
• Use ChatGPT as a sounding board for sharpening mission statements and generating user segments, personas, and motivations, while keeping the core thinking yourself.
• Treat data as an asset: even basic data collection or a data science intern can reveal AI opportunities, and PMs should think about what existing data could be leveraged.
• Expect AI work to involve uncertainty and longer experimentation cycles than typical launches, and set expectations with leadership about what progress means.
• Use adjacent successful products and a clear rollback plan to build trust and buy-in for big ML bets.
• Learn the fundamentals, such as how models are trained and what data they need, through short courses or hands-on coding so you understand the tools you rely on.
• Build and teach something yourself, since explaining concepts and handling student questions sharpens your understanding of AI.

02Frameworks mentioned

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