Product management in the AI era: stack decisions, AI feature prioritization, business case, ROI, workflow integration, and the PM role in products that use artificial intelligence.
The PM in AI products needs to understand technical trade-offs (model, cost, latency), translate technical capability into business value, design governance and the human experience around AI, and build the business case. The model is one piece — the product is the whole, including workflow, data, people, and process.
Prioritize by value generated vs delivery and operational cost. A feature that saves ten hours of manual work per week is worth more than the cents in tokens. Also consider governance maturity, technical feasibility, and alignment with existing workflow. AI features that require process redesign cost more to deliver.
Figma published a map with 13 AI tools organized by product lifecycle stage. The point is not to know all of them, but to understand which problems each category solves: research, ideation, prototyping, analysis, documentation. Knowing the landscape helps make better stack decisions.