How much about AI should your average product manager know?

A product manager doesn’t need to be an ML engineer, but should understand enough AI to make good product decisions, assess risk, and communicate clearly with stakeholders. Practically, that means:

• Core concepts: difference between supervised/unsupervised/foundation models, what prompts do, and typical failure modes (hallucination, bias, brittleness).
• Business implications: when AI adds real value vs. when it introduces cost, latency, or abuse surface; how to set success metrics tied to business outcomes.
• Data & evaluation: importance of data quality, labeling effort, test sets, and human-in-the-loop evaluation strategies.
• Operational realities: inference costs, latency, versioning, monitoring, and rollback plans.
• Ethics & compliance: privacy, fairness, explainability, and regulatory constraints that affect launch and marketing.
• Collaboration skills: translate product goals into technical requirements, prioritize trade-offs, and design experiments.

With this baseline you can scope feasibility, prioritize features, and mitigate risks without deep coding.

What kind of product and team (size/ML expertise) are you working with?