AI Product Management Foundations

What you will be able to do

• Explain how managing AI products differs from traditional product management across discovery, metrics, delivery, and the post-launch lifecycle.
• Describe in plain terms what current machine learning and foundation models can and cannot do, and flag claims that cross that line.
• Match a business problem to a fitting AI approach, or correctly decide that no AI is needed.
• Source and frame high-value AI opportunities using jobs-to-be-done and opportunity mapping.
• Prioritize competing AI ideas by value, confidence, and effort so the roadmap reflects real payoff.
• Run a structured feasibility review across data readiness, model performance, cost, latency, and error tolerance.
• Write a defensible build, buy, or skip recommendation, backed by a clear go/no-go memo.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Why AI Products Are Different

   AI products behave unlike the deterministic software most product managers grew up on. Outputs are probabilistic, the training data is part of the product, and a shipped model drifts and decays instead of staying finished. This module resets your mental model so you can run discovery, define success, and manage the lifecycle for systems that learn, keeping the PM habits that still hold and replacing the ones that quietly break.

   2 lessons · 5 quiz questions

2. 02

   What AI Can and Cannot Do

   A non-technical but honest map of modern AI for product managers. You will learn the three learning paradigms (supervised, unsupervised, and reinforcement), how classical models differ from foundation models and LLMs, and which tasks each one is actually good at. Then you will study the failure modes that separate a real capability from a flattering demo (bias, hallucination, and drift), and learn to plan every AI feature around confidence scores and an explicit error tolerance.

   2 lessons · 5 quiz questions · assignment

3. 03

   Finding High-Value Opportunities

   AI creates value in a few recognizable shapes: prediction, classification, ranking, generation, and extraction. This module teaches you to start from a real user or business problem using jobs-to-be-done and opportunity mapping, then test whether AI is genuinely the best tool for the job. You finish by building a prioritized slate that separates high-payoff bets from novelty projects chasing the hype cycle.

   2 lessons · 5 quiz questions

4. 04

   Assessing Feasibility and Deciding

   A promising idea is not a fundable one until it survives a feasibility review. This module follows Nadia Osei, an AI product manager at Marlow, a mid-market expense and accounts-payable software company, as she puts a favored feature called AutoCode through the four checks that make or break AI projects: data readiness, a performance target worth defending, cost and latency at production volume, and the real cost of a wrong answer. You then turn those findings into a build, buy, or fine-tune decision, run a responsible-AI risk check with the NIST AI Risk Management Framework, and write a one-page go/no-go memo a stakeholder can act on.

   2 lessons · 5 quiz questions · assignment