AI Product Strategy & Ethics

What you will be able to do

• Set an AI product strategy and roadmap that ties model capabilities to a defensible business thesis and clear buyer value.
• Run go-to-market for probabilistic features, including pricing, positioning, packaging, and honest messaging about accuracy and limits.
• Design a metrics stack that separates model quality, product outcomes, and business impact, then instrument it for fast iteration.
• Build an evaluation and feedback loop with offline evals, online experiments, and human review to decide when to ship or roll back.
• Apply the NIST AI Risk Management Framework to map, measure, and manage risk across the AI product lifecycle.
• Assess regulatory exposure under regimes like the EU AI Act and translate obligations into product requirements and launch gates.
• Operationalize fairness, transparency, human oversight, and incident response as concrete product decisions rather than slogans.
• Align executives, engineering, legal, and go-to-market partners around one AI product plan backed by a governance model that speeds decisions.

What is inside

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

1. 01

   AI Product Strategy and Roadmap

   Advanced product managers start from a defensible thesis, not a model demo. This module connects AI capabilities to real buyer problems, weighs build versus buy versus fine-tune, and sequences a roadmap under deep uncertainty with explicit kill criteria, so you leave with a strategy narrative and a roadmap that survives contact with reality.

   2 lessons · 5 quiz questions

2. 02

   Go-to-Market and Measuring AI Features

   Probabilistic products break the launch playbooks that work for deterministic software. This module builds both halves together: a go-to-market plan (positioning, pricing, and packaging for features that are right most of the time) and the measurement stack (model quality, product outcomes, evals, and iteration) that tells you what to ship next.

   2 lessons · 5 quiz questions · assignment

3. 03

   Responsible AI and Ethics in Practice

   Responsible AI is a set of product decisions, not a values statement in a deck. This module turns fairness, transparency, human oversight, and data ethics into requirements, guardrails, and launch gates you can actually ship, and it drills the reasoning you need when user value, business goals, and potential harm collide.

   2 lessons · 5 quiz questions

4. 04

   AI Risk, Governance, and Stakeholder Alignment

   Shipping AI at scale means managing risk without freezing the roadmap. This module applies the NIST AI Risk Management Framework and maps obligations from regimes like the EU AI Act into concrete launch gates, then builds a lightweight governance model. You finish by aligning executives, engineering, legal, and go-to-market around one plan.

   2 lessons · 5 quiz questions · assignment