This unit synthesizes proactive AI-first product management practices, cross-functional collaboration, and responsible-AI decision-making. Learners apply ideation techniques and stakeholder coordination to anticipate user needs, evaluate ethical and feasibility trade-offs, and produce actionable artifacts for handoff to engineering and data teams.
Learning Objectives
- Analyze product telemetry and user signals to identify and prioritize at least three AI-driven feature opportunities and produce a 3-month prioritized roadmap with clear success metrics.
- Apply cross-functional collaboration frameworks to create a stakeholder RACI and an engineering handoff checklist that align PM, design, data science, and legal requirements for an AI feature.
- Demonstrate responsible-AI practice by conducting an ethical and feasibility assessment for a proposed AI use case that includes bias risk analysis, privacy considerations, and mitigation strategies documented in a review report.
- Evaluate and generate a minimum of three AI/ML use-case concepts using structured ideation techniques, scoring each against user impact, technical feasibility, and business viability with a provided rubric.
Curriculum
- 1 Section
- 6 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 4 — Proactive AI Product Leadership6
- 1.1Proactive Product Management—Anticipating User Needs with AI20 mins
- 1.2How the AI-Experiences PM Interacts with Other Roles20 mins
- 1.3Responsible AI Foundations—Ethical and Feasibility Considerations20 mins
- 1.4and Key Takeaways20 mins
- 1.5Practical Ideation Techniques for AI/ML Use Cases—Thinking Like an AI-First Product Manager20 mins
- 1.6and Key Takeaways20 mins
Instructor
Free
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