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- Core Tech
Product Management: Managing AI Products
AI features are now expected in your products, but managing them like deterministic software leaves you exposed: output that’s confident, well-formed, and wrong passes as success, and performance quietly shifts after launch. This path is for product managers who need to move past concepts and actually build, ship, and maintain AI features with a clear view of how they behave and fail.\ \ In the Product Management: Managing AI Products Skill Path, you’ll decide whether a problem warrants AI and which build path it implies, frame business goals as testable hypotheses with paired quality and outcome metrics, design experiences that set accurate expectations and keep users in control when the AI is wrong, and evaluate, launch, and sustain AI products as their behavior and obligations change. The path is tool-agnostic, so the methods apply whichever models or platforms your team uses.\ \ By the end of this path, you’ll be able to own an AI feature from first framing through production monitoring and handoff.
**This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Product Management: Managing AI Products
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What You'll Learn
- Decide whether a problem truly needs AI, then frame it as a testable hypothesis with paired metrics for output quality and business outcome
- Design AI experiences that set accurate user expectations and keep people in control when the output is wrong
- Evaluate AI features before launch and monitor output quality in production so you catch degradation before your users do
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- 1. Managing AI Products: Getting Started \
- 2. Managing AI Products: Framing AI Problems \
- 3. Managing AI Products: Designing the AI Product Experience \
- 4. Managing AI Products: Evaluating, Launching, and Sustaining AI Products
- You should already be a working product manager who is comfortable with core practice beyond the foundations: discovering and validating user problems, writing requirements, prioritizing a roadmap, defining success metrics, and working day to day with engineering and design. You don’t need hands-on machine learning, data science, or coding experience, and no specific AI tool or platform is assumed.
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