AI Product Management 101: From Problem to AI Opportunity
Priyanka Shetty
AI Product Leader & Builder
Decide where AI belongs in your product. One weekend, one AI Opportunity Brief.
Every product manager has already heard it: "Can we add AI to this?" Leaders want it, customers ask about it, and competitors keep announcing it. The PM is the person who needs to know how to turn that request into a decision.
This two-session weekend course gives you a method. You work with TrailStart, a fictional hiking app with six AI ideas on the table: a trail recommender, a review summarizer, a chat assistant, a packing list generator, a closure alert writer, and a support message sorter. Using a five-question AI-fit screen, you decide which deserve AI, which a simpler solution fits better, and which carry too much risk.
Then you take your strongest idea through value, effort, data, risk, and a safeguard users can see. You study three real-world AI cases, write a success measure, and make the call: build it, test it first, or skip it. On Sunday you apply the method to an idea of your own.
You leave with a completed AI Opportunity Brief. No AI tool account or technical background is needed.
What you’ll learn
Learn a clear method to decide where AI belongs in your product, and respond to every “add AI” request with confidence and evidence.
Use an eight-term glossary and a capabilities guide to talk confidently with engineers and executives.
Recognize the four kinds of AI help in a product: recommend, summarize and write, sort, and chat.
Apply the screen to six TrailStart ideas and give each a verdict: AI, simpler solution, or not yet.
See why question order matters, so simple rules or better design win when they should.
Rate effort as light, medium, or heavy, and check whether the data you need exists.
Complete the opportunity blocks of your AI Opportunity Brief on a realistic scenario.
Study three public AI cases and name which screen question each one tests.
Map your top two risks and design one user-facing safeguard with a human fallback.
Write a first success measure with a guardrail that tracks the value you promised.
Practice the judgment calls PMs face: what to build, what to cut, and when to say no.
Draft the problem, the screen verdict, and a recommendation for your own product idea.
Review a peer's brief against a rubric and improve one part of your own.
Leave with a completed brief, a blank template, and a checklist you can reuse.
Learn directly from Priyanka
Priyanka Shetty
AI Product Leader (Oracle, S&P Global, Weill Cornell) | AI Strategist & Founder
Who this course is for
Working Product Managers
Who are asked to “add AI” and want a clear way to decide where it belongs.Graduates of Product Management Fundamentals
Who have learned the basics of PM work and want to understand how AI can improve the product.Founders, designers, and analysts.
Who are moving toward product ownership and want to make smart calls about AI features
What's included
Live sessions
Learn directly from Priyanka Shetty in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
AI Opportunity Brief
A completed AI Opportunity Brief for TrailStart (one page, six blocks), built across both sessions.
A one-page glossary and a one-page capabilities guide
Two plain-language one-pagers you receive after enrolling. The glossary explains eight AI terms, from model to hallucination, with simple examples. The capabilities guide shows what AI does well and where it struggles across four common uses: recommend, summarize and write, sort, and chat. No technical background needed.
Your own AI Opportunity Brief starter
A blank brief with sentence starters, plus the first draft of your own idea that you start in Session 2. Finish it after the course and reuse it on your next AI request.
Five-question AI-fit checklist and rubric
The one-page screen you use to judge any AI idea, plus the rubric you use to review your own work and a peer's. Keep both for your next AI conversation at work.
Case notes on three real-world AI cases
Short notes on three public AI cases, with source links, showing which screening question each one tests and which safeguard applies.
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Course syllabus
2 live sessions • 6 lessons • 2 projects
Week 1
Nov
14
Live Session 1: Find, Screen & Size AI Opportunities
Sat 11/145:00 PM—8:00 PM (UTC)Nov
15
Live Session 2: Protect, Decide & Improve
Sun 11/155:00 PM—8:00 PM (UTC)
Find, Screen & Size AI Opportunities
Protect, Decide & Improve
Frequently asked questions
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