Product people have two AI jobs now. One is using AI in their own work: drafting PRDs, writing user stories, synthesizing research. The other, which is harder, is deciding which AI features to build, scoping them realistically, and knowing whether they work once they ship. A feature that answers correctly 90% of the time can still be the wrong feature to launch.
GenAI & Agentic AI for Product Owners and Product Managers is a 12-hour course that covers both jobs. This guide walks through what it teaches, how it differs from general AI courses, and who it suits.
The course at a glance
| Format | Live online, three consecutive days |
| Hours | 12 |
| Modules | 6 |
| Coding during the course | None |
| Prerequisites | Python for AI pre-work (6 hours) and basic product management concepts |
| Assessment | Hands-on exercises and a final test |
| Covers | Both product owner and product manager roles |
| Current price | $450 or ₹25,000 |
Additional discounts are available. Contact us to know more.
What the six modules cover
The first three modules are about using AI in product work. The last three are about building AI into products, which is where most product teams are less prepared.
| # | Module | Part | What you practice |
|---|---|---|---|
| 1 | PRDs and user stories | Using AI | Drafting and refining requirements with AI without losing your own judgment |
| 2 | Roadmap and prioritization | Using AI | Using AI to compare options and pressure-test priorities |
| 3 | Customer research and competitive analysis | Using AI | Synthesizing interviews, reviews, and competitor information faster |
| 4 | AI feature feasibility | Building AI | Choosing between prompting, RAG, and fine-tuning for a feature |
| 5 | Evaluation design | Building AI | Writing golden sets and using LLM-as-judge to decide whether a feature is good enough |
| 6 | Responsible AI and human-in-the-loop | Building AI | Deciding where people must review AI output, using the NIST AI Risk Management Framework |
Why evaluation design matters most
If you only take one thing from the course, it’s probably module 5. With traditional features, acceptance criteria are pass or fail. With AI features, the same input can give different outputs, and “correct” is often a judgment call. Product owners who can’t define what good enough looks like end up either shipping features that embarrass the company or blocking features that were fine.
The course teaches two practical tools for this:
| Tool | What it is | When to use it |
|---|---|---|
| Golden set | A fixed list of real inputs with the answers you’d accept, reviewed by people who know the domain | Before launch and after every change to the prompt or model |
| LLM-as-judge | Using a second model to score outputs against your criteria, checked against human ratings | When you have too many outputs for people to review all of them |
Product owner or product manager?
The course covers both roles, and the split falls naturally. Product owners working in Scrum tend to get the most from modules 1, 4, and 5, which affect backlog items and acceptance criteria directly. Product managers tend to lean on modules 2, 3, and 6, which are about strategy, the market, and risk. Both roles need the feasibility module, since it decides whether an AI feature is worth starting at all.
For examples of where AI fits in each role, see GenAI and agentic AI use cases for the agile product owner role.
Who it fits, and who it doesn’t
| If you are… | Fit |
|---|---|
| A product owner or product manager about to ship an AI feature | Strong fit |
| A PM who uses ChatGPT daily but has never scoped an AI feature | Strong fit |
| A business analyst working closely with product | Good fit |
| A product leader setting company-wide AI strategy | Consider AI Transformation for Business Leaders |
| A PM who wants to build AI features personally, in code | Consider the GenAI and Agentic AI BootCamp |
| A project or program manager | Consider GenAI & Agentic AI for Project & Program Management |
Cost and dates
The current price is $450 in the US and ₹25,000 in India.
| Cohort | Dates |
|---|---|
| October | October 12 to 14, 2026 |
| November | November 9 to 11, 2026 |
| December | December 7 to 9, 2026 |
Check the course page for current dates and time zones.
Additional discounts are available. Contact us to know more.
FAQ
Why is Python listed as pre-work if there’s no coding?
So you can follow what engineers are talking about when they discuss prompts, APIs, and evaluation scripts. You won’t write code during the course.
Is there an exam?
Yes, a final test, along with hands-on exercises during the sessions.
Will this help with a certification like CSPO or PSPO?
No. It’s a separate course focused on AI, and it doesn’t prepare you for Scrum certification exams.
I’m already using AI tools every day. Will this be too basic?
The first three modules may cover some ground you know. The second half, on feasibility, evaluation, and human review, is where most experienced AI users still have gaps.