Written by the AgileFever editorial team. Program details were checked against the live program pages in October 2026. We teach the Product Owner and Product Manager course mentioned below, so read its description as a vendor view.
Product people already spend their weeks on requirements, roadmaps, customer research and stakeholder communication. AI helps with the drafting. The part that now sets people apart is different: scoping an AI feature honestly, and knowing whether it works once it exists.
The six skills and the decision each one improves
| Skill | Decision it improves | What you should be able to produce |
|---|---|---|
| Requirements and PRDs | What exactly are we building, and for whom | A PRD with acceptance criteria and listed edge cases |
| Prioritisation synthesis | What goes first | A ranked backlog and a roadmap narrative from usage and feedback data |
| Research synthesis | What do customers need | Structured insights from interview notes and competitor research |
| AI feature feasibility | Can this actually ship | A scoped feature with a reasoned choice between prompting, RAG and fine-tuning |
| Evaluation design | Is the output good enough | A golden set and a scoring method (rubric, human raters or a model as judge) |
| Responsible AI and oversight | When must a person step in | Escalation rules and a risk checklist before launch |
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Why evaluation is the skill to learn first
A model can look excellent in a demo and fail in production, so someone has to define what “good” means. That someone is often the product owner. A golden set is a fixed list of realistic inputs with the answers or behaviours you expect. You run it every time a prompt, model or document set changes. A new model version can move your scores up on some questions and down on others, which is why a fixed set beats a feeling.
| Scoring method | Good for | Watch out for |
|---|---|---|
| Rubric scored by people | Tone, helpfulness, policy fit | Cost and inconsistency between reviewers |
| Model as judge | Fast checks at scale | Bias toward its own style; sample and audit it |
| Exact-match tests | Facts, formats, calculations | Misses answers that are correct but worded differently |
How the AgileFever course covers it
GenAI & Agentic AI for Product Owners and Product Managers is 12 hours with six modules, three generative (PRDs and user stories, roadmap and prioritisation, customer research) and three agentic (AI feature feasibility, evaluation design, responsible AI and human-in-the-loop). It costs $450 in the US. You complete the 6-hour Python for AI course first, no coding is required during the course itself, and no prior AI experience is needed. You pass a final test and practical exercises to receive the certificate. The page is clear that the depth is aimed at decision-making, not at replacing a data scientist.
The six course modules
Related reading: Product Owner to AI Product Owner roadmap, who owns the AI roadmap and the complete course guide.
Questions people ask
Do I need a data science background?
No. The course expects you to read an evaluation and scope a feature with data and ML teams, not to build models.
Is this only for product owners?
Product managers, program managers moving into product work, and business analysts are also listed as fits.
What if my company has no AI features yet?
The generative modules still apply to everyday product work. Evaluation and feasibility are worth learning before the first AI feature, not after.
