AI for Product Management: Core

Product managers learn to use AI across discovery, specs, and prioritization. The course covers synthesizing research, writing specs and PRDs, roadmapping and prioritization, and working with metrics. Participants finish able to move from insight to plan much faster.

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Structuring Unstructured Feedback with AI — Learn how to ingest raw customer support tickets, transcripts, and reviews into AI tools to automatically categorize and cluster user feedback.
Synthesizing Research and Identifying Core Pain Points — Practice evaluating AI-generated research summaries to separate high-value user insights from noise and avoid synthesis bias.
Drafting AI-Assisted User Personas — Utilize synthesized research data to prompt AI to generate realistic, data-backed user personas and user journey maps.
Prompt Engineering for PRDs and Feature Specs — Master structured prompting techniques to generate comprehensive first drafts of PRDs, including system behaviors and user stories.
Defining Edge Cases and Acceptance Criteria — Use AI to stress-test your product specifications by generating unexpected user behaviors, technical constraints, and robust acceptance criteria.
Collaborative Spec Refinement with AI — Learn how to use AI as an interactive sounding board to refine technical feasibility and negotiate scope before engineering handoff.
Setting Up AI-Assisted Prioritization Frameworks — Configure AI models to score feature backlogs using standard frameworks like RICE or Kano based on qualitative and quantitative inputs.
Trade-off Analysis and Roadmapping Scenarios — Navigate complex resource constraints and stakeholder demands by generating and comparing alternative roadmap scenarios using AI.
Mapping Dependencies and Release Milestones — Use AI to analyze cross-team dependencies and automatically draft logical release phases and milestone definitions.
Writing SQL and Analytics Queries with AI — Translate plain-English product questions into accurate SQL queries and analytics tracking plans directly within your data tools.
Interpreting Metric Anomalies and Funnel Drops — Analyze performance data using AI to identify potential root causes for sudden metric drops or shifts in user behavior.
Drafting Data-Driven Product Recommendations — Synthesize quantitative metrics and qualitative insights with AI to build compelling, evidence-based recommendations for stakeholders.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to AI Ethics, Generative AI Concepts, Data Analysis, Content Strategy, Critical Thinking.

AI Ethics
Generative AI Concepts
Data Analysis
Content Strategy
Critical Thinking

Who it is for

PMs, product leaders. The material is pitched at intermediate level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: Product track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

See how this training works for the whole function on AI training for Product.

Also in this track

AI for Product Management: Essentials — Learners will be able to use AI for the everyday work of product: understanding feedback, writing things down, and keeping people informed. The course covers turning customer comments into three clear themes, drafting a one-page idea write-up, and asking AI to argue against a plan before committing. They leave with faster product habits and sharper write-ups.
AI for Product Management: Advanced — An advanced course for PMs, product leaders who already use AI in daily work. It moves beyond the essentials into advanced discovery synthesis, spec automation, metric-driven iteration, and AI-feature strategy, with an emphasis on quality control, automation, and judgment at scale.
Product Management with AI — Use AI across discovery, specs, and prioritization

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-PRD.