AI for Product Managers

Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hours
Cost: $1,495

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

Generative AI is changing what a product manager can deliver in a day — but most PMs are still using it like a faster search engine. This immersive, hands-on three-day course closes that gap. It prepares mid-level product managers in enterprise environments to put AI to work across the full product lifecycle, leaving with a set of working artifacts you build live, not just notes you take.

Designed around real enterprise product work, the class covers what generative AI can and can’t do, how to prompt it like a PM, and where it earns its keep across seven core product competencies. You’ll work a single realistic case study — FinServ mobile onboarding — from market research through to a functional prototype and an executive-ready roadmap, while carrying a product of your own in parallel.

Along the way you’ll build a reusable prompt library, discovery and requirements artifacts, a working prototype, a metrics plan, an AI governance audit, and a personal 30/90-day adoption roadmap. By the end of Day 3, you’ll be ready to apply an AI-enabled approach to product management with whatever toolset your organization runs.

What you will earn

You’ll leave able to apply AI across the full arc of product work — not in theory, but with artifacts you build live in class. Every capability is taught hands-on against a realistic enterprise case study, then transferred to a product of your own.

In this course, you’ll learn to:

Explain what generative AI can and can’t do for product work — and catch the “confidence trap” before polished output reaches a stakeholder
Prompt effectively and iteratively with a repeatable five-element structure that holds up across Claude, ChatGPT, Gemini, and Copilot
Accelerate discovery — run AI-assisted market research and turn customer interviews into themes, jobs-to-be-done, and real insight
Generate enterprise-quality requirements — user stories, acceptance criteria, and edge cases — from raw discovery output
Use AI as a decision partner for prioritization, trade-off analysis, and strategy stress-testing
Analyze product data, build a metrics plan, and verify AI’s output before you trust it
Generate functional, clickable prototypes from a prompt and refine them in plain language
Communicate one product update to every audience — exec, engineering, customer, and board — from a single source
Automate recurring PM work with agentic, connector-based AI workflows
Manage AI governance, ethics, data privacy, and bias in enterprise product work — and leave with a personal 30/90-day adoption roadmap

Built on the GenAI Product Framework: seven product competencies on a foundation of AI Fluency — Strategy & Decision Support, Market Research & Synthesis, Requirements Generation, Data Analysis & Metrics, Prototyping & Concept Validation, Stakeholder

Communication, and Workflow Automation & Agents — each grounded in core fluency skills: prompting patterns, model selection, verification, enterprise data hygiene, and bias awareness.

What you will learn

Foundational Product Management knowledge – through either formal training or experience in the role
Access to at least one generative AI tool during class (two would be preferred) – Claude, ChatGPT, Gemini, or Copilot.

This course is great for

Product Managers at medium to large enterprises
Product Directors
Product Marketing
Operations / Digital Transformation Managers

Topics Covered

Twelve sections across three days, built around key Product Management competencies and anchored in a single realistic enterprise case study. Lecture stays tight — most of class is spent building.

Day 1 — Orientation, AI Basics + Discovery

Focus: Orientation & AI Fluency (2 sections), Market Research & Synthesis, Requirements Generation

Section 1 — Orientation + GenAI Foundations for PMs

What this class is – and isn’t. A brief overview of the AI landscape and how generative AI is reshaping product management.
The PM productivity gap and where AI fits (brief framing, not a lecture)
What GenAI actually is, in plain language: AI / ML / generative AI / LLMs, and what LLMs do (and don’t) — language engines, not search engines
Capabilities and limits: where GenAI excels, where it falls flat, and the gray zone
The five PM capability patterns — summarize, analyze, create, predict, recommend — with a FinServ-onboarding example for each
Practice: “capability” drill — score a set of product-delivery tasks for PM-usefulness, and spot the one that’s a bad fit for AI

Section 2 — Prompting Like a PM

How the major tools differ — Claude, ChatGPT, Gemini, and Copilot on the same task, and when to reach for which
The five-element prompt anatomy: role, context, task, format, and constraints
The iteration loop — refining a prompt one element at a time instead of starting over
Holding context across a working session, plus persistent setups like Claude Projects and custom GPTs
Enterprise guardrails: what’s safe to put into a public LLM — PII, IP, confidential roadmaps, and the rules that govern them
Practice: take a crude prompt to executive-grade in four passes, and add your best to a reusable prompt library

Section 3 — Market Research & Synthesis

Where AI fits across the seven product pillars — a quick orientation before the hands-on work
AI-powered competitive and category research using deep-research tools
Synthesizing customer interviews into themes, jobs-to-be-done, and pain points
The observation-vs-insight line: AI surfaces observations fast, but the insight stays human
Spotting hallucinations and building a verification habit you can trust
Practice: turn three raw customer interviews into a synthesized themes-and-insights brief

Section 4 — Requirements Generation

The discovery-to-PRD chain: how raw inputs become structured requirements
From meeting notes to user stories, acceptance criteria, and edge cases
Pressure-testing requirements against enterprise PRD standards
The “red-team my requirements” pattern — using AI to surface what you missed
Practice: turn a set of meeting notes into review-ready user stories and acceptance criteria, then capture the one workflow you’ll try on real work tomorrow

Day 2 — Strategy, Building + Analyzing

Focus: Strategy & Decision Support, Data Analysis & Metrics, Prototyping & Concept Validation

Section 5 — Strategy & Decision Support

AI as thinking partner vs. AI as scribe — and when each earns its place
Prioritization with AI: running RICE and trade-off analysis, then defending your call
The “skeptical CEO” stress test — using AI to red-team your own strategy
Pre-mortems on demand: surfacing failure modes before you commit
Practice: prioritize a backlog under real constraints and produce a one-page opportunity brief for your own product

Section 6 — Data Analysis & Metrics

Exploring product usage data with AI: finding patterns, anomalies, and segments
SQL without the syntax — describing a question in plain English and refining the query
Reading an A/B test honestly: drafting a readout that names its caveats
Building a metrics tree with AI as your collaborator
The critical-thinking check: catching the subtle error in AI-generated analysis
Practice: turn a raw usage dataset into a findings readout, then pressure-test an AI analysis for errors

Section 7 — Prototyping & Concept Validation

The shift from describing wireframes to generating working prototypes
Turning a single prompt into an interactive prototype in Claude, ChatGPT Canvas, or Gemini Canvas
Refining a concept in plain language — “make the empty state more helpful,” “add a confirmation step”
Practice: build a clickable prototype for one feature of your product, from prompt to working concept

Section 8 — Prototyping continued + user research integration

Heuristic review on demand: getting AI to critique an existing mockup
Generating and refining customer journey maps
Sharing work in progress and learning from the room
Practice: produce an annotated design critique and a journey map, then transfer one of today’s workflows to your personal project

Day 3 — Communicating, Automating, Governance + Capstone

Focus: Stakeholder Communication, Workflow Automation & Agents, Governance, end-to-end capstone + Personal AI Roadmap

Section 9 — Stakeholder Communication + AI Governance

The translation problem: one message, many audiences
Turning a feature list into a roadmap narrative that lands
Rehearsing difficult conversations — pushback from engineering or sales — with AI as a sparring partner
Governance and ethics for enterprise PMs: data classification and retention, bias, and stakeholder transparency
Practice: turn one product update into four audience-ready artifacts — exec summary, eng kickoff, customer announcement, and board bullet — then run an ethics audit on your own work

Section 10 — Workflow Automation & Agents

The agentic shift, in plain terms: from “AI writes for me” to “AI works for me”
Connector-based workflows that read your shared drives and tools
Ready-to-steal patterns: weekly status synthesizer, backlog deduper, release-note generator, meeting-prep agent
Practice: build a reusable PM assistant — a custom project, Gem, or GPT — loaded with your own context

Section 11 — Capstone Sprint (full framework, end-to-end)

A fresh executive-sponsor ask, dropped in live
The full framework end-to-end on your own product: research → requirements → prototype → metrics plan → exec summary
Peer consulting: a built-in panel of PMs sharpening each other’s work
Practice: run the complete GenAI Product Framework on your personal project in a single focused sprint

Section 12 — Showcase, Critical Use + Personal AI Roadmap

Capstone showcase: see what the cohort built
When NOT to use AI: hallucination patterns, judgment calls, IP and compliance traps, and stakeholder trust
The PM’s evolving skillset — what to double down on, and what to hand to AI
Resources, community, and follow-up office hours
Practice: finalize your personal 30/90-day AI roadmap, including the one workflow you’ll deploy in week one

Upcoming Sessions