Product Training

Two courses. One goal: better product decisions.

Upskill your product organization to thrive in a product operating model and put generative AI to work across the full product lifecycle. Both courses run live, hands-on, and can be delivered privately to your team.

AI for Product Managers Product Thinking Register interest

Choose your starting point

Which course fits

Half day · any team member

Product Thinking

For everyone around the product — engineers, designers, analysts, coaches, delivery leads, and the business partners who work alongside them. Shifts the conversation from when something ships to why it matters, so the whole team can contribute to the decision instead of just executing it.

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Three days · practicing PMs

AI for Product Managers

An immersive, hands-on class for mid-level product managers in enterprise environments. You leave with working artifacts you built live — a prompt library, discovery and requirements output, a functional prototype, a metrics plan, and a personal adoption roadmap.

See the full syllabus →

Three-day instructor-led class for practicing PMs

AI for Product Managers

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 enterprise case study, 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.

Format: live online, three sessions of 4.5 hours.

What you will learn

Capabilities you build live

You’ll leave able to apply AI across the full arc of product work — not in theory, but with artifacts, skillsets, and competencies 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.

  • 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. Underpinning that are the core fluency skills you’ll pick up along the way: prompting patterns, model selection, verification, enterprise data hygiene, and bias awareness.

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, doing, and applying.

Day 1 — Orientation, AI Basics + Discovery Orientation & AI Fluency, 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
  • 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: a “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
  • 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 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 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

What you will need

Prerequisites

  • Foundational product management knowledge — through either formal training or experience in the role
  • Access to at least one generative AI tool during class, two preferred — Claude, ChatGPT, Gemini, or Copilot

This course is great for

Who should attend

  • Product managers at medium to large enterprises
  • Product directors
  • Product marketing
  • Operations and digital transformation managers

Register interest Bring this to your team

Half-day virtual class · for any team member

Product Thinking

Most teams are fluent in when — when it ships, when it’s done, when the next one starts. Far fewer are fluent in why. This four-hour course gives everyone around the product the language and the tools to join that conversation.

It’s built for the whole team, not just product managers: engineers, designers, analysts, coaches, delivery leads, and the business partners who work alongside them. Wherever you sit, you make product decisions — about scope, sequence, and trade-offs. This course makes those decisions deliberate.

What you’ll take away

Six shifts

Beyond your own discipline

Move past the confines of your role and into the broader world of product thinking. Discover how to make a more profound impact on business and product strategy.

Master the “why”

Shift your conversations from “when” to “why.” Learn to ask the critical questions that drive strategic decision-making.

Diverse growth strategies

Explore product-led, customer-led, and sales-led growth strategies, and understand their profound effects on team culture.

Operationalize success

Gain the skills to operationalize company strategies, creating laser-sharp focus within your organization.

Metrics mastery

Distinguish between customer, business, and product metrics. Learn how they intertwine to fuel sustainable growth.

Inclusive design tactics

Adapt human-centered design discovery tactics to be more inclusive and empowering for your product team.

Who should join

This course is for you if…

  • You work on or alongside a product team and want to influence business and product strategy, whatever your title
  • You’re tired of “when” questions and eager to explore “why” questions
  • You want to expand your understanding beyond your own discipline’s values and practices

Course outcomes

What changes afterward

  • Empower your organization with diverse growth strategies
  • Forge stronger connections with the product side of your organization
  • Distill the “why” behind your strategy and leverage product thinking
  • Collaborate effectively with other lines of business outside of IT
  • Help leaders create clarity and focus through strategic operationalization

Logistics

How the session runs

This intensive four-hour virtual training, hosted via Zoom and Miro, is your ticket to deeper influence. Attendance is capped at 30 participants per session to keep the experience immersive.

Next cohort forming

Register your interest and you’ll get dates before they’re public

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Running this for an internal team instead? Book a call and we’ll tailor it.

Not sure which course fits your team?

Fifteen minutes on where your organization actually is and we’ll work out which one moves the needle — or whether it’s neither.

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