Andrew Crossley
AI PRODUCT MANAGEMENT + MVP COURSE · FOUNDING COHORT

Learn AI Product Management by building an AI MVP

A practical five-day course for aspiring AI Product Managers, existing PMs and founders. Learn how to identify a problem worth solving, define AI success metrics, scope the product, build an MVP, evaluate the AI and prepare it for real customers.

SHORT ANSWER

An AI Product Manager is responsible for turning customer problems into useful AI products: deciding where AI belongs, defining product strategy, setting evaluation and success metrics, prioritising scope, managing model limitations and cost, and connecting the product to measurable customer and business outcomes. This course teaches those responsibilities through a real product build.

Join the Founding CohortSee the 5-day curriculum
From £497Limited to 20 learnersNo coding required

LEARN BY BUILDING

By the end of the course you'll have

A validated startup idea
A complete product strategy
User personas & customer research
Wireframes and product flows
A working AI-powered MVP
An AI evaluation and success-metrics plan
A professional landing page
Go-to-market and pricing strategy
AI workflows to continue building after the course

WHO IS THIS FOR

People who want practical AI product evidence

The course is designed for people who want to understand AI Product Management by making the decisions an AI PM actually makes, not by memorising terminology.

Aspiring AI Product ManagersProduct Managers moving into AINon-technical foundersStartup foundersEntrepreneursDesigners and marketersDevelopers moving into productCareer changers

No coding experience required.

CORE AI PRODUCT MANAGER RESPONSIBILITIES

What you'll learn to own as an AI Product Manager

The role is broader than prompting. AI Product Managers still own customer and business outcomes, but they also need to understand evaluation, model behaviour, failure handling and the economics of running AI in production.

Find customer problems where AI creates real value
Define product strategy, scope and the smallest useful MVP
Set AI success metrics and create evaluation criteria
Choose models based on quality, latency and cost
Design failure states, confidence thresholds and human fallback
Prioritise features using evidence rather than novelty
Take an AI prototype through production readiness and launch
Connect product usage to retention, revenue and unit economics

Want the wider learning path first? Explore the AI Product Management hub or read the AI Product Success Metrics guide.

BUILT AROUND THE CROSSLEY METHOD

From customer problem to first revenue

The five live days compress the practical parts of the Crossley Method: Discover, Validate, Prototype, MVP, Launch, First Revenue and then Scale. The point is not speed for its own sake. It is making the next product decision with evidence.

Discover

Problem framing

Sharpen the customer, problem and job before choosing technology.

Validate

Prove demand

Use customer evidence to kill, sharpen or continue the idea before building.

Prototype

Make it testable

Map the user journey, choose the AI approach and define what a good output looks like.

MVP

Build one valuable journey

Ship the smallest end-to-end product that can produce a meaningful user outcome.

Launch

Prepare for real users

Add evaluation, analytics, onboarding, pricing and a launch surface.

Revenue

Learn from the market

Launch, collect evidence and use customer behaviour to decide the next investment.

5 days

Idea → clickable MVP

Founding-cohort pilot: brief on Monday, working AI-powered prototype live by Friday.

£0 → paying

First revenue in <14 days

Course frameworks used with a solo founder to land pilot users the fortnight after launch.

1 person

No dev team needed

Non-technical founders shipping features that previously required a small engineering squad.

Illustrative outcomes from founding-cohort pilots, not third-party testimonials.

WHAT YOU'LL LEARN

The 5-day AI Product Management curriculum

1Day 1

Discover & Validate the Problem

  • Find problems worth solving with AI
  • Validate demand before building
  • Customer discovery
  • Competitor research
  • Define the customer outcome and value proposition

Outcome: Evidence that the problem deserves a product.

2Day 2

Define the AI Product

  • Product strategy and feature prioritisation
  • User journeys and wireframes
  • AI product requirements
  • Success metrics and evaluation criteria
  • Failure states and human fallback

Outcome: A focused product blueprint and evaluation plan.

3Day 3

Prototype & Build the MVP

  • ChatGPT, Claude, Cursor, Lovable and Bolt
  • AI coding assistants and agents
  • Model choice, latency and cost trade-offs
  • API integration patterns
  • Ship one complete user journey

Outcome: A working AI-powered MVP.

4Day 4

Evaluate & Get Launch Ready

  • Run an eval set against the live product
  • Analytics and user onboarding
  • Reliability, access and production-readiness checks
  • Landing page and waitlist or checkout
  • Pricing and commercial launch decisions

Outcome: A product that can survive a real user, not just a demo.

5Day 5

Launch, Learn & Find First Revenue

  • Launch to a focused audience
  • Measure customer, AI quality and commercial metrics
  • Collect customer evidence
  • Decide what to improve, remove or stop
  • Create the next product roadmap

Outcome: A launch plan built around evidence and first revenue.

WHAT MAKES THIS DIFFERENT

Learn the job by doing the job

This isn't another prompt course. You learn AI Product Management through the decisions that make or break an AI product: customer evidence, scope, evaluation, model trade-offs, reliability, pricing and what happens after launch.

  • Learn AI product management by making real product decisions
  • Validate before spending money
  • Define AI quality before you ship
  • Understand model cost, latency and failure modes
  • Build one useful MVP instead of a feature-heavy demo
  • Connect product work to customers and first revenue

MEET YOUR INSTRUCTOR

Andrew Crossley

Andrew Crossley is a UK AI Product Consultant and Product Partner specialising in taking AI products from customer problem and validation through MVP, production readiness, launch and first revenue. The course is built from the same practical decisions used across founder products and client work.

Product work and ventures include:

  • Wocal
  • Co-Ride
  • Journly
  • CallFlow AI
About Andrew & experience

EVERYTHING INCLUDED

Everything you need to learn and ship

  • 5 Days of Live Training
  • Lifetime Course Access
  • AI Product Management Templates
  • AI Prompts
  • Product Strategy Frameworks
  • MVP Planning Toolkit
  • AI Evaluation & Metrics Scorecard
  • Landing Page Templates
  • Community Access
  • Course Updates

BONUS RESOURCES

Bonuses

  • AI Startup Toolkit
  • 500+ Startup Prompts
  • MVP Validation Checklist
  • Product Roadmap Template
  • Go-To-Market Framework
  • AI Tool Stack Guide
  • Lifetime Updates

FOUNDING COHORT

£497

Limited-time launch price · Future cohorts will increase to £997+

  • 5 days of live cohort training
  • Lifetime access + updates
  • Templates, prompts, metrics & product toolkits
  • Founder and product community access

No spam. I'll only email you about the cohort.

Your details are used only to reply to you and are never sold or shared. See the privacy policy.

Limited to 20 learners

Prefer LinkedIn? Reach out directly via LinkedIn

FREQUENTLY ASKED QUESTIONS

AI Product Management course questions

Is this an AI Product Management course?
Yes. The course teaches the core responsibilities of AI Product Management through a real build: customer discovery, product strategy, MVP scope, AI evaluation, success metrics, model trade-offs, failure handling, production readiness, launch and commercial validation.
What are the key responsibilities of an AI Product Manager?
An AI Product Manager identifies where AI creates customer value, defines product strategy and scope, sets AI quality and success metrics, prioritises features, manages model limitations and failure states, balances quality against latency and cost, and connects product outcomes to customer and commercial results.
Do I need to know how to code?
No. You'll learn how to use modern AI development tools such as Claude, Cursor, Lovable and Bolt to build products faster without needing to be a traditional software engineer.
Can I build SaaS products?
Yes. The frameworks and AI tool stack work for SaaS, marketplaces, internal tools and consumer AI apps.
Will I have a working MVP?
The goal is to leave with a functional, testable MVP or prototype plus an evaluation and launch plan you can act on immediately. The exact scope depends on the complexity of your idea.
Is this suitable for experienced Product Managers?
Yes. Existing Product Managers can use the course to build practical AI product skills around evaluation, model economics, AI UX, production readiness and AI-assisted prototyping.
Is this suitable if I want to become an AI Product Manager with no prior experience?
Yes. It is designed to give career changers and aspiring AI Product Managers practical evidence of product thinking by working through discovery, prioritisation, metrics, evaluation and a real MVP instead of learning theory alone.

Compare this approach with other learning options in the AI Product Management course comparison, or explore the free AI Product Management learning hub.

Learn it. Build it. Put it in front of customers.

The course is designed to leave you with more than AI Product Management theory. You make the product decisions, build the evidence and create something that can be tested in the real world.

Reserve my spot
© 2026 Andrew Crossley · Back to portfolio

EXPLORE NEXT

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Every stage from first idea to first revenue, mapped end to end.

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AI App Launch Readiness Score

Free 20-question check for customer evidence, access and data, reliability, AI quality and commercial launch readiness.

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Co-Ride case study

How a shared-travel marketplace was scoped down to one journey worth building.

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The Crossley Method

The seven-stage framework that takes an idea to first revenue.

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