Transitioning to Become an AI Product Manager
AI Product Management is not traditional Product Management with a few AI features added.
AI products behave differently. Their performance depends on data, models, user behaviour, feedback loops, infrastructure costs, security controls and continuously changing regulations. This practical workshop helps Product Managers build the foundational knowledge required to identify, evaluate, launch and monetise responsible AI products.
Built for those deciding where AI creates real value.
- Product Managers transitioning into AI Product Management
- Senior Product Managers and Product Leaders
- Founders developing AI-enabled products
- Business leaders responsible for AI initiatives
- Product, design and technology professionals working with AI teams
No previous AI or machine-learning experience is required.
Ten capabilities you'll leave with.
Four hours, structured to build real capability.
AI Fundamentals for Product Managers
A practical introduction to the concepts an AI PM must understand — models, training data, inference, predictive vs generative AI, foundation models, hallucinations, and build-buy-partner decisions.
Outcome · A working vocabulary for AI teamsFinding the Right AI Opportunities
Solving meaningful customer problems instead of adding AI for novelty. Mapping AI capability to need; assessing desirability, feasibility and viability; prioritising by value, risk and effort.
Outcome · A shortlist of viable opportunitiesDeciding What to Build — and What Not to Build
A structured way to recognise unsuitable, unsafe or commercially weak ideas. When conventional software wins; the cost of model errors; human-in-the-loop; clear no-go criteria.
Framework · The AI Product Go/No-Go ChecklistData Security, Privacy and Responsible AI
Data ownership and consent, PII, data minimisation, leakage through prompts, third-party model risk, bias and explainability, red-teaming, and responsible controls across the lifecycle.
Outcome · An initial AI Risk & Safeguards MapAI Regulations Across Global Markets
An executive-level view across the US, EU, India and key APAC markets: risk-based regulation, transparency, automated decisions, prohibited use cases, and when to involve legal, privacy and security.
Framework · Regulatory Readiness ChecklistAI Product Economics and Revenue Loops
The most important commercial module. Managing value, usage, learning and monetisation as one system — inference cost per outcome, pricing models, protecting margins, and building revenue loops that strengthen with use.
Activity · Design a Revenue Loop CanvasCreating the Transition Roadmap
Turning the workshop into an actionable plan: concepts to learn, experiments to run, relationships to build, and a portfolio project that demonstrates AI PM capability.
Outcome · A personal 90-day transition planCustomer problem → AI-powered outcome → demonstrated value → increased usage → learning & improvement → retention or expansion → revenue → reinvestment in product quality.
Seven working tools — not slides.
- AI Product Opportunity Canvas
- AI Product Go/No-Go Checklist
- AI Risk and Safeguards Map
- Regulatory Readiness Checklist
- AI Revenue Loop Canvas
- AI Product Metrics Framework
- Personal 90-day Transition Roadmap
How it runs.
The workshop combines structured instruction, practical frameworks, group discussion and applied exercises. Participants are encouraged to bring a real product or AI opportunity to evaluate during the programme. Runs as an open cohort or as an in-house programme for a single organisation.
Stop adding AI features. Start building responsible, valuable and commercially sustainable AI products.
Ready to make the transition?
Places are limited and cohorts are kept small by design. Message directly to check dates, ask about an in-house programme for your team, and see if it's the right fit.
Available as an open cohort (max 10) · or as a tailored corporate / in-house programme
Enquire on WhatsApp