Hands-on AI product management for the teams that own the roadmap: how LLMs, retrieval and agents behave, what they cost, and how to turn them into features customers adopt. Instructor-led, with no code required.
The AI product management program assumes product experience, not code. Nobody is asked to write any — the point is to work with the engineers who do, and to know what to ask for.
People who already run a product and now have an AI feature on the roadmap, with no idea what to ask the engineering team.
Product groups going from a prototype that impressed someone to a feature that has to hold up with real users on it.
The people around the product who have to specify, measure and support AI features without owning the code.
Product teams are asked to ship AI features without knowing what the technology can hold. Session by session, AI product management gives them the judgement to decide what ships and what waits.
The fundamentals of product management and core AI concepts, and how their convergence creates the hybrid role.
Learning objectiveExplain what changes when AI enters the productThe decisions that move to the product manager, and the ones that stay with engineering.
Certify Your TeamWhat the AI product manager does, the skills it takes, and the processes that bridge engineering and business goals.
Learning objectiveSet how the team ships AI featuresClear handoffs and sign-offs, so an AI feature never stalls between two teams.
Certify Your TeamLarge language models, generative AI, machine learning and copilots — how they work and what they change.
Learning objectiveAsk engineering the right questionsEnough grounding to challenge an estimate or a vendor claim instead of accepting it.
Certify Your TeamUser interaction, ethics and keeping systems aligned to human needs so what ships is usable and defensible.
Learning objectiveDesign AI features people trustFeatures users adopt because they can see what the system is doing and why.
Certify Your TeamTechnical literacy, strategic thinking and stakeholder collaboration, and how to hold all three at once.
Learning objectiveSpeak for the product in every roomCredible in the engineering review and in the business case, in the same week.
Certify Your TeamModel performance, data quality and the metrics that tie AI outcomes to business goals.
Learning objectiveRead the numbers behind an AI featureTell a model problem from a data problem before either one reaches the roadmap.
Certify Your TeamFrom ideation and data collection through deployment and iteration, with the failure modes unique to AI.
Learning objectiveNavigate the whole AI product lifecycleKnow what each stage needs before it starts, so the launch date holds.
Certify Your TeamDefining metrics that align to strategy, measuring success and optimising what the product actually does.
Learning objectiveProve an AI feature earns its placeNumbers that justify the next investment, or the decision to stop early.
Certify Your TeamWhat agents can do, how they interact with users and environments, and their role in autonomous agentic systems.
Learning objectiveSpecify what an agent may doFirm limits on what it acts on alone, written down before engineering builds it.
Certify Your TeamHow copilots change user experience, the design considerations, and how to integrate one without breaking the product.
Learning objectiveDecide where a copilot fitsAssistance where it helps users, and nothing in the places it would get in the way.
Certify Your TeamA full product requirements document for an AI feature — features, technical specification and business goals.
Learning objectiveWrite an AI PRD engineering can build fromYour own feature specified well enough that the estimate stops changing every sprint.
Certify Your TeamHow generative models create text, images, audio and code, and where that lands across industries.
Learning objectiveMatch the model to the use caseKnow what kind of output the product needs before anyone picks a vendor.
Certify Your TeamThe difference in how each processes data and decides, and when to reach for which.
Learning objectiveChoose the right model familyA model choice you can explain to engineering and to the board.
Certify Your TeamGANs, VAEs and large language models, how they generate, and where each is applied.
Learning objectiveJudge a model by where it failsWeigh the options on quality, cost and risk for your own product.
Certify Your TeamIterative validation, user feedback and data-driven calls that keep an MVP honest.
Learning objectiveKnow when to scale or stop an MVPEvidence strong enough to decide before the budget decides for you.
Certify Your TeamCrafting prompts that guide a model to the output the product actually needs.
Learning objectiveEngineer prompts that holdPrompts tested against real inputs rather than assumed to work.
Certify Your TeamWhat pre-trained models can do with minimal customisation, and how to use them in a product.
Learning objectiveShip on a pre-trained model firstReach users in weeks, and save custom training for when the product has earned it.
Certify Your TeamDelivered live by practitioners who have built and shipped AI-powered products, not pre-recorded video.
Real deliverables for AI product management — metrics frameworks, AI PRDs, prompt engineering exercises and plans for testing an MVP.
No engineering background needed. Enough understanding of LLMs, RAG and agents to hold the AI product management conversation with the engineers who build.
A part-time AI product management schedule for people who are still running their product while they take it.
Human factors, ethics and responsible design are woven through the curriculum rather than added at the end.
A Mastering Gen AI in Product Development Certificate per participant, verified through Accredible and issued at the end of the AI product management program.
How LLMs, retrieval and agents actually behave, what they cost, where they fail, and what that means for a roadmap, from the first concept to an AI PRD and a tested MVP.
12+ hours across three modules and seventeen sessions, instructor-led, on site or live online.
Product managers, product owners and the people around them who now have to ship AI features and answer for them.
No. This AI product management program requires no code, though the material is precise enough to hold a conversation with engineering.
An AI feature worked through end to end on their own product, and the certificate for those who complete the assessments.
The AI product management program is built for the group and priced on team size, level and timeline.
The full product path — discovery, prioritisation, roadmapping, and scaling a product organisation.
Learn More →For the engineers building the features — LLM integration, RAG pipelines, agents and a capstone they deploy and keep.
Learn More →21 hours redesigning how a team works — specs, documents, meetings, and a workflow proposal with its impact estimate.
Learn More →Your roadmap already has an AI feature on it. This is where the people who own it learn AI product management, and what it takes to ship.