Interesting technology, weak problem
Find a real user need before selecting an AI pattern or model.
Course 08 · 7 days
AI Product Management
Turn an AI idea into a product decision: discover the user problem, choose the right AI pattern, define evaluations, and plan a responsible launch.
1 · Learner problem
AI product work fails when the model becomes the product strategy.
Product managers need to connect user value, data, model behavior, workflow, evaluation, experimentation, and responsible launch decisions.
Data and model choices are abstract
Translate data, RAG, GenAI, and agent options into product trade-offs.
Success is hard to define
Design evaluations and experiments for quality, usefulness, safety, and adoption.
A responsible launch plan
Bring discovery, product design, evals, experimentation, and launch into one capstone.
2 · Course outcome
Leave able to make an AI product decision with evidence behind it.
Frame the user problem
Define user, workflow, pain, value, and the decision the product should improve.
Choose the AI pattern
Compare ML, GenAI, RAG, agentic, and human-in-the-loop options.
Define evals and experiments
Set quality, safety, usefulness, and adoption measures before launch.
Build a product plan
Present roadmap, responsible use, launch risks, and next validation step.
For emerging product leaders working with AI.
- Associate AI product managers
- Product analysts and business analysts
- Founders and delivery leads shaping AI products
- Technical PMs translating AI capability into user value
Bring a product problem worth testing.
Technical depth is helpful but not required. The course values clear decisions, evidence, collaboration, and responsible product ownership.
4 · Curriculum
Eight modules from discovery to launch.
- AI Product DiscoveryFrame user needs, workflow friction, and product opportunity.
- Data → ML ProductConnect data, models, product behavior, and value.
- GenAI & LLM ProductsDesign useful model-powered product experiences.
- RAG & Enterprise AIEvaluate knowledge-grounded product patterns.
- Agentic AI ProductsDefine bounded autonomy, tools, approvals, and ownership.
- Evals, Experimentation & Responsible AIMeasure quality, risk, usefulness, and learning.
- AI Product Launch + CapstonePresent a product launch plan with evidence and next steps.
5 · Live project
Build an AI product launch plan.
Choose a knowledge assistant, data product, workflow copilot, or agentic product idea and take it through discovery, evaluation, and launch.
User and workflow brief
Define the user, problem, current process, value, and non-goals.
AI pattern and experiment
Compare product approaches and define evidence for quality and adoption.
Responsible launch plan
Present roadmap, risks, evals, ownership, and next validation milestone.
6 · Trainer credentials
View trainer profileAI product thinking taught through real workflow decisions.
MHGrad’s practice covers AI applications, agents, RAG, automation, software delivery, analytics, and responsible adoption. Verified trainer credentials and references are available during enrolment conversations.
Seven-day live intensive
- Daily product discovery and design work
- Evaluation and responsible AI checkpoints
- AI product launch capstone presentation
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next intensive, availability, payment steps, or private team delivery. Refund terms are published before payment.
Enquire for current fee
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FAQ and refund terms
Before you enrol.
Do I need a technical background?
No. Product experience and a real user problem matter more. Technical concepts are taught in a decision-oriented way.
Will we build a working product?
The capstone is a product launch plan and demonstrable concept. Production delivery depends on scope, data, and engineering follow-up.
Can teams take this course privately?
Yes. Ask about a private delivery adapted to your product portfolio and decision process.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Make your next AI product decision clearer.
Ask for dates, fee, prerequisites, or a team-training conversation.