A hands-on, case-study-driven program where you learn to identify, design, evaluate and ship AI-powered products the way real AI teams practice it, and walk away with a portfolio-ready AI product case study.
Artificial Intelligence, Machine Learning & Generative AI, explained at a PM level. Where AI systems succeed, struggle & fail (hallucinations, bias & uncertainty). The AI PM role: partnering with data scientists, ML & software engineers. Applied work: Ai Product Teardown.
Spotting where AI creates real leverage (automation, prediction, generation, personalization). Separating genuine customer problems from AI-shaped solutions looking for one. Evaluating opportunities on value, feasibility, data availability & risk. Applied work: Evaluate & Rank Ai Opportunities.
Structured vs unstructured data, and what makes training data usable. Data collection, labeling, cleaning, privacy & bias. Building a data strategy that creates a sustainable advantage. Applied work: Data Requirements Assessment.
Defining AI product vision: customer outcome, business outcome, AI's role in it. Build vs buy vs partner, model selection & competitive differentiation. AI business models: subscription, usage-based, API & enterprise pricing. Applied work: Build An Ai Product Strategy.
How LLMs work: tokens, context windows, foundation models & their limits. Prompting, embeddings, RAG, fine-tuning & tool/function calling. AI agents vs chatbots (planning, memory & human-in-the-loop workflows). Applied work: Deconstruct An Ai Product'S Architecture.
Designing for probabilistic outputs (confidence, error recovery & graceful failure). AI UX patterns: copilots, chat, recommendations & agentic workflows. Building trust through transparency, sourcing & human override. Applied work: Design An Ai-Powered Experience.
Writing AI-specific requirements: accuracy, latency, cost & model behaviour. AI user stories, acceptance criteria & failure scenarios. The AI development lifecycle: data, model, integration, testing & monitoring. Applied work: Write An Ai Prd.
Designing AI experiments with real baselines & success criteria. Model evaluation: accuracy, precision, recall & benchmarking. Evaluating generative AI: relevance, groundedness, safety & consistency. Applied work: Build An Evaluation Framework.
Responsible AI principles: fairness, transparency & accountability. Identifying AI risk: bias, hallucination, prompt injection & over-reliance. Governance, guardrails & incident management for AI products. Applied work: Conduct An Ai Risk Assessment.
What MLOps means for a PM: model lifecycle, deployment & serving. Monitoring for data drift, model drift, latency & reliability. Managing inference costs & the economics of scaling an AI product. Applied work: Build An Operational Plan.
Launch readiness, beta & pilot programs, and customer education. Positioning & communicating AI value to technical and non-technical buyers. AI monetization: usage-based, token-based, seat-based & outcome-based pricing. Applied work: Build A Gtm & Pricing Strategy.
Leading AI teams through ambiguity & incomplete information. Trade-offs: build vs buy, automation vs oversight, speed vs quality. Presenting a portfolio-ready, end-to-end AI Product Management case study. Applied work: Present The Complete Ai Product.
| Format | Live, weekly |
| Duration | 12 weeks |
| Time commitment | ~2 hrs/week + pre-reads |
| Self-paced option | Available |
| Certificate | On completion |
| Mentorship | Direct access throughout |
Choose your cohort and enroll now, or download the brochure first if you want the details in hand.