PRODUCT MANAGEMENT FOR AI & ML

Kick off your AI PM journey with a clear roadmap for the course, meet your instructor and peers, and understand how each session builds your expertise. Get a sneak peek at industry relevance and how your capstone project will tie everything together.

• Meet your instructor and peers
• Course structure & learning journey
• Sessions, assignments, case studies, and capstone overview
• Industry context: why AI PM matters now
• Career guidance and support
• Q&A and student expectations

Explore the unique responsibilities of AI PMs and how they bridge business, engineering, and data science. Gain insights into the AI product lifecycle, market trends, and how to spot value in AI opportunities.

• AI PM vs traditional PM
• AI product lifecycle and market fit
• Business, data, & engineering balance
• Industry landscape: Trends, platforms, key players
• AI fundamentals
• AI hype cycle & value frameworks

Learn to identify and articulate high-impact AI problems. Understand when AI creates value, how to prioritize opportunities, and assess risks with real-world examples.

• Business problem identification and framing
• When AI adds value vs when it doesn’t
• Identifying high ROI AI opportunities
• Feasibility and impact prioritization
• Risk-aware problem statements

Assignment 1: AI Product Landscape & Opportunity

Dive into the central role of data in AI products. Learn the essentials of data quality, governance, and engineering, and explore real-world AI product types and data strategies.

• What data means in AI
• Common AI product types
• Dataset design & annotation challenges
• Data governance basics & data engineering
• Data licensing & ethical considerations
• Group discussion: Identifying data needs for your capstone AI product idea
• Case Study: Recommender and Ranking Systems 

Build a strong conceptual understanding of AI and ML without coding. Learn how models work, interpret outputs, and consider ethical implications to make informed PM decisions.

• AI vs Machine Learning vs Deep Learning
• Supervised, unsupervised, & reinforcement learning
• Model training, validation, & generalization
• Model outputs & limitations
• Ethical considerations 

Assignment 2: Problem Framing & Product Concept

Understand generative AI capabilities, design user experiences for probabilistic outputs, and learn prompt engineering fundamentals to iterate on solutions for real business problems.

• What is generative AI?
• Prompt engineering basics  & AI-driven UX design
• Iterative AI development & prototyping principles
• Tokenization & fine-tuning
• Common prompt engineering patterns
• Workshop: Iterating on prompts to solve real business problems
• Case Study: Automated Customer Service and Virtual Assistants 

Identify high-value generative AI opportunities and explore emerging workflows such as RAG, multi-step agents, and real-world use cases to align technology with strategy.

• Generative AI capabilities
• Retrieval-Augmented Generation (RAG) & chaining
• Agents & multi-step AI workflows
• Demo: Designing simple agentic AI flows
• Business value & real-world product use cases
• Prompt design to align with goals
• Case Study: Self-Driving Cars and Autonomous Systems

Define and measure AI product success. Learn which KPIs matter, how to connect metrics to ROI, and design experiments that reduce risk and validate impact.

• Core AI KPIs
• Experimentation lifecycle
• KPI alignment with business objectives
• Experiment design best practices
• Risk mitigation strategies
• Business and financial fundamentals 

Delve into ethical AI practices, regulatory compliance, and governance frameworks. Build the mindset and tools to ensure fairness, privacy, and trust in AI products.

• Bias mitigation, privacy, transparency pillars
• Governance & legal considerations
• Responsible AI checklist building
• Privacy and fairness auditing techniques
• Governance maturity models
• Building user trust, compliance & risk mitigation 

Assignment 3: Responsible AI, Features & Prompt Design

Learn frameworks for managing AI product development and launching to market. Focus on cross-functional alignment, GTM strategy, and translating insights into actionable plans.

• Agile & iterative development
• Business requirements gathering & prioritization
• Cross-functional communication strategies
• GTM planning
• Workshop: Drafting GTM plan for capstone

Collaborate effectively with AI teams and maintain product health post-launch. Learn deployment strategies, performance monitoring, and continuous improvement practices.

• Engineering, data science, & ML ops teams
• Deployment & scaling considerations
• Post-launch performance & data drift
• Incident management & continuous improvement
• Workshop: Post-launch monitoring strategies

Assignment 4: Product Metrics & Experimentation Plan + Capstone Project: End-to-End AI Product Pitch & Strategy

Explore emerging AI architectures, personalization, and agentic AI. Understand evolving AI PM roles, scaling strategies, and how to position yourself for leadership in AI product management.

• Agentic AI & multi-agent systems
• AI personalization, collaborative AI
• Anticipating & leading AI innovation
• Evolving roles of AI PMs
• AI Product portfolios & scaling AI initiatives
• Building your AI PM career path
• Finalize Assignments 3 & 4 deliverables

Present your end-to-end AI product strategy to peers and instructors, receive actionable feedback, and consolidate learnings to prepare for real-world AI PM roles.

• Student presentations
• Peer’s and instructor's feedback 

Equip yourself for the next step in your AI PM career. Learn how to leverage AI tools for portfolio building, continuous learning, and enhancing productivity. Receive targeted advice for resumes and portfolios.

• AI-powered tools to build your PM portfolio
• AI for continuous learning and productivity
• Resume and portfolio advice
• Course reflection & continuous learning paths