AI Product Management Course

Lesson 1 • The AI Product Manager Role

Defines responsibilities, required skills, and stakeholder relationships unique to AI PMs. Clarifies how this role bridges technical and business domains.

Lesson 2 • What Makes AI Products Different

Contrasts AI products with rule-based software on key dimensions. Sets the conceptual baseline for every subsequent chapter.

Lesson 3 • AI and Machine Learning Primer for PMs

Delivers just-enough ML literacy for product decisions without requiring coding. Enables PMs to evaluate feasibility and scope AI features confidently.

Lesson 4 • AI Product Landscape and Taxonomy

Maps the ecosystem of AI product types, platforms, and deployment patterns. Provides a reference framework used throughout the course.

Lesson 1 • Data as a Product Asset

Reframes data from a technical artifact to a strategic resource requiring ownership. Introduces data product thinking as a PM discipline.

Lesson 2 • Measuring and Improving Data Quality

Provides tools to audit datasets and implement continuous quality improvement. Links data quality metrics directly to downstream model outcomes.

Lesson 3 • Data Governance and Privacy

Establishes policies for data access, retention, consent, and compliance. Prepares PMs to build trustworthy products that respect user rights.

Lesson 4 • Data Collection and Acquisition

Covers sourcing strategies including first-party, third-party, and synthetic data. Connects data sourcing decisions to model performance and cost trade-offs.

Lesson 1 • Identifying AI Opportunities

Teaches systematic scanning of user pain points and business processes for AI fit. Filters opportunities by impact, feasibility, and data availability.

Lesson 2 • Ideation and Concept Validation

Generates and rapidly filters AI product concepts using structured workshops. Validates concepts with lightweight experiments before full investment.

Lesson 3 • Feasibility and Risk Assessment

Evaluates technical, data, and organisational readiness before committing resources. Produces a go/no-go framework for AI feature investment.

Lesson 4 • Problem Framing for AI Solutions

Converts ambiguous business problems into precise ML task definitions. Prevents costly misalignment between product goals and model objectives.

Lesson 1 • AI-Specific Requirements Frameworks

Introduces requirement types unique to AI: model performance, data, and behavioural specs. Distinguishes AI requirements from traditional functional requirements.

Lesson 2 • Ethical Requirements and Constraints

Embeds fairness, transparency, and harm-prevention criteria into product specifications. Creates accountability structures before development begins.

Lesson 3 • Writing Effective AI User Stories

Adapts user story format to capture AI-specific acceptance criteria and edge cases. Ensures engineering and data science teams share a common definition of done.

Lesson 4 • Defining Success Metrics

Establishes a dual-metric system linking ML metrics to business KPIs. Prevents optimising models that fail to move business outcomes.

Lesson 1 • Principles of Human-AI Interaction

Establishes foundational design principles for products where AI makes or influences decisions. Frames UX as a trust-building mechanism.

Lesson 2 • Designing for Edge Cases and Errors

Creates graceful degradation patterns for low-confidence and out-of-scope AI outputs. Ensures error states maintain user trust and provide recovery paths.

Lesson 3 • Onboarding Users to AI Features

Designs onboarding flows that set accurate expectations and build initial trust. Reduces abandonment caused by misaligned mental models.

Lesson 4 • Personalisation and Adaptive Interfaces

Applies AI-driven personalisation to improve relevance while respecting user autonomy. Balances algorithmic optimisation with user preference control.

Lesson 1 • Collaborating with Data Science Teams

Establishes productive working norms between PMs and data scientists. Reduces friction by clarifying decision rights and communication cadences.

Lesson 2 • Handling Model Failures and Regressions

Prepares PMs to detect, triage, and communicate model failures in production. Establishes incident response protocols for AI-specific issues.

Lesson 3 • Evaluating Model Performance

Teaches PMs to interpret model evaluation reports and challenge results constructively. Links model metrics to product readiness decisions.

Lesson 4 • Managing Experimentation and Trade-offs

Guides PMs through A/B testing design and model trade-off decisions. Builds confidence in making data-driven go/no-go calls on model versions.

Lesson 1 • AI Business Model Patterns

Surveys recurring revenue and value-capture models specific to AI-powered products. Connects model architecture choices to monetisation strategy.

Lesson 2 • AI as a Strategic Differentiator

Analyses how AI creates defensible competitive advantages through data network effects and proprietary models. Frames strategy around compounding AI assets.

Lesson 3 • Measuring AI Product-Market Fit

Adapts product-market fit signals for AI products with evolving capabilities. Defines leading indicators that predict sustainable AI product growth.

Lesson 4 • Build vs. Buy vs. Partner Decisions

Provides a decision framework for sourcing AI capabilities internally or externally. Evaluates trade-offs across cost, control, speed, and strategic fit.

Lesson 1 • AI Product Launch Strategy

Adapts go-to-market planning for AI's probabilistic nature and iterative improvement cycle. Defines launch readiness criteria specific to AI products.

Lesson 2 • Monitoring AI in Production

Establishes observability systems to detect model drift, data issues, and performance degradation. Connects monitoring outputs to product decision triggers.

Lesson 3 • Post-Launch Iteration and Roadmap Evolution

Applies production insights to refine the AI product roadmap and prioritise next improvements. Closes the loop between launch learnings and future planning.

Lesson 4 • Feedback Loops and Continuous Improvement

Designs mechanisms to capture user signals and route them back into model improvement. Creates a flywheel between product usage and model quality.