1
Foundations of AI in Product Management
Gain an understanding of the fundamentals of AI, ML, and DL, and how they are used for product management.
Learning Outcomes
2
Learning AI Use-Cases Across Product Management
Identify opportunities to integrate AI into product features, workflows, and customer experiences.
3
Evaluating AI Solutions
Evaluate AI product feasibility considering data availability, technical complexity, and ethical constraints.
4
Practical AI Implementation
Apply AI-driven methods for product road mapping, prioritization, and lifecycle management.
5
Ethics, Law & Privacy
Recognize governance, privacy, and regulatory considerations in AI product development.
6
Insights from Real-World Cases
Analyze real-world AI product case studies to understand successes, failures, and best practices.
Program structure
Lesson Plan
5 modules • 51hours total length
Module 1: AI Foundations for Product Managers
Module 1: AI Foundations for Product Managers
What you'll learn
- Define AI, ML, and DL in the context of product management
- Trace the evolution of AI-powered products and market trends
- Explain core AI concepts, learning types, and model evaluation
- Differentiate roles of product managers vs. data scientists in AI projects
- Apply AI maturity models to assess organizational readiness
- Recognize and address common misconceptions about AI in products
Skills you'll gain
AI FundamentalsProduct AI ConceptsData PipelinesModel EvaluationGenerative AI BasicsAI ReadinessPM–Data Science Collaboration
Module 2: Identifying AI Opportunities
Module 2: Identifying AI Opportunities
What you'll learn
- Apply AI to personalize and predict user experiences
- Design AI use cases that drive business value and workflow automation
- Validate AI opportunities using discovery and scoring frameworks
- Build rapid prototypes to test and refine AI feature assumptions
- Identify inclusivity, legacy, and hidden AI opportunities within data
- Assess competitive intelligence and market positioning with AI
Skills you'll gain
User PersonalizationWorkflow AutomationOpportunity ScoringAI PrototypingLegacy Product AIMarket Intelligence
Module 3: Designing AI-Powered Products
Module 3: Designing AI-Powered Products
What you'll learn
- Define data strategies for sourcing, partnerships, and annotation
- Design product architectures for AI model integration
- Prototype AI features and conduct A/B testing for validation
- Manage model drift and feedback loops in production environments
- Apply explainability, trust, and security in AI-enabled products
- Design UI/UX and human-in-the-loop workflows for AI features
Skills you'll gain
Data StrategyAI Integration PatternsA/B TestingModel Drift ManagementExplainability & TrustUX for AIHuman-in-the-Loop Design
Module 4: AI Product Lifecycle Management
Module 4: AI Product Lifecycle Management
What you'll learn
- Prioritize AI and non-AI features in product roadmaps
- Design MVP-to-scale strategies for AI product rollouts
- Apply governance, compliance, and ethical AI principles
- Track AI feature KPIs, adoption metrics, and trust signals
- Lead cross-functional AI product teams and manage budgets
- Oversee vendor partnerships, procurement, and AI feature sunset plans
Skills you'll gain
Road mappingGovernance & ComplianceEthical AIAI Metrics & KPIsCross-Functional LeadershipBudgeting AI ProjectsVendor Management
Module 5: Case Studies & Future Trends
Module 5: Case Studies & Future Trends
What you'll learn
- Analyze global success stories and failure cases of AI products
- Explore emerging trends like multimodal AI, agents, and autonomous products
- Evaluate industry-specific AI innovations and societal impacts
- Design resilient product strategies for AI-driven disruption
- Define the evolving role of product managers in AI-first companies
- Build long-term visions with frontier technologies like quantum AI
Skills you'll gain
Case Study AnalysisFuture Trend MappingMultimodal AIAI AgentsIndustry InnovationSocietal ImpactStrategic Visioning
