AI for Product Managers
Understand AI deeply enough to lead products — without writing a single line of code. Strategy, evaluation, and practical frameworks for PM leaders.
Who Is This For?
Product Managers
Build AI product specs and evaluate AI opportunities with confidence.
Business Leaders
Understand AI capabilities and limitations to drive strategic decisions.
Cross-Functional Teams
Collaborate effectively with ML engineers, data scientists, and AI teams.
Course Modules
AI Fundamentals for PMs
What is AI, ML, Deep Learning, and GenAI? Core concepts explained without jargon. Understand supervised vs. unsupervised learning, neural networks, and foundation models at a conceptual level.
- AI vs ML vs DL
- Foundation Models
- Key Terminology
- How Models Learn
Evaluating AI Opportunities
Frameworks for identifying where AI adds value in your product. Learn to distinguish hype from real capabilities, assess feasibility, and estimate ROI.
- Opportunity Assessment
- Feasibility Matrix
- Build vs Buy vs API
- ROI Frameworks
AI Product Strategy & Roadmapping
How to write AI product specs, define success metrics, plan data requirements, and build a phased AI product roadmap.
- AI PRDs
- Data Strategy
- Success Metrics
- Phased Rollout
Working with AI/ML Teams
Effective collaboration between PMs and ML engineers. Understand model development lifecycle, experimentation, and how to communicate requirements clearly.
- ML Lifecycle
- Experimentation
- Communication
- Trade-offs
AI Ethics, Risk & Governance
Responsible AI practices for product leaders. Bias detection, fairness, transparency, regulatory compliance, and building trust with users.
- Bias & Fairness
- Transparency
- Compliance
- Risk Management
GenAI & LLMs for Product
How to leverage LLMs, RAG, and generative AI in products. Prompt engineering basics, API integration patterns, and designing AI-native user experiences.
- LLM Integration
- Prompt Design
- RAG Patterns
- AI-Native UX