AI for Product Managers

In this AI for Product Managers course, you'll learn:

Ai Foundations

• The AI Landscape:
• What AI is good at:
  • Pattern recognition, ranking, synthesis
• What AI struggles with:
  • Deterministic logic, factual certainty, edge cases
• The “PM-Level” AI Stack
  • Understanding the relationship between Compute, Models (LLMs/SLMs), and Application layers
  • Where PM decisions actually matter

Opportunity Identification & Problem Framing

• Identifying “high-friction” workflows
• Automation vs augmentation decisions
• Translating business goals to AI problem statements
• Using the Moat Strategy to evaluate where value comes from

Data Strategy as a Product Requirement

• Data types: structured vs unstructured
• Training data, labels, and feedback loops
• Data quality vs data availability
• Data inventory
  • What exists vs what’s needed
• Data gaps and bias risks
• Privacy & Compliance
  • GDPR, SOC2 basics
  • Data residency considerations
  • User trust implications

Business Case & Economic Modeling

• Cost Structures
  • Token/API costs (for LLMs)
  • Infrastructure & hosting
• Hidden costs
  • Human-in-the-loop (HITL)
  • QA, moderation, retraining
• ROI Frameworks
  • Efficiency gains (cost reduction, automation)
  • Revenue expansion (new features, personalization)
• AI vs Non-AI Baseline
  • When is AI actually worth it?

Designing AI Solutions without Coding

• Model Strategy
  • Buy (APIs like OpenAI)
  • Tune (fine-tuning existing models)
  • Build (custom models)
• Prompting as Prototyping
• Using no-code tools
  • Validating ideas quickly
  • Exploring edge cases
• System Design Thinking
  • Inputs - determining what is relevant and available
  • Model - choosing the right approach
    • Predictive
    • Generative
    • Hybrid
  • Outputs - what good output looks like
  • Feedback Loop

Managing Uncertainty & AI UX

• Probabilistic vs deterministic systems
• Confidence scores vs “correct answers”
• Designing for failure and ambiguity
• Communicating uncertainty
• Handling hallucinations gracefully
• Capturing user corrections

Shipping, Experimentation & Scaling

• “Wizard of Oz” approach
• Rapid validation before heavy investment
• Experimentation
  • A/B testing AI systems
  • Defining success metrics:
  • Monitoring in Production
  • Model drift
  • Performance degradation
  • Feedback loops at scale

Risk, Ethics & Trust

• AI Risks
• Mitigation Strategies
• Trust as a Product Feature