AI Product Management

Description

This AI Product Management training course teaches product managers how to lead the development of AI-powered products from idea to launch. You will gain technical fluency in machine learning, learn frameworks for identifying genuine AI product opportunities, design for uncertainty and probabilistic outputs, build data strategies, manage launches, measure success, and navigate ethics and governance. By the end, you will be able to confidently ship AI products that users love.

Course Content

Module 1: The AI Product Manager Role

How AI PM differs from traditional product management
Core responsibilities: discovery, scoping, shipping, measuring
Working with data scientists, ML engineers, and designers

Module 2: AI and ML Fundamentals for PMs

Demystifying machine learning, deep learning, and LLMs
What AI can and cannot do today
Reading model cards and evaluation metrics
Speaking the language of your technical team

Module 3: Identifying AI Product Opportunities

Spotting problems where AI adds genuine value
Avoiding "AI for the sake of AI" anti-patterns
Competitive analysis and market positioning
Build vs. buy vs. partner decisions

Module 4: Scoping and Designing AI Features

Writing AI product requirements documents
Designing for uncertainty and probabilistic outputs
UX patterns for AI: confidence indicators, fallbacks
Defining success metrics that matter

Module 5: Data Strategy

Data as a product moat
Data collection, labeling, and quality assurance
Privacy, consent, and compliance considerations
Building feedback loops into your product

Module 6: Launching AI Products

Phased rollouts and beta testing strategies
Setting user expectations and managing edge cases
Pricing AI features and managing API costs
Go-to-market for AI products

Module 7: Measuring and Iterating

Online and offline evaluation methods
A/B testing AI features responsibly
User feedback loops and qualitative research
Continuous improvement and model retraining cadence

Module 8: Ethics, Risk, and Governance

Bias, fairness, and inclusive product design
Communicating AI limitations to users
Internal AI governance and approval processes
Crisis management when AI fails publicly

Duration: 6 – 8 weeks

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