Why you need this
Building AI products is different from traditional software. The uncertainty is higher, the technical constraints are less familiar, and user expectations are shaped by sci-fi, not reality.
This toolkit gives product managers the frameworks and templates needed to navigate AI product development confidently—even without a machine learning background.
Perfect for:
- Product managers adding AI features to existing products
- PMs at AI-first startups defining new products
- Technical product managers bridging engineering and business
- Product leaders building AI strategy
- Anyone who needs to ship AI features successfully
What's inside
Discovery & Validation
AI Opportunity Assessment
- Scoring framework for AI feature ideas
- Technical feasibility checklist
- Data requirements evaluation
- Build vs. buy decision framework
User Research for AI
- Interview guide: Understanding AI expectations
- Survey templates for AI feature validation
- Prototype testing approaches
- Feedback interpretation guidelines
Planning & Requirements
AI Feature PRD Template
Structured template including:
- Problem statement
- Success metrics (accuracy, latency, adoption)
- User stories with AI-specific acceptance criteria
- Edge cases and failure modes
- Data requirements
- Model behavior specifications
Roadmap Planning
- Phased approach to AI features
- Risk mitigation strategies
- Dependency mapping for AI projects
- Resource estimation guidelines
Working with Engineering
Technical Translation Guide
- ML concepts every PM should know
- Questions to ask data scientists
- How to interpret model performance metrics
- Red flags in AI project planning
Collaboration Templates
- AI project kick-off meeting agenda
- Model review meeting structure
- Weekly sync templates
- Escalation frameworks
Launch & Iteration
AI Feature Launch Checklist
- Pre-launch verification points
- Monitoring setup requirements
- Fallback and rollback procedures
- User communication templates
Post-Launch Operations
- Performance monitoring dashboard guide
- Feedback collection systems
- Iteration prioritization framework
- Model drift detection basics
How to use it
For new AI features:
- Start with Opportunity Assessment
- Validate with User Research templates
- Document with AI Feature PRD
- Collaborate using Technical Translation Guide
- Ship with Launch Checklist
For existing AI products:
- Use Post-Launch Operations to improve monitoring
- Apply Iteration framework for enhancements
- Leverage communication templates for stakeholders
Example scenario:
You're adding AI-powered recommendations to your e-commerce app:
- Score the opportunity (is this the right AI feature?)
- Research user expectations (what do they think AI will do?)
- Write AI-specific PRD (define success beyond "good recommendations")
- Work with engineers (understand model limitations)
- Launch with monitoring (catch problems before users do)
Want to go deeper?
- AI Strategy Basics — Build organizational AI capability
- When Not to Use AI — Avoid AI for the wrong use cases
- AI Use Case Evaluator — Score your AI opportunities
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You may share and adapt for any purpose with attribution.