Building AI-Powered Products Marily Nika

Spis treści książki

• Preface
  • Why AI Product Management?
  • Who Should Read This Book?
  • What This Book Covers
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • Closing Thoughts
• 1. The Role of AI Product Managers
  • The Stages of AI Evolution
    • Traditional AI (1950sPresent)
    • Generative AI (Late 2010sPresent)
    • Artificial General Intelligence (2030s?)
    • Artificial Superintelligence (~2040s?)
  • How Products Leverage AI
  • Unique Features of AI
    • Probabilistic Nature
    • Dependency on Data
    • Model Drift
    • The Need for Model Interpretability and Explainability
    • Automated Decision Making
    • Scalability
    • How These Unique Features Can Impact User Experience
  • Superpowers of AI and GenAI
    • Superpower 1: Learning from Massive Data and Content
    • Superpower 2: Personalization at Scale
    • Superpower 3: Automating and Optimizing Workflows
    • Superpower 4: Generating New Content and Experiences
    • Superpower 5: Prediction and Forecasting
    • Superpower 6: Real-Time Adaptation
    • Superpower 7: Unlocking New Types of User Experiences with New Form Factors
  • The AI PMs Role
  • The AI PMs Skill Set
  • Organizational Structures
  • Why Become an AI PM?
    • Whats Great About Being an AI PM
    • Subtypes of AI Product Management Roles
  • Book Road Map
  • Conclusion
• 2. The AI Product Development Lifecycle
  • Types of AI Products
    • 0-to-1 AI Products
    • 1-to-n AI Products
  • The AI Product Development Lifecycle
    • Ideation
      • Step 1: Adopt an innovation-first mindset
      • Step 2: Understand AI-powered features and their capabilities
      • Step 3: Brainstorm with your team
      • Step 4: Know your customers by using the RICE framework
    • Opportunity
      • Productmarket fit
      • Business viability
        • ROI analysis.
        • Monetizing AI features.
        • Risk evaluation.
        • Regulatory compliance.
      • Technical feasibility
      • User desirability
      • Achieving AI productmarket fit
    • Concept and Prototype
      • Require putting together a hardcoded experience
      • Demonstrate (ideally low-effort) integration compatibility
      • Showcase domain-specific expertise
      • Add value from day one
    • Testing and Analysis
    • Rollout
  • Conclusion
• 3. Essential AI PM Knowledge
  • Core Product Management Craft and Practices
    • Identifying User Segments, User Personas, Pain Points, and User Needs
    • Writing User Stories
    • Assessing Trade-offs and Prioritizing in AI Product Management
      • Accuracy versus speed
      • Complexity versus simplicity
      • Data quality versus quantity
      • Generalization versus specificity
      • User privacy versus personalization
      • Ethical considerations versus business goals
      • Explainability versus performance
    • Building or Buying? Strategic Trade-offs
    • Defining Your Trade Space
    • Incorporating Trade-offs in a Product Review
  • How to Develop General Product Management Skills
    • Educational Pursuits
    • Hands-on Experience
    • Continuous Learning
  • Essential Leadership and Collaboration Skills
    • Creativity
      • Innovative problem-solving and design thinking
      • Product differentiation
      • Storytelling
    • Communication
    • Leadership
    • Analytical Thinking
    • Empathy
  • Engineering Foundations for Product Managers
    • Working with Code
      • Version control
      • Build process
      • Testing
      • Resource management
    • Key Technical Concepts
      • APIs
      • Algorithms
      • System architecture
      • Software development methodologies
      • Estimation frameworks
        • Top-down estimation.
        • Bottom-up estimation.
        • Parametric evaluation.
        • Expert judgment evaluation.
      • Data analysis software
  • The AI Product Development Lifecycle and Operational Awareness
    • Project Scoping
    • Data Collection
    • Model Training
    • Validation and Testing
    • Deployment
    • Remember to Keep Humans in the Loop
    • Mapping AI Algorithms and Applications
      • Supervised learning
      • Self-supervised learning
      • Unsupervised learning
      • Reinforcement learning
    • Responsible AI Practices
      • Ethics and compliance
      • Explainable AI (XAI)
  • Conclusion
• 4. The AI PMs Day-to-Day
  • The AI PM Career Ladder
    • Execution-Level AI Product Management (Levels 46)
    • AI/ML Product Management (Levels 57)
    • Strategic Leadership (Levels 8+)
  • AI Product Manager Profiles
    • Ethan Cole
    • Mark Cramer
    • Diego Granados
    • Jaclyn Konzelmann
    • Arun Rao
    • Nino Tasca
    • Yana Welinder
    • My Two Cents
    • Cross-functional Collaboration
      • AI and ML teams
      • Operations teams
      • Engineering teams
      • UX teams
      • Business teams
      • Third-party stakeholders
      • Governance, risk, and compliance (GRC) experts
      • Leadership teams
  • Conclusion
• 5. Strategic Thinking in AI
  • Your Business Strategy: Evaluating AI As a Solution
    • AI Might Not Always Be the Answer
    • Disruptive or Sustaining? Navigating the Innovators Dilemma
  • Your AI Strategy: To Build or to Buy?
    • The Build-Versus-Buy Decision Matrix
    • Hybrid Approaches: A Balanced Strategy
  • Your Data Strategy: Populating and Adapting Your Model
    • Synthetic Versus Real-World Data
    • Fine-tuning, RAG, or Grounding?
      • Fine-tuning
      • RAG
      • Grounding
      • A decision-making framework for fine-tuning, RAG, and grounding
  • Product Reviews: Getting Buy-in from Leadership
  • Conclusion
• 6. Setting Goals and Measuring Success
  • Product Health Metrics
  • System Health Metrics
  • AI Proxy Metrics
    • Model Quality Metrics
    • Objective Functions
    • Confusion Matrices
  • OKRs for AI Products
    • Tying Metrics to Goals
    • A Framework for Crafting AI Product OKRs
  • Conclusion
• 7. AI Tools for Product Managers
  • Tools to Enhance the AIPDL
  • Tools for Collaboration and Tracking
  • Conclusion
• 8. Building AI Agents
  • What Is an AI Agent?
    • Not Just Glorified Chatbots
    • Early Rule-Based Agents
  • Agentive Products
    • Comparing Chatbots to AI Agents and Multi-agents
    • The AI Agent Product Landscape
  • Crafting the Right AI Agent for Your Product
    • Task-Specific Vertical Agents Versus General-Purpose Agents
    • Agent Activation
    • Autonomy
    • Feedback and Learning
  • Design Patterns for Agent Interaction
    • Side Panel
    • Floating Bubble
    • Chat Interface
    • Integrated UI
    • Pop-up Notifications
    • Collaborative Browser Interface
    • Scalability, Future-proofing, and Other Considerations
  • Define Success for Your Agent
  • AI Agent Questionnaire
  • Conclusion
• A. Appendix
  • Product Review Template
  • AI Product Requirements Document Template
  • Worksheet 1: Assessing AI Opportunity for Your Organization
    • Section 1: AI Capabilities Assessment
    • Section 2: Company Mission and AI Alignment
    • Section 3: Product Pain Points
    • Section 4: User Impact
    • Section 5: Benefits and Risks Analysis
    • Section 6: Resource Assessment
    • Section 7: Competitive Analysis
    • Section 8: Implementation Decision
    • Next Steps
  • Worksheet 2: AI Implementation Strategy Worksheet
    • Section 1: Core Project Components
    • Section 2: User Impact Assessment
    • Section 3: Benefits and Risks Analysis
    • Section 4: Resource Assessment
    • Section 5: Competitive Analysis
    • Section 6: Implementation Decision
• Index

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