Building AI-Powered Products - Helion

ebook

Autor: Marily Nika
ISBN: 9781098152666
stron: 230, Format: ebook
Data wydania: 2025-02-15
Księgarnia: Helion

Cena książki: 135,15 zł (poprzednio: 168,94 zł)
Oszczędzasz: 20% (-33,79 zł)

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Drawing from her experience at Google and Meta, Dr. Marily Nika delivers the definitive guide for product managers building AI and GenAI powered products. Packed with smart strategies, actionable tools, and real-world examples, this book breaks down the complex world of AI agents and generative AI products into a playbook for driving innovation to help product leaders bridge the gap between niche AI and GenAI technologies and user pain points. Whether you're already leading product teams or are an aspiring product manager, and regardless of your prior knowledge with AI, this guide will empower you to confidently navigate every stage of the AI product lifecycle.

Confidently manage AI product development with tools, frameworks, strategic insights, and real-world examples from Google, Meta, OpenAI, and more
Lead product orgs to solve real problems via agentic AI and GenAI capabilities
Gain AI Awareness and technical fluency to work with AI models, LLMs, and the algorithms that power them; get cross-functional alignment; make strategic trade-offs; and set OKRs

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Spis treści

Building AI-Powered Products eBook -- spis treści

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