کتاب ساخت محصولات مبتنی بر هوش مصنوعی

نقش مدیران محصول هوش مصنوعی

The Stages of AI Evolution • Traditional AI (1950s–Present) • Generative AI (Late 2010s–Present) • 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 PM’s Role • The AI PM’s Skill Set • Organizational Structures • Why Become an AI PM? • What’s Great About Being an AI PM • Subtypes of AI Product Management Roles • Book Road Map • Conclusion

چرخه عمر توسعه محصول هوش مصنوعی

Types of AI Products • 0-to-1 AI Products • 1-to-n AI Products • The AI Product Development Lifecycle • Ideation • Opportunity • Concept and Prototype • Testing and Analysis • Rollout • Conclusion

دانش ضروری برای مدیران محصول هوش مصنوعی

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 • 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 • Communication • Leadership • Analytical Thinking • Empathy • Engineering Foundations for Product Managers • Working with Code • Key Technical Concepts • 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 • Responsible AI Practices • Conclusion

فعالیت‌های روزمره مدیر محصول هوش مصنوعی

The AI PM Career Ladder • Execution-Level AI Product Management (Levels 4–6) • AI/ML Product Management (Levels 5–7) • 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 • Conclusion

تفکر استراتژیک در هوش مصنوعی

Your Business Strategy: Evaluating AI As a Solution • AI Might Not Always Be the Answer • Disruptive or Sustaining? Navigating the Innovator’s 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? • Product Reviews: Getting Buy-in from Leadership • Conclusion

تعیین اهداف و اندازه‌گیری موفقیت

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

ابزارهای هوش مصنوعی برای مدیران محصول

Tools to Enhance the AIPDL • Tools for Collaboration and Tracking • Conclusion

ساخت ایجنت‌های هوش مصنوعی

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