book

Building AI-Powered Products

by Marily Nika

February 2025

Beginner to intermediate

230 pages

5h 58m

English

O'Reilly Media, Inc.

Read now

Unlock full access

Includes

Includes Quizzes

Why AI Product Management?Who Should Read This Book?What This Book CoversO’Reilly Online LearningHow to Contact UsAcknowledgmentsClosing Thoughts
The Stages of AI EvolutionTraditional AI (1950s–Present)Generative AI (Late 2010s–Present)Artificial General Intelligence (2030s?)Artificial Superintelligence (~2040s?)How Products Leverage AIUnique Features of AIProbabilistic NatureDependency on DataModel DriftThe Need for Model Interpretability and ExplainabilityAutomated Decision MakingScalabilityHow These Unique Features Can Impact User ExperienceSuperpowers of AI and GenAISuperpower 1: Learning from Massive Data and ContentSuperpower 2: Personalization at ScaleSuperpower 3: Automating and Optimizing WorkflowsSuperpower 4: Generating New Content and ExperiencesSuperpower 5: Prediction and ForecastingSuperpower 6: Real-Time AdaptationSuperpower 7: Unlocking New Types of User Experiences with New Form FactorsThe AI PM’s RoleThe AI PM’s Skill SetOrganizational StructuresWhy Become an AI PM?What’s Great About Being an AI PMSubtypes of AI Product Management RolesBook Road MapConclusion
Types of AI Products0-to-1 AI Products1-to-n AI ProductsThe AI Product Development LifecycleIdeationOpportunityConcept and PrototypeTesting and AnalysisRolloutConclusion
Core Product Management Craft and PracticesIdentifying User Segments, User Personas, Pain Points, and User NeedsWriting User StoriesAssessing Trade-offs and Prioritizing in AI Product ManagementBuilding or Buying? Strategic Trade-offsDefining Your Trade SpaceIncorporating Trade-offs in a Product ReviewHow to Develop General Product Management SkillsEducational PursuitsHands-on ExperienceContinuous LearningEssential Leadership and Collaboration SkillsCreativityCommunicationLeadershipAnalytical ThinkingEmpathyEngineering Foundations for Product ManagersWorking with CodeKey Technical ConceptsThe AI Product Development Lifecycle and Operational AwarenessProject ScopingData CollectionModel TrainingValidation and TestingDeploymentRemember to Keep Humans in the LoopMapping AI Algorithms and ApplicationsResponsible AI PracticesConclusion
The AI PM Career LadderExecution-Level AI Product Management (Levels 4–6)AI/ML Product Management (Levels 5–7)Strategic Leadership (Levels 8+)AI Product Manager ProfilesEthan ColeMark CramerDiego GranadosJaclyn KonzelmannArun RaoNino TascaYana WelinderMy Two CentsCross-functional CollaborationConclusion
Your Business Strategy: Evaluating AI As a SolutionAI Might Not Always Be the AnswerDisruptive or Sustaining? Navigating the Innovator’s DilemmaYour AI Strategy: To Build or to Buy?The Build-Versus-Buy Decision MatrixHybrid Approaches: A Balanced StrategyYour Data Strategy: Populating and Adapting Your ModelSynthetic Versus Real-World DataFine-tuning, RAG, or Grounding?Product Reviews: Getting Buy-in from LeadershipConclusion
Product Health MetricsSystem Health MetricsAI Proxy MetricsModel Quality MetricsObjective FunctionsConfusion MatricesOKRs for AI ProductsTying Metrics to GoalsA Framework for Crafting AI Product OKRsConclusion
Tools to Enhance the AIPDLTools for Collaboration and TrackingConclusion
What Is an AI Agent?Not Just Glorified ChatbotsEarly Rule-Based AgentsAgentive ProductsComparing Chatbots to AI Agents and Multi-agentsThe AI Agent Product LandscapeCrafting the Right AI Agent for Your ProductTask-Specific Vertical Agents Versus General-Purpose AgentsAgent ActivationAutonomyFeedback and LearningDesign Patterns for Agent InteractionSide PanelFloating BubbleChat InterfaceIntegrated UIPop-up NotificationsCollaborative Browser InterfaceScalability, Future-proofing, and Other ConsiderationsDefine Success for Your AgentAI Agent QuestionnaireConclusion
Product Review TemplateAI Product Requirements Document TemplateWorksheet 1: Assessing AI Opportunity for Your OrganizationSection 1: AI Capabilities AssessmentSection 2: Company Mission and AI AlignmentSection 3: Product Pain PointsSection 4: User ImpactSection 5: Benefits and Risks AnalysisSection 6: Resource AssessmentSection 7: Competitive AnalysisSection 8: Implementation DecisionNext StepsWorksheet 2: AI Implementation Strategy WorksheetSection 1: Core Project ComponentsSection 2: User Impact AssessmentSection 3: Benefits and Risks AnalysisSection 4: Resource AssessmentSection 5: Competitive AnalysisSection 6: Implementation Decision

Content preview from Building AI-Powered Products

Preface

Artificial intelligence (AI) has been a core area of research and development for computer scientists and engineers for decades. While the potential of AI remained constrained by hardware and software for years, recent breakthroughs have allowed product managers to harness AI’s power, scaling it into practical and impactful solutions.

In 2023, the launch of OpenAI’s ChatGPT and similar large language models transformed the landscape of digital products, empowering product managers to infuse AI into user experiences in ways that were unimaginable just a year before. In the span of a year, the market has seen a Cambrian explosion of generative AI models, from image generation tools like Midjourney, Stable Diffusion, and DALL-E to advanced search solutions like Deepseek—and even multimodal AI systems like Google’s Gemini. As of 2024, most tech companies are focused on integrating some aspect of AI into their products, with many prioritizing the transition from task-specific AI solutions to general-purpose AI and building new user experiences with multimodal AI and personalized AI agents.

Building AI-Powered Products: The Essential Guide to AI and GenAI Product Management is a road map for navigating the complexities of creating AI-driven products. Drawing on my experiences at Google and Meta, and complemented by insights from the AI Product Academy, this guide aims to equip product managers, entrepreneurs, and business leaders with the tools and frameworks to confidently ...