book

AI Product Manager's Handbook - Second Edition

by Irene Bratsis

November 2024

Beginner to intermediate

488 pages

17h 42m

English

Packt Publishing

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Getting the most out of this book – get to know your free benefitsDefinitions – what AI is and is notIntroducing ML and DLThe old – exploring MLA brief history of DLThe new – exploring DLInvisible influencesML versus DL – understanding the differenceMLDLLearning paradigms in MLSupervised learningUnsupervised learningSemi-supervised learningReinforcement learningLLMs, NLP, GANs, and generative AISucceeding in AI – how well-managed AI companies do infrastructure rightThe order – what is the optimal flow and where does every part of the process live?Step 1 – DefinitionStep 2 – Data availability and centralizationStep 3 – Choose and train the model Step 4 – Feedback Step 5 – Deployment Step 6 – Continuous maintenanceStoring and managing dataDatabaseData warehouseData lake (and lakehouse)Data pipelinesManaging projects – IaaSDeployment strategies – what do we do with these outputs?Shadow deployment strategyA/B testing model deployment strategyCanary deployment strategyExampleThe promise of AI – where is AI taking us?SummaryAdditional resourcesReferencesJoin us on Discord
Understanding the stages of NPDStep 1 – DiscoveryStep 2 – DefineStep 3 – DesignStep 4 – ImplementationStep 5 – MarketingStep 6 – Beta testingStep 7 – LaunchModel types – from linear regression to neural networksOKRsObjectives and key resultsMetrics and KPIsTraining – when is a model ready for market?Deployment – what happens after training?Testing and troubleshootingEthical retraining – the ethics of how often we update our modelsThe current state of accountabilityImplementing ethical standards in your organizationSummaryAdditional resourcesReferences
Types of neural networksMultilayer perceptronsCase studyRadial basis function networksSelf-organizing mapsConvolutional neural networks Recurrent neural networks Long short-term memory networksDeep belief networks Exploring generative AI modelsGenerative adversarial networksAutoencodersDiffusion models Transformer models Emerging technologies – ancillary and related techExplainability – optimizing for ethics, caveats, and responsibilityGuidelines for successSummaryReferences
The professionals – examples of B2B products done rightThe artists – examples of B2C products done rightThe pioneers – examples of blue ocean productsThe rebels – examples of red ocean productsThe GOATs – examples of differentiated disruptive and dominant strategy productsThe dominant strategyThe disruptive strategyThe differentiated strategySummaryReferencesJoin us on Discord
Money and value – how AI could revolutionize our economic systemsExamples and use casesLimitations and uneven adoptionProduct perspectiveSickness and health – the benefits of AI and nanotech across healthcareExamples and use casesProduct perspectiveGoods and services – growth in commercial applicationsExamples and use casesProduct perspectiveGovernment and autonomy – how AI will shape our borders and freedomBasic needs – AI for GoodSummaryAdditional resourcesReferences
Stages of AI product developmentPhase 1 – IdeationPhase 2 – Data managementPhase 3 – Research and developmentPhase 4 – DeploymentAI/ML product dream teamAI PMAI/ML/data strategists/architectsData engineerData analystData scientistML engineerFrontend/backend/full stack engineerQA/testing engineerUX designer/researcherCustomer success specialistMarketing/sales/go-to-market teamInvesting in your tech stackProductizing AI-powered outputs – how AI product management is differentAI customizationSelling AI – product management as a higher octave of salesCase study AI product development cycleTeam breakdownTech stackAI outputsGTM strategy and verticalizationSummaryReferences

Basics of productizingAI versus traditional software product managementHow are the products different?ScalabilityProfit marginsUncertaintyHow are the products similar?Agile developmentDataHow does the role of an AI PM compare with a traditional PM?B2B versus B2C – productizing business modelsDomain knowledge for B2B products – understanding the needs of your marketExperimentation with B2C products – discover the needs of your collectiveUsing AIOps/MLOpsConsistency and AIOps/MLOps – reliance and trustPerformance evaluation – testing, retraining, and hyperparameter tuningFeedback loop – relationship buildingCase study SummaryReferences
Domains – orienting AI toward specific areasUnderstanding your marketUnderstanding how your product design will serve your marketBuilding your AI product strategyVerticals – examination of some key domainsFintechChatbots and virtual assistantsFraud detectionAlgorithmic trading and predictive analyticsHealthcareImaging and diagnosisDrug discovery and researchMarketing – segmentationManufacturing – predictive managementEducation – personalized learningCybersecurity – anomaly detection and user and entity behavior analyticsThought leadership – learning from peer groupsCase Study The marketProduct design and strategyThought leadershipSummaryReferencesJoin us on Discord
Product design elements 101 Understanding the end userDefining the problemExperimentationValidationIterationAestheticsDocumentationWhat makes the AI-native product design process special? User obsessionMachine learningExplainabilityChoosing your priorities wiselyEnsuring clarityAdding complexityBrandingWhat’s the story you’re telling?Set the stageCharactersProgressionKnowledgeCall to actionCase studySummaryReferences
Value metrics – a guide to north star metrics, KPIs and OKRsNorth star metricsKPIs and other metricsOKRs and product strategyHacking – product-led growthThe tech stack – early signalsCustomer data platforms (CDPs)Customer engagement platforms (CEPs)Product analytics toolsA/B testing toolsData warehousesBusiness Intelligence (BI) toolsGrowth-hacking toolsManaging costs and pricing – AI is expensiveCase studyNorth star metricsKPIsOKRsGrowth hackingSummaryReferencesJoin us on Discord
The head – Managing alignment VisionGood vision statementsBad vision statementsCommunicationThe heart – Managing people and values SafetyEmpowermentThe guts – Managing the rest Case study SummaryReferences Join us on Discord
Evolve or die – when change is the only constantChanges in the Fourth Industrial RevolutionCultural and structural changesWorking with an AI consultantWorking with a third partyThe first hireThe first AI teamFear is not the answer – there is more to gain than lose (or spend)Anticipating potential risksHow LLMs are evolving and the rise of open source LLM capabilitiesCase studyImplementationRisksMarkers of successSummaryReferences
Highest growth areas for AI integrationApplied/embedded AI – applied and integrated use casesEthical AI – responsibility and privacyGenAI – immersive applicationsAutonomous AI development – TuringBotsLow-hanging fruit – quickest wins for AI enablementRiding the GenAI waveSummaryReferences
Ideation – what’s possible, what’s desirable, and what’s probableList 1 – valueList 2 – scopeList 3 – reachCase studyValueScopeReachData management – the bloodstream of the companyPreparation and researchEnsuring quality partnershipsBenchmarking and defining successCompetition – love your enemiesProduct strategy – building a blueprint that works for everyoneProduct visionProduct strategyProduct goalsThe product roadmapRed flags and green flags – what to look for and watch out forRed flagsGreen flagsSummaryAdditional resourcesJoin us on Discord
The evolution of product design Ideation: Managing expectationsData management: Strategizing and integrityR&D: Mapping the user experience journeyDeployment: Are you ready to scale?Expansion: What makes the evolved AI product special? Decisions and insightsAutomation and adaptabilityPersonalization and learningChoosing your words carefully Product language fitAccessibility and inclusivityBuilding with trust and security BiasAccountability and explainabilitySecurityCase studyIntegrating AI into ProjectABZ: A project management tool created by ABCDZCoIdeation and researchDesign and developmentMarketing and communicationSummaryReferences
The head – managing alignment Strategic alignmentFeedback loopsThe heart – managing the people and values The guts – managing data, infrastructure, and ongoing maintenance Infrastructure and dataMaintenanceCase studyAI transformation for ProjectABZManagement alignmentPeople alignmentOperational alignmentResults and outcomesSummaryJoin us on Discord
Bolstering your knowledge in theory and practice TheoryPracticeWhat an AI PM looks like todayThe importance of communities Choosing your AI PM specializationCase study SummaryReferences
A job family of many hats Technical proficiencyTechnologistAI expertTechnical translatorData stewardData strategistQuality controllerAnalystBusiness acumenStrategistRevenue driverPartnership builderInnovatorMarket researcherCompetitor analystCommunicationProject managerChange agentStakeholder managerEducatorRisk assessorLeadershipVisionaryEthicistTeam leaderStorytellerMotivatorKnowledge sharerProblem solvingCustomer advocateRegulatory complierFacilitatorData-driven decision makerAdaptability managerConflict resolverThe AI whisperer and the role of communicating accessiblyCommon challenges and opportunities as you’re leveling up in your careerThe importance of self-care Case studySummary
Projecting – what’s your ideal AI PM roadmap?Level 1 – building a foundationLevel 2 – strategic growthLevel 3 – specializing and leadingLevel 4 – a light for othersLearning – staying informed and inspiredThought leadershipCertifications and degreesProfessional developmentNetworking – deepening your involvement with the professional communityGrowing – the student becomes the teacher Embracing challengesReflectingEstablishing a feedback loopWhat’s next? The world is our oyster Case studyProjectingLearningNetworkingGrowingWhat’s next?Summary
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Overview

The "AI Product Manager's Handbook" is your ultimate guide to mastering the art and science of managing AI-driven products. In this book, you will learn how to bridge the gap between AI capabilities and real-world product needs, create successful AI strategies, and manage the challenges of scaling AI solutions. Gain actionable insights from an experienced AI product expert.

What this Book will help me do

Develop a strategic vision for AI-integrated products, aligning business objectives with AI capabilities.
Understand and address ethical and regulatory considerations in building responsible AI solutions.
Master techniques for cross-functional collaboration, including working with data scientists and engineers.
Identify market opportunities and evaluate AI-specific risks to ensure effective product solutions.
Gain hands-on skills in implementing AI technologies in a product management context.

Author(s)

Irene Bratsis is a recognized expert in AI product management, with extensive experience developing and leading successful AI-driven projects across various industries. Irene's pragmatic and structured frameworks guide readers through the complexities of managing AI in real-world scenarios. Her insightful writing distills advanced AI concepts into practical advice accessible to both new and experienced product managers.

Who is it for?

This book is ideal for product managers, AI professionals, and business strategists looking to harness AI in their products and strategies. Readers with a basic understanding of AI will find this book enriching for both foundational knowledge and advanced applications. Whether you're aspiring to enter AI product management or enhance your current expertise, this book equips you with the strategies and tools needed to succeed. Engineers, designers, and leaders aiming to align AI and business goals will also gain valuable insights.