book

Demystifying AI for the Enterprise

Name: Demystifying AI for the Enterprise
ISBN: 9781351032926

by Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan

December 2021

Intermediate to advanced

434 pages

15h 14m

English

Productivity Press

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Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgements
Author Bios
Chapter 1 AI Strategy for the Executive
IntroductionApplications of AIDetermining Practical Realization: ConsiderationsDefinitionsIMPACT Framework for Enterprise AIImaginationMaturityDimensions of AI MaturityStrategyLeadershipProcessDataAssessing and Inc reasing AI Maturity in Your OrganizationPeopleConsiderations on the Data Scientist RoleAutomation, Amplification, and AugmentationCultureTransformationBest Practices for the Use of Data in AIVolumeVarietyVelocityValue and VeracityValueVeracityData Fidelity over Data QualityConclusionsNotes

Chapter 2 Learning Algorithms, Machine/Deep Learning, and Applied AI: A Conceptual Framework
IntroductionChapter OverviewA Brief History of AI-MLWhat’s Different about AI-ML Today?What Is Machine Learning?How Do Machines Reason and Learn: A Crash Course in Learning AlgorithmsMastering the Basics of Machine LearningTask, TPerformance, PExperience, EArtificial Neural Networks: An OverviewDeep LearningA Guided Tour of Learning AlgorithmsBest Practices for Successful Machine Learning and AI Applications in Your EnterpriseAsk a Specific QuestionStart SimpleTry Many AlgorithmsTreat Your Data with SuspicionNormalize Your InputsValidate Your ModelEnsure the Quality of Your Training DataSet Up a Feedback LoopDon’t Trust Black BoxesCorrelation Is Not CausationMonitor Ongoing PerformanceKeep Track Of Your Model ChangesDon’t be Fooled by “Accuracy”AcknowledgmentsNotes
Chapter 3 AI for Supply Chain Management
IntroductionUnderstandAutomatePredictForecasting the FuturePredictive Analytics as Inference: What’s Behind Curtain Number Three?OptimizePlan: How AI Can Improve the Life of a PlannerBuy: How Buyers Can Leverage AI for Better Pricing and AvailabilityMake: AI Helps Manufacturing Make More, Better, Faster, and CheaperSell: How AI Can Improve Marketing, Promotion, and Operations Planning in the Supply ChainDeliver: AI Automates and Streamlines LogisticsSupply Chain Control TowersControl Towers for logisticsControl Towers for Visibility Across the Enterprise Supply ChainControl Towers Transcending Organizational BoundariesSupply Chain Staffing in an AI-Enhanced EnterpriseConclusionsNotes
Chapter 4 HR and Talent Management
IntroductionWorkforce Planning and HiringSourcingCandidate AssessmentBackground ChecksCompensationHelping Employees Succeed In the WorkplaceOnboardingTrainingCoachingOptimizing the WorkplaceRetention: Keeping EmployeesCategory 1: Employees Who Are Already Thinking of LeavingCategory 2: Bad BossesCategory 3: Corporate Culture and the Importance of the Work (Clarity, Meaning, Influence, and Feedback)Category 4: Compensation, but not Just SalaryCategory 5: Employees in Highly Competitive Roles such as Data ScienceMinimizing RiskMeasurement in HR: Statistics, Metrics, and AnalyticsPrivacy and EthicsUse of InformationCore PrincipleIntentConclusionsNotes
Chapter 5 Customer Experience Management
IntroductionCustomer ExperienceBeyond Relationship to EngagementAI powering the Digital Marketing FunnelAwareness (Discoverer), Interest (Curious), Intent (Motivated), Conversion (Decider), Loyalty (Customer), Advocacy (Fan)The Discovery PhaseProduct DescriptionsCustomer TerritoryBirds of a FeatherGetting to Know YouThe Interest PhaseThe Conversion Phase—Closing the SaleAI powering the 5 E’s of Experience (Connected to the Marketing Funnel through Maslow’s Hierarchy of Needs)Encounter (Create Awareness Among Your Stakeholders)Expectations (Identify Your Stakeholders’ Needs)Empathy (Meet Your Stakeholders at Their Place of Need)Engagement (Generate Interest and Curiosity Via Data-Informed Experiences)Emotion (Apply Sentiment Analysis to Discover How the Experience Made Your Stakeholders Feel)Recommender Engines and PersonalizationHistory of Customer PersonalizationExamples in Marketing (Customers)Examples in Services (Digital Users)AI for Workforce Automation (Employees)AI for Competitive Intelligence & Business Development (Executives and Strategists)The New Hyper-PersonalizationContextual: IoT = The Internet of Context?Geospatial: Location AnalyticsCognitive Analytics: Next-best Action, Based on a 360 View of the CustomerThe Growing Role of AI in Customer RelationsEasier Support Ticket ManagementBig Data Is the Fuel (The Input) That Informs the Enterprise About the Customer: Sources (Digital Devices, IoT, Data Lake,…)Machine Learning Is the Tool (The Value-Creation Lever) to Gain Insights from the Customer, in 3 WaysSupervised Learning (Predictive Analytics): Forecasting Customer NeedsUnsupervised Learning (Discovery Analytics): Segment / Pattern / Trend Discovery in Customer Behaviors and ExperiencesReinforcement Learning for Prescriptive Behavioral Analytics: Adapting, Improving, Optimizing Customer ExperienceAnalytics as the Outcome (i.e., the Business Product)Notes
Chapter 6 AI in Financial Services
IntroductionWhy AI Should Be Used to Create a Competitive Advantage in Financial ServicesSo, What’s Holding Back the Banks?The Open Banking Revolution that Will Change EverythingSurviving and Thriving with AIMay the Best Network WinThe New Banking: Customer Empathy at ScaleAI Use Cases in Financial ServicesCredit DecisioningLiquidity ManagementManagement of Physical CashPaymentsSelf-Driving FinanceCustomer Support & Conversation AutomationAI as a Fraud ToolUnique Opportunities in Corporate & Institutional BankingAI and Algorithmic TradingSpeculationArbitragePrediction and StatArbOperational EfficiencyMethods: Supervised Training and SimulationSupervised LearningSimulationWarnings, Caveats, and AdviceAI for Central BankingHow to Identify Your Best Use CasesBuilding AI at Scale in Financial ServicesPicking the Right Use CasesEmpowering Analytics & Data Science FunctionsDesigning the Data and Digital InfrastructureCulture Is Half the StoryA New Kind of Analytics LeadershipDoes Your Business Intelligence Have Artificial Intelligence?Getting to AI Adoption at ScaleAvoiding Common Traps is Key to SuccessTrap 1: Lack of Business Buy-in and Understanding of the Analytics RoadmapTrap 2: Forgetting to Train the Rest of the Business in AnalyticsTrap 3: Treating the Analytics Team(s) As an Internal ConsultancyTrap 4: Being Stuck in the Pilot StageTrap 5: Giving up After False StartsTrap 6: Building Huge Data Infrastructure Without the End in MindDon’t Go Alone, Use PartnershipsA Note on Responsible AIAI-Driven Banking: A Peek into Capital One’s JourneyThe Future of Financial ServicesNotes
Chapter 7 Artificial Intelligence in Retail
IntroductionChapter OverviewThe Retail Industry Landscape and ChallengesGeographiesProductsChannelsImportance of DataCollaborationTypes of DataData QualityRetail Use CasesAnalyticsSphere of Influence and Big DataCustomer BehaviorRetail ManagementManaging DemandForecastingOut of Stock (OOS) and Availability ManagementMarketingProduct and Market researchPricingPlacementPromotionDemand FulfillmentDesigning the Stores and E-commerce WebsitesProduct MatchingStore DesignProduct DesignManufacturingResponsible RetailingFuture of AI/ML in RetailAcknowledgementsNotes
Chapter 8 Visualization
IntroductionExploringLinkingBrushingTransformationsInteractive User InterfacesPresentingOne Categorical VariableOne Continuous VariableTime SeriesTwo Categorical VariablesTwo Continuous VariablesOne Categorical Variable and One Continuous VariableThree VariablesMany VariablesScatterplot MatricesParallel CoordinatesCluster HeatmapsTreesHierarchical TreesMinimum Spanning TreesAdditive TreesTreemapsPrediction TreesDiagnosingConclusionNotes
Chapter 9 Solution Architectures
IntroductionAI Inference Architecture TaxonomyLatencyBandwidthLegal, Policy, and Security ConstraintsPopular Design Patterns for Deploying AIModel as a ServiceMicro Service ArchitecturesLab vs. ProductionOn DebtData DependencyData ManagementRecruitment and DebtSome Design Considerations for the Production Use of Machine LearningThe Machine-Learning LifecycleFeaturizationComposition and ReuseTraining Models and Hardware SelectionTraining at HomeTraining in the CloudTracking ExperimentsNear-Term Hardware DevelopmentsInference and HardwareImages Per Dollar (Samples Per Dollar)Images Per Hour (Throughput)Lowest LatencyMonitoring Machine Learning in production“Out of Distribution” ErrorsCompeting Solutions“Why'd It Do That?”: Explainability and TraceabilitySecurity and the Design of Machine-Learning-Based ServicesPiracyMalicious InputsSummaryNotes
Chapter 10 AI and Corporate Social Responsibility
IntroductionThings That Keep Us Up At NightPrivacyThe Surveillance SocietyTechnology That Helps Protect Our PrivacyFederated LearningDifferential PrivacyInference PrivacyKeeping Your Model PrivatePrivacy-Preserving Machine Learning in Practice and BusinessBiasTransparency, Explainability, and Interpretability in AIQuestions About AlgorithmsQuestions about processesCan We Trust this Algorithm’s Output?The Algorithm made a Mistake. What Can We Do to Fix It?Someone Was Harmed. Who Is Liable?Does This Process Comply with Applicable Regulations?Something That Helps Us Sleep At Night: Building Good AIDoing RightA Guide to Using AI to Effect Positive Social ChangeWhy AI for Corporate Social Responsibility?Building a Business CaseA Technical Contributor’s ExperienceThe Problem DomainFirst Find your Use CaseInnovation is a Last ResortBuilding a DatasetFirst Find the FaceEmbeddingsTrain, Review… Train againSometimes the Right Answer Is NoReturn on InvestmentA Note on EmpathyAn Entrepreneur’s ExperienceCall to ActionTying It All Together: A Practical ExampleSummaryNotes
Chapter 11 Future of Enterprise AI
IntroductionNew Computing SubstratesThe Return of the… ASIC?Ultra-low Power DevicesNeural Turing MachinesBayesian Machine LearningQuantum Mechanics and the AI RevolutionSelf-Driving ChemistryCostQualityQuantum Computing and OptimizationThe Blockchain, Cryptography and AIHow Cryptography Can Help Solve AI’s Data ProblemWill AI Front-runners Become Monopolists?Machines of Loving Grace35SummaryNotes
Appendix
Case Study 1: Get More Value from Your Banking Data—How to Turn Your Analytics Team into a Profit Centre
Setting Up the Team’s StrategyBecome Part of the Sales CycleCreate the Value and Give It AwayBuild on Your Success to Become a Profit Centre
Case Study 2: AI in Financial Services: WeBank Practices—A Large Gap in China’s SME Financing Landscape
The Conventional Approach to SME Lending Is Labor-IntensiveWeBank Addresses Challenges with Advanced AI TechnologiesSmart Technical Adoption Drives Market ImpactAbout WeBank
Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance
IntroductionWhy Is Configuring a Planning System so Complex?Oii in ActionOptimizing the Packaging Network of a Major Pharma CompanyOptimizing Frozen Food Supply across a Multi-Echelon Supply NetworkEnd to End Multi-Echelon Deployment of Oii Across a Collaborative NetworkSummaryConclusion
Case Study 4: 7-Eleven and Cashierless Stores
Business OpportunityThe SolutionImpact and Lessons LearnedNext Steps
Case Study 5: Paper Quality at Georgia-Pacific
Business OpportunityThe SolutionImpact and Lessons LearnedNext Steps
Case Study 6: GE Healthcare: 1st FDA Clearance for an AI-Enabled X-Ray Device
BackgroundGetting the “AI Product” Steps RightVerifying Product Definition and DesignData VolumeData VarietyData FidelityConclusion
Case Study 7: UCSF Health and H2O.ai—Applying Document AI to Automate Workflows in Healthcare
Problem OverviewUCSF: –H2O.ai PartnershipObjectivesH2O.ai’s Document AIProduct FeaturesProblem PrioritizationData Science Considerations/DesignKey results from This Successful AI ProgramLast Mile: How Do You Enable Workflows with AI?Feedback Loop: Retraining Models with User InputWhat Did We Learn?What Do We Go Next?ConclusionsUCSFH2O.aiAbout UCSF HealthAbout UCSF CDHIAbout H2O.aiAbout the H2O AI Hybrid CloudNotes
Index
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Content preview from Demystifying AI for the Enterprise

Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance

David Evans

Co-Founder Oii

Introduction
Why Is Configuring a Planning System so Complex?
Oii in Action
Optimizing the Packaging Network of a Major Pharma Company
Optimizing Frozen Food Supply across a Multi-Echelon Supply Network
End to End Multi-Echelon Deployment of Oii Across a Collaborative Network
Summary
Conclusion

Introduction

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Publisher Resources

ISBN: 9781351032926

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Demystifying AI for the Enterprise

by Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan

Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance

Contents

Introduction

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