Demystifying AI for the Enterprise

Table of contents

1. Cover
2. Half Title
3. Title Page
4. Copyright Page
5. Dedication
6. Contents
7. Preface
8. Acknowledgements
9. Author Bios
10. Chapter 1 AI Strategy for the Executive
    1. Introduction
    2. Applications of AI
       1. Determining Practical Realization: Considerations
    3. Definitions
    4. IMPACT Framework for Enterprise AI
       1. Imagination
       2. Maturity
          1. Dimensions of AI Maturity
             1. Strategy
             2. Leadership
             3. Process
             4. Data
          2. Assessing and Inc reasing AI Maturity in Your Organization
       3. People
          1. Considerations on the Data Scientist Role
       4. Automation, Amplification, and Augmentation
       5. Culture
       6. Transformation
    5. Best Practices for the Use of Data in AI
       1. Volume
       2. Variety
       3. Velocity
       4. Value and Veracity
       5. Value
       6. Veracity
          1. Data Fidelity over Data Quality
    6. Conclusions
    7. Notes
11. Chapter 2 Learning Algorithms, Machine/Deep Learning, and Applied AI: A Conceptual Framework
    1. Introduction
       1. Chapter Overview
       2. A Brief History of AI-ML
       3. What’s Different about AI-ML Today?
       4. What Is Machine Learning?
       5. How Do Machines Reason and Learn: A Crash Course in Learning Algorithms
          1. Mastering the Basics of Machine Learning
             1. Task, T
             2. Performance, P
             3. Experience, E
          2. Artificial Neural Networks: An Overview
          3. Deep Learning
       6. A Guided Tour of Learning Algorithms
       7. Best Practices for Successful Machine Learning and AI Applications in Your Enterprise
          1. Ask a Specific Question
          2. Start Simple
          3. Try Many Algorithms
          4. Treat Your Data with Suspicion
          5. Normalize Your Inputs
          6. Validate Your Model
          7. Ensure the Quality of Your Training Data
          8. Set Up a Feedback Loop
          9. Don’t Trust Black Boxes
          10. Correlation Is Not Causation
          11. Monitor Ongoing Performance
          12. Keep Track Of Your Model Changes
          13. Don’t be Fooled by “Accuracy”
    2. Acknowledgments
    3. Notes
12. Chapter 3 AI for Supply Chain Management
    1. Introduction
    2. Understand
    3. Automate
    4. Predict
       1. Forecasting the Future
       2. Predictive Analytics as Inference: What’s Behind Curtain Number Three?
    5. Optimize
    6. Plan: How AI Can Improve the Life of a Planner
    7. Buy: How Buyers Can Leverage AI for Better Pricing and Availability
    8. Make: AI Helps Manufacturing Make More, Better, Faster, and Cheaper
    9. Sell: How AI Can Improve Marketing, Promotion, and Operations Planning in the Supply Chain
    10. Deliver: AI Automates and Streamlines Logistics
    11. Supply Chain Control Towers
        1. Control Towers for logistics
        2. Control Towers for Visibility Across the Enterprise Supply Chain
        3. Control Towers Transcending Organizational Boundaries
    12. Supply Chain Staffing in an AI-Enhanced Enterprise
    13. Conclusions
    14. Notes
13. Chapter 4 HR and Talent Management
    1. Introduction
    2. Workforce Planning and Hiring
       1. Sourcing
       2. Candidate Assessment
       3. Background Checks
       4. Compensation
    3. Helping Employees Succeed In the Workplace
       1. Onboarding
       2. Training
       3. Coaching
       4. Optimizing the Workplace
    4. Retention: Keeping Employees
       1. Category 1: Employees Who Are Already Thinking of Leaving
       2. Category 2: Bad Bosses
       3. Category 3: Corporate Culture and the Importance of the Work (Clarity, Meaning, Influence, and Feedback)
       4. Category 4: Compensation, but not Just Salary
       5. Category 5: Employees in Highly Competitive Roles such as Data Science
    5. Minimizing Risk
    6. Measurement in HR: Statistics, Metrics, and Analytics
    7. Privacy and Ethics
    8. Use of Information
       1. Core Principle
       2. Intent
    9. Conclusions
    10. Notes
14. Chapter 5 Customer Experience Management
    1. Introduction
    2. Customer Experience
       1. Beyond Relationship to Engagement
    3. AI powering the Digital Marketing Funnel
       1. Awareness (Discoverer), Interest (Curious), Intent (Motivated), Conversion (Decider), Loyalty (Customer), Advocacy (Fan)
       2. The Discovery Phase
          1. Product Descriptions
          2. Customer Territory
          3. Birds of a Feather
          4. Getting to Know You
       3. The Interest Phase
       4. The Conversion Phase—Closing the Sale
    4. AI powering the 5 E’s of Experience (Connected to the Marketing Funnel through Maslow’s Hierarchy of Needs)
       1. Encounter (Create Awareness Among Your Stakeholders)
       2. Expectations (Identify Your Stakeholders’ Needs)
       3. Empathy (Meet Your Stakeholders at Their Place of Need)
       4. Engagement (Generate Interest and Curiosity Via Data-Informed Experiences)
       5. Emotion (Apply Sentiment Analysis to Discover How the Experience Made Your Stakeholders Feel)
    5. Recommender Engines and Personalization
       1. History of Customer Personalization
       2. Examples in Marketing (Customers)
       3. Examples in Services (Digital Users)
    6. AI for Workforce Automation (Employees)
    7. AI for Competitive Intelligence & Business Development (Executives and Strategists)
    8. The New Hyper-Personalization
    9. Contextual: IoT = The Internet of Context?
    10. Geospatial: Location Analytics
    11. Cognitive Analytics: Next-best Action, Based on a 360 View of the Customer
    12. The Growing Role of AI in Customer Relations
        1. Easier Support Ticket Management
    13. Big Data Is the Fuel (The Input) That Informs the Enterprise About the Customer: Sources (Digital Devices, IoT, Data Lake,…)
    14. Machine Learning Is the Tool (The Value-Creation Lever) to Gain Insights from the Customer, in 3 Ways
        1. Supervised Learning (Predictive Analytics): Forecasting Customer Needs
        2. Unsupervised Learning (Discovery Analytics): Segment / Pattern / Trend Discovery in Customer Behaviors and Experiences
        3. Reinforcement Learning for Prescriptive Behavioral Analytics: Adapting, Improving, Optimizing Customer Experience
    15. Analytics as the Outcome (i.e., the Business Product)
    16. Notes
15. Chapter 6 AI in Financial Services
    1. Introduction
    2. Why AI Should Be Used to Create a Competitive Advantage in Financial Services
       1. So, What’s Holding Back the Banks?
       2. The Open Banking Revolution that Will Change Everything
    3. Surviving and Thriving with AI
       1. May the Best Network Win
       2. The New Banking: Customer Empathy at Scale
    4. AI Use Cases in Financial Services
       1. Credit Decisioning
       2. Liquidity Management
       3. Management of Physical Cash
       4. Payments
       5. Self-Driving Finance
       6. Customer Support & Conversation Automation
       7. AI as a Fraud Tool
       8. Unique Opportunities in Corporate & Institutional Banking
       9. AI and Algorithmic Trading
          1. Speculation
          2. Arbitrage
          3. Prediction and StatArb
          4. Operational Efficiency
          5. Methods: Supervised Training and Simulation
          6. Supervised Learning
          7. Simulation
          8. Warnings, Caveats, and Advice
       10. AI for Central Banking
    5. How to Identify Your Best Use Cases
    6. Building AI at Scale in Financial Services
       1. Picking the Right Use Cases
       2. Empowering Analytics & Data Science Functions
       3. Designing the Data and Digital Infrastructure
       4. Culture Is Half the Story
       5. A New Kind of Analytics Leadership
       6. Does Your Business Intelligence Have Artificial Intelligence?
    7. Getting to AI Adoption at Scale
       1. Avoiding Common Traps is Key to Success
          1. Trap 1: Lack of Business Buy-in and Understanding of the Analytics Roadmap
          2. Trap 2: Forgetting to Train the Rest of the Business in Analytics
          3. Trap 3: Treating the Analytics Team(s) As an Internal Consultancy
          4. Trap 4: Being Stuck in the Pilot Stage
          5. Trap 5: Giving up After False Starts
          6. Trap 6: Building Huge Data Infrastructure Without the End in Mind
       2. Don’t Go Alone, Use Partnerships
       3. A Note on Responsible AI
       4. AI-Driven Banking: A Peek into Capital One’s Journey
    8. The Future of Financial Services
    9. Notes
16. Chapter 7 Artificial Intelligence in Retail
    1. Introduction
    2. Chapter Overview
    3. The Retail Industry Landscape and Challenges
       1. Geographies
       2. Products
       3. Channels
    4. Importance of Data
       1. Collaboration
       2. Types of Data
       3. Data Quality
    5. Retail Use Cases
       1. Analytics
       2. Sphere of Influence and Big Data
       3. Customer Behavior
       4. Retail Management
          1. Managing Demand
             1. Forecasting
          2. Out of Stock (OOS) and Availability Management
             1. Marketing
             2. Product and Market research
             3. Pricing
             4. Placement
             5. Promotion
          3. Demand Fulfillment
       5. Designing the Stores and E-commerce Websites
          1. Product Matching
          2. Store Design
          3. Product Design
       6. Manufacturing
    6. Responsible Retailing
    7. Future of AI/ML in Retail
    8. Acknowledgements
    9. Notes
17. Chapter 8 Visualization
    1. Introduction
    2. Exploring
       1. Linking
       2. Brushing
       3. Transformations
    3. Interactive User Interfaces
    4. Presenting
       1. One Categorical Variable
       2. One Continuous Variable
          1. Time Series
       3. Two Categorical Variables
       4. Two Continuous Variables
       5. One Categorical Variable and One Continuous Variable
       6. Three Variables
       7. Many Variables
          1. Scatterplot Matrices
          2. Parallel Coordinates
          3. Cluster Heatmaps
       8. Trees
          1. Hierarchical Trees
          2. Minimum Spanning Trees
          3. Additive Trees
          4. Treemaps
          5. Prediction Trees
    5. Diagnosing
    6. Conclusion
    7. Notes
18. Chapter 9 Solution Architectures
    1. Introduction
    2. AI Inference Architecture Taxonomy
       1. Latency
       2. Bandwidth
       3. Legal, Policy, and Security Constraints
    3. Popular Design Patterns for Deploying AI
       1. Model as a Service
       2. Micro Service Architectures
    4. Lab vs. Production
    5. On Debt
       1. Data Dependency
       2. Data Management
       3. Recruitment and Debt
    6. Some Design Considerations for the Production Use of Machine Learning
    7. The Machine-Learning Lifecycle
       1. Featurization
       2. Composition and Reuse
    8. Training Models and Hardware Selection
       1. Training at Home
       2. Training in the Cloud
       3. Tracking Experiments
       4. Near-Term Hardware Developments
    9. Inference and Hardware
       1. Images Per Dollar (Samples Per Dollar)
       2. Images Per Hour (Throughput)
       3. Lowest Latency
    10. Monitoring Machine Learning in production
        1. “Out of Distribution” Errors
        2. Competing Solutions
        3. “Why'd It Do That?”: Explainability and Traceability
    11. Security and the Design of Machine-Learning-Based Services
        1. Piracy
        2. Malicious Inputs
    12. Summary
    13. Notes
19. Chapter 10 AI and Corporate Social Responsibility
    1. Introduction
    2. Things That Keep Us Up At Night
       1. Privacy
       2. The Surveillance Society
       3. Technology That Helps Protect Our Privacy
       4. Federated Learning
       5. Differential Privacy
       6. Inference Privacy
       7. Keeping Your Model Private
       8. Privacy-Preserving Machine Learning in Practice and Business
       9. Bias
       10. Transparency, Explainability, and Interpretability in AI
           1. Questions About Algorithms
           2. Questions about processes
           3. Can We Trust this Algorithm’s Output?
           4. The Algorithm made a Mistake. What Can We Do to Fix It?
           5. Someone Was Harmed. Who Is Liable?
       11. Does This Process Comply with Applicable Regulations?
       12. Something That Helps Us Sleep At Night: Building Good AI
           1. Doing Right
       13. A Guide to Using AI to Effect Positive Social Change
           1. Why AI for Corporate Social Responsibility?
           2. Building a Business Case
    3. A Technical Contributor’s Experience
       1. The Problem Domain
       2. First Find your Use Case
       3. Innovation is a Last Resort
       4. Building a Dataset
       5. First Find the Face
       6. Embeddings
       7. Train, Review… Train again
       8. Sometimes the Right Answer Is No
       9. Return on Investment
       10. A Note on Empathy
    4. An Entrepreneur’s Experience
       1. Call to Action
       2. Tying It All Together: A Practical Example
    5. Summary
    6. Notes
20. Chapter 11 Future of Enterprise AI
    1. Introduction
    2. New Computing Substrates
       1. The Return of the… ASIC?
          1. Ultra-low Power Devices
       2. Neural Turing Machines
    3. Bayesian Machine Learning
    4. Quantum Mechanics and the AI Revolution
       1. Self-Driving Chemistry
          1. Cost
          2. Quality
    5. Quantum Computing and Optimization
    6. The Blockchain, Cryptography and AI
       1. How Cryptography Can Help Solve AI’s Data Problem
       2. Will AI Front-runners Become Monopolists?
    7. Machines of Loving Grace35
    8. Summary
    9. Notes
21. Appendix
    1. Case Study 1: Get More Value from Your Banking Data—How to Turn Your Analytics Team into a Profit Centre
       1. Setting Up the Team’s Strategy
       2. Become Part of the Sales Cycle
       3. Create the Value and Give It Away
       4. Build on Your Success to Become a Profit Centre
    2. Case Study 2: AI in Financial Services: WeBank Practices—A Large Gap in China’s SME Financing Landscape
       1. The Conventional Approach to SME Lending Is Labor-Intensive
       2. WeBank Addresses Challenges with Advanced AI Technologies
       3. Smart Technical Adoption Drives Market Impact
       4. About WeBank
    3. Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance
       1. Introduction
       2. Why Is Configuring a Planning System so Complex?
       3. Oii in Action
       4. Optimizing the Packaging Network of a Major Pharma Company
       5. Optimizing Frozen Food Supply across a Multi-Echelon Supply Network
       6. End to End Multi-Echelon Deployment of Oii Across a Collaborative Network
       7. Summary
       8. Conclusion
    4. Case Study 4: 7-Eleven and Cashierless Stores
       1. Business Opportunity
       2. The Solution
       3. Impact and Lessons Learned
       4. Next Steps
    5. Case Study 5: Paper Quality at Georgia-Pacific
       1. Business Opportunity
       2. The Solution
       3. Impact and Lessons Learned
       4. Next Steps
    6. Case Study 6: GE Healthcare: 1st FDA Clearance for an AI-Enabled X-Ray Device
       1. Background
       2. Getting the “AI Product” Steps Right
          1. Verifying Product Definition and Design
          2. Data Volume
          3. Data Variety
          4. Data Fidelity
          5. Conclusion
    7. Case Study 7: UCSF Health and H2O.ai—Applying Document AI to Automate Workflows in Healthcare
       1. Problem Overview
       2. UCSF: –H2O.ai Partnership
          1. Objectives
       3. H2O.ai’s Document AI
          1. Product Features
          2. Problem Prioritization
       4. Data Science Considerations/Design
          1. Key results from This Successful AI Program
       5. Last Mile: How Do You Enable Workflows with AI?
       6. Feedback Loop: Retraining Models with User Input
       7. What Did We Learn?
       8. What Do We Go Next?
       9. Conclusions
          1. UCSF
          2. H2O.ai
       10. About UCSF Health
       11. About UCSF CDHI
       12. About H2O.ai
       13. About the H2O AI Hybrid Cloud
       14. Notes
22. Index
    1. A
    2. B
    3. C
    4. D
    5. E
    6. F
    7. G
    8. H
    9. I
    10. J
    11. K
    12. L
    13. M
    14. N
    15. O
    16. P
    17. Q
    18. R
    19. S
    20. T
    21. U
    22. V
    23. W
    24. X
    25. Y