Demystifying AI for the Enterprise: A Playbook for Business Value and Digital Transformation
by Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Prashant Natarajan, Shantha Mohan
- Length: 360 pages
- Edition: 1
- Language: English
- Publisher: Productivity Press
- Publication Date: 2021-12-17
- ISBN-10: 103214520X
- ISBN-13: 9781032145204
- Sales Rank: #2315717 (See Top 100 Books)
Artificial Intelligence in its various forms – machine learning, chat-bots, robots, agents, etc. – is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets.
Increasing business process and workflow maturity coupled with recent trends in cloud computing, datafication, IoT, cyber security, and advanced analytics, there is appreciation that the challenges of tomorrow cannot be solely addressed with today’s people, processes, and products. A recent Gartner article supports our contention that AI is essential because it “promises to solve problems organizations could not before because it delivers benefits that no humans could legitimately perform.”
There is still considerable mystery, hype, and fear about AI. A considerable amount t of current discourse focuses on a dystopian future – with adversity impacted individuals/employees/society. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, current state of business/technology, or the primarily augmentative nature of tomorrow’s AI.
Our book demystifies AI for the enterprise. Our journey takes the reader from the basics (definitions, state of the art, etc.) to a multi-industry journey, and concludes with validated expert advice on everything an organization and its people must do to succeed. Along the way, we also debunk myths, provide practical pointers, and include best practices with appropriate vignettes.
In summary, AI brings to enterprises capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues and achieve better customer/user satisfaction.
Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Acknowledgements Author Bios Chapter 1: AI Strategy for the Executive Introduction Applications of AI Determining Practical Realization: Considerations Definitions IMPACT Framework for Enterprise AI Imagination Maturity Dimensions of AI Maturity Strategy Leadership Process Data Assessing and Inc reasing AI Maturity in Your Organization People Considerations on the Data Scientist Role Automation, Amplification, and Augmentation Culture Transformation Best Practices for the Use of Data in AI Volume Variety Velocity Value and Veracity Value Veracity Data Fidelity over Data Quality Conclusions Notes Chapter 2: Learning Algorithms, Machine/Deep Learning, and Applied AI: A Conceptual Framework Introduction Chapter Overview A Brief History of AI-ML What’s Different about AI-ML Today? What Is Machine Learning? How Do Machines Reason and Learn: A Crash Course in Learning Algorithms Mastering the Basics of Machine Learning Task, T Performance, P Experience, E Artificial Neural Networks: An Overview Deep Learning A Guided Tour of Learning Algorithms Best Practices for Successful Machine Learning and AI Applications in Your Enterprise Ask a Specific Question Start Simple Try Many Algorithms Treat Your Data with Suspicion Normalize Your Inputs Validate Your Model Ensure the Quality of Your Training Data Set Up a Feedback Loop Don’t Trust Black Boxes Correlation Is Not Causation Monitor Ongoing Performance Keep Track Of Your Model Changes Don’t be Fooled by “Accuracy” Acknowledgments Notes Chapter 3: AI for Supply Chain Management Introduction Understand Automate Predict Forecasting the Future Predictive Analytics as Inference: What’s Behind Curtain Number Three? Optimize Plan: How AI Can Improve the Life of a Planner Buy: How Buyers Can Leverage AI for Better Pricing and Availability Make: AI Helps Manufacturing Make More, Better, Faster, and Cheaper Sell: How AI Can Improve Marketing, Promotion, and Operations Planning in the Supply Chain Deliver: AI Automates and Streamlines Logistics Supply Chain Control Towers Control Towers for logistics Control Towers for Visibility Across the Enterprise Supply Chain Control Towers Transcending Organizational Boundaries Supply Chain Staffing in an AI-Enhanced Enterprise Conclusions Notes Chapter 4: HR and Talent Management Introduction Workforce Planning and Hiring Sourcing Candidate Assessment Background Checks Compensation Helping Employees Succeed In the Workplace Onboarding Training Coaching Optimizing the Workplace Retention: Keeping Employees Category 1: Employees Who Are Already Thinking of Leaving Category 2: Bad Bosses Category 3: Corporate Culture and the Importance of the Work (Clarity, Meaning, Influence, and Feedback) Category 4: Compensation, but not Just Salary Category 5: Employees in Highly Competitive Roles such as Data Science Minimizing Risk Measurement in HR: Statistics, Metrics, and Analytics Privacy and Ethics Use of Information Core Principle Intent Conclusions Notes Chapter 5: Customer Experience Management Introduction Customer Experience Beyond Relationship to Engagement AI powering the Digital Marketing Funnel Awareness (Discoverer), Interest (Curious), Intent (Motivated), Conversion (Decider), Loyalty (Customer), Advocacy (Fan) The Discovery Phase Product Descriptions Customer Territory Birds of a Feather Getting to Know You The Interest Phase The Conversion Phase—Closing the Sale AI 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 Personalization History of Customer Personalization Examples 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-Personalization Contextual: IoT = The Internet of Context? Geospatial: Location Analytics Cognitive Analytics: Next-best Action, Based on a 360 View of the Customer The Growing Role of AI in Customer Relations Easier Support Ticket Management Big 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 Ways Supervised Learning (Predictive Analytics): Forecasting Customer Needs Unsupervised Learning (Discovery Analytics): Segment / Pattern / Trend Discovery in Customer Behaviors and Experiences Reinforcement Learning for Prescriptive Behavioral Analytics: Adapting, Improving, Optimizing Customer Experience Analytics as the Outcome (i.e., the Business Product) Notes Chapter 6: AI in Financial Services Introduction Why AI Should Be Used to Create a Competitive Advantage in Financial Services So, What’s Holding Back the Banks? The Open Banking Revolution that Will Change Everything Surviving and Thriving with AI May the Best Network Win The New Banking: Customer Empathy at Scale AI Use Cases in Financial Services Credit Decisioning Liquidity Management Management of Physical Cash Payments Self-Driving Finance Customer Support & Conversation Automation AI as a Fraud Tool Unique Opportunities in Corporate & Institutional Banking AI and Algorithmic Trading Speculation Arbitrage Prediction and StatArb Operational Efficiency Methods: Supervised Training and Simulation Supervised Learning Simulation Warnings, Caveats, and Advice AI for Central Banking How to Identify Your Best Use Cases Building AI at Scale in Financial Services Picking the Right Use Cases Empowering Analytics & Data Science Functions Designing the Data and Digital Infrastructure Culture Is Half the Story A New Kind of Analytics Leadership Does Your Business Intelligence Have Artificial Intelligence? Getting to AI Adoption at Scale Avoiding Common Traps is Key to Success Trap 1: Lack of Business Buy-in and Understanding of the Analytics Roadmap Trap 2: Forgetting to Train the Rest of the Business in Analytics Trap 3: Treating the Analytics Team(s) As an Internal Consultancy Trap 4: Being Stuck in the Pilot Stage Trap 5: Giving up After False Starts Trap 6: Building Huge Data Infrastructure Without the End in Mind Don’t Go Alone, Use Partnerships A Note on Responsible AI AI-Driven Banking: A Peek into Capital One’s Journey The Future of Financial Services Notes Chapter 7: Artificial Intelligence in Retail Introduction Chapter Overview The Retail Industry Landscape and Challenges Geographies Products Channels Importance of Data Collaboration Types of Data Data Quality Retail Use Cases Analytics Sphere of Influence and Big Data Customer Behavior Retail Management Managing Demand Forecasting Out of Stock (OOS) and Availability Management Marketing Product and Market research Pricing Placement Promotion Demand Fulfillment Designing the Stores and E-commerce Websites Product Matching Store Design Product Design Manufacturing Responsible Retailing Future of AI/ML in Retail Acknowledgements Notes Chapter 8: Visualization Introduction Exploring Linking Brushing Transformations Interactive User Interfaces Presenting One Categorical Variable One Continuous Variable Time Series Two Categorical Variables Two Continuous Variables One Categorical Variable and One Continuous Variable Three Variables Many Variables Scatterplot Matrices Parallel Coordinates Cluster Heatmaps Trees Hierarchical Trees Minimum Spanning Trees Additive Trees Treemaps Prediction Trees Diagnosing Conclusion Notes Chapter 9: Solution Architectures Introduction AI Inference Architecture Taxonomy Latency Bandwidth Legal, Policy, and Security Constraints Popular Design Patterns for Deploying AI Model as a Service Micro Service Architectures Lab vs. Production On Debt Data Dependency Data Management Recruitment and Debt Some Design Considerations for the Production Use of Machine Learning The Machine-Learning Lifecycle Featurization Composition and Reuse Training Models and Hardware Selection Training at Home Training in the Cloud Tracking Experiments Near-Term Hardware Developments Inference and Hardware Images Per Dollar (Samples Per Dollar) Images Per Hour (Throughput) Lowest Latency Monitoring Machine Learning in production “Out of Distribution” Errors Competing Solutions “Why'd It Do That?”: Explainability and Traceability Security and the Design of Machine-Learning-Based Services Piracy Malicious Inputs Summary Notes Chapter 10: AI and Corporate Social Responsibility Introduction Things That Keep Us Up At Night Privacy The Surveillance Society Federated Learning Differential Privacy Inference Privacy Keeping Your Model Private Privacy-Preserving Machine Learning in Practice and Business Bias Transparency, Explainability, and Interpretability in AI Questions About Algorithms Questions about processes Can 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 AI Doing Right A Guide to Using AI to Effect Positive Social Change Why AI for Corporate Social Responsibility? Building a Business Case A Technical Contributor’s Experience The Problem Domain First Find your Use Case Innovation is a Last Resort Building a Dataset First Find the Face Embeddings Train, Review… Train again Sometimes the Right Answer Is No Return on Investment A Note on Empathy An Entrepreneur’s Experience Call to Action Tying It All Together: A Practical Example Summary Notes Chapter 11: Future of Enterprise AI Introduction New Computing Substrates The Return of the… ASIC? Ultra-low Power Devices Neural Turing Machines Bayesian Machine Learning Quantum Mechanics and the AI Revolution Self-Driving Chemistry Cost Quality Quantum Computing and Optimization The Blockchain, Cryptography and AI How Cryptography Can Help Solve AI’s Data Problem Will AI Front-runners Become Monopolists? Machines of Loving Grace Summary Notes 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 Strategy Become Part of the Sales Cycle Create the Value and Give It Away Build 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-Intensive WeBank Addresses Challenges with Advanced AI Technologies Smart Technical Adoption Drives Market Impact About WeBank Case Study 3: How Orchestrated Intelligence Inc. (Oii) Is Utilizing Artificial Intelligence to Model a Transformation in Supply Chain Performance 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 Case Study 4: 7-Eleven and Cashierless Stores Business Opportunity The Solution Impact and Lessons Learned Next Steps Case Study 5: Paper Quality at Georgia-Pacific Business Opportunity The Solution Impact and Lessons Learned Next Steps Case Study 6: GE Healthcare: 1 st FDA Clearance for an AI-Enabled X-Ray Device Background Getting the “AI Product” Steps Right Verifying Product Definition and Design Data Volume Data Variety Data Fidelity Conclusion Case Study 7: UCSF Health and H2O.ai—Applying Document AI to Automate Workflows in Healthcare Problem Overview UCSF: –H2O.ai Partnership Objectives H2O.ai’s Document AI Product Features Problem Prioritization Data Science Considerations/Design Key results from This Successful AI Program Last Mile: How Do You Enable Workflows with AI? Feedback Loop: Retraining Models with User Input What Did We Learn? What Do We Go Next? Conclusions UCSF H2O.ai About UCSF Health About UCSF CDHI About H2O.ai About the H2O AI Hybrid Cloud Notes Index
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