Enterprise Artificial Intelligence TranSFORMATION

WE ARE ON THE BRINK OF THE ALGORITHMIC ENTERPRISE. ENTREPRENEURS AND BUSINESS LEADERS CAN HAVE A METAMORPHIC IMPACT ON HUMANITY BY CATALYZING APPLIED AI FOR EVERY ENTERPRISE.

What People Are Saying

“Given the breadth of opportunities and the importance of a balanced approach to your organization’s AI journey, this book provides a critical reference for business leaders on how to think about your company’s – as well as your personal – AI plan.”

— Steve Guggenheimer, Corporate VP for AI, Microsoft

“Artificial intelligence is more than the mere use of machine learning algorithms, and its successful implementation is equally as much about building a practical solution that fits into the ecosystem and culture of its end-users. This insightful book explains how to do it - without the hot air.”

— Aldo Faisal, Professor of Artificial Intelligence, Imperial College London

ABOUT THE BOOK

A Playbook for the Next Generation of Business and Technology Leaders TO ACHIEVE, MAINTAIN, AND GROW SUCCESSFUL AND COST-EFFECTIVE AI AT SCALE.

Enterprise Artificial Intelligence Transformation is a one-of-a-kind book that delves into both the business and technical aspects of AI business transformation. We have the data available, we see that society is heading in the direction of AI and machine learning, and we can envision ourselves as the algorithmic, data-driven market leaders of the future. What we have been missing—up to now—is a clear guide to understanding the issues involved in actually taking that step, making the transition to large-scale artificially intelligent operation. This is that guide.

It offers a unique framework for making revolutionary changes to the way your organization works. Artificial intelligence is poised to revolutionize business and, thereby, humanity. We are on the verge of creating algorithmic enterprises capable of leveraging AI to improve business decisions, streamline processes, and develop new product lines. With this new technology at our fingertips, we can create business insights from data that would have been impossible in the pre-AI era.

Established organizations are ready to implement enterprise-wide AI strategies, but for established firms there are significant hurdles to doing so. From change management and culture issues to technical and human resource challenges, AI presents a major organizational upheaval. To help you overcome these hurdles and put in place a solid, scalable AI capability, this book offers a roadmap to understanding and seizing upon the opportunities that AI is opening up.

Inside this book, you’ll find an overview of artificial intelligence and associated technologies like deep learning, machine learning, semantic reasoning, and more. You’ll also see how AI is currently being used in industries from banking to manufacturing, with real-world use cases that will spark your creativity and empower you to make the most of AI investment. From high-level AI strategy and leadership to more granular architecture and modeling considerations, this book provides all the necessary information for moving forward with artificial intelligence at scale. For AI practitioners, analytics managers, and leaders with no prior AI experience, Enterprise Artificial Intelligence Transformation is an indispensable playbook for transforming business.

Table of Contents

Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks xv

  • Prologue: A Guide to This Book xxi

  • Part I: A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE 1

    • Chapter 1: A Revolution in the Making 3
      The Impact of the Four Revolutions | AI Myths and Reality | The Data and Algorithms Virtuous Cycle | The Ongoing Revolution – Why Now? | AI: Your Competitive Advantage

    • Chapter 2: What Is AI and How Does It Work? 17
      The Development of Narrow AI | The First Neural Network | Machine Learning | Types of Uses for Machine Learning | Types of Machine Learning Algorithms | Supervised, Unsupervised, and Semisupervised Learning | Making Data More Useful | Semantic Reasoning | Applications of AI

  • Part II: ARTIFICIAL INTELLIGENCE IN THE ENTERPRISE 43

    • Chapter 3: AI in E-Commerce and Retail 45
      Digital Advertising | Marketing and Customer Acquisition | Cross-Selling, Up-Selling, and Loyalty | Business-to-Business Customer Intelligence | Dynamic Pricing and Supply Chain Optimization | Digital Assistants and Customer Engagement

    • Chapter 4: AI in Financial Services 67
      Anti-Money Laundering | Loans and Credit Risk | Predictive Services and Advice | Algorithmic and Autonomous Trading | Investment Research and Market Insights | Automated Business Operations

    • Chapter 5: AI in Manufacturing and Energy 85
      Optimized Plant Operations and Assets Maintenance | Automated Production Lifecycles | Supply Chain Optimization | Inventory Management and Distribution Logistics | Electric Power Forecasting and Demand Response | Oil Production | Energy Trading

    • Chapter 6: AI in Healthcare 103
      Pharmaceutical Drug Discovery | Clinical Trials | Disease Diagnosis | Preparation for Palliative Care | Hospital Care

  • PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117

    • Chapter 7: Developing an AI Strategy 119
      Goals of Connected Intelligence Systems | The Challenges of Implementing AI | AI Strategy Components | Steps to Develop an AI Strategy | Some Assembly Required | Creating an AI Center of Excellence | Building an AI Platform | Defining a Data Strategy | Moving Ahead

    • Chapter 8: The AI Lifecycle 137
      Defining Use Cases | Collecting, Assessing, and Remediating Data | Data Instrumentation | Data Cleansing | Data Labeling | Feature Engineering | Selecting and Training a Model | Managing Models | Testing, Deploying, and Activating Models | Testing | Governing Model Risk | Deploying the Model | Activating the Model | Production Monitoring

    • Chapter 9: Building the Perfect AI Engine 171
      AI Platforms versus AI Applications | What AI Platform Architectures Should Do | Some Important Considerations | Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? | Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? | Should a Business Use Batch or Real-Time Processing? | Should a Business Use Monolithic or Microservices Architecture? | AI Platform Architecture | Data Minder | Model Maker | Inference Activator | Performance Manager

    • Chapter 10: Managing Model Risk 193
      When Algorithms Go Wrong | Mitigating Model Risk | Before Modeling | During Modeling | After Modeling | Model Risk Office

    • Chapter 11: Activating Organizational Capability 213
      Aligning Stakeholders | Organizing for Scale | AI Center of Excellence | Standards and Project Governance | Community, Knowledge, and Training | Platform and AI Ecosystem | Structuring Teams for Project Execution | Managing Talent and Hiring | Data Literacy, Experimentation, and Data-Driven Decisions

  • Part IV: DELVING DEEPER INTO AI ARCHITECTURE AND MODELING 233

    • Chapter 12: Architecture and Technical Patterns 235
      AI Platform Architecture | Data Minder | Model Maker | Inference Activator | Performance Manager | Technical Patterns | Intelligent Virtual Assistant | Personalization and Recommendation Engines | Anomaly Detection | Ambient Sensing and Physical Control | Digital Workforce

    • Chapter 13: The AI Modeling Process 259
      Defining the Use Case and the AI Task | Selecting the Data Needed | Setting Up the Notebook Environment and Importing Data | Cleaning and Preparing the Data | Understanding the Data Using Exploratory Data Analysis | Feature Engineering | Creating and Selecting the Optimal Model

  • Part V: LOOKING AHEAD 289

    • Chapter 14: The Future of Society, Work, and AI 291
      AI and the Future of Society | AI and the Future of Work | Regulating Data and Artificial Intelligence | The Future of AI: Improving AI Technology | Reinforcement Learning | Generative Adversarial Learning | Federated Learning | Natural Language Processing | Capsule Networks | Quantum Machine Learning | And This Is Just the Beginning

  • Further Reading 313

  • Acknowledgments 317

  • About the Author 319

  • Index 321