Enterprise Artificial Intelligence Transformation

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 4

AI Myths and Reality 6

The Data and Algorithms Virtuous Cycle 7

The Ongoing Revolution – Why Now? 8

AI: Your Competitive Advantage 13

Chapter 2: What Is AI and How Does It Work? 17

The Development of Narrow AI 18

The First Neural Network 20

Machine Learning 20

Types of Uses for Machine Learning 23

Types of Machine Learning Algorithms 24

Supervised, Unsupervised, and Semisupervised Learning 28

Making Data More Useful 32

Semantic Reasoning 34

Applications of AI 40

Part II: Artificial Intelligence In the Enterprise 43

Chapter 3: AI in E-Commerce and Retail 45

Digital Advertising 46

Marketing and Customer Acquisition 48

Cross-Selling, Up-Selling, and Loyalty 52

Business-to-Business Customer Intelligence 55

Dynamic Pricing and Supply Chain Optimization 57

Digital Assistants and Customer Engagement 59

Chapter 4: AI in Financial Services 67

Anti-Money Laundering 68

Loans and Credit Risk 71

Predictive Services and Advice 72

Algorithmic and Autonomous Trading 75

Investment Research and Market Insights 77

Automated Business Operations 81

Chapter 5: AI in Manufacturing and Energy 85

Optimized Plant Operations and Assets Maintenance 88

Automated Production Lifecycles 91

Supply Chain Optimization 91

Inventory Management and Distribution Logistics 93

Electric Power Forecasting and Demand Response 94

Oil Production 96

Energy Trading 99

Chapter 6: AI in Healthcare 103

Pharmaceutical Drug Discovery 104

Clinical Trials 105

Disease Diagnosis 106

Preparation for Palliative Care 109

Hospital Care 111

PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117

Chapter 7: Developing an AI Strategy 119

Goals of Connected Intelligence Systems 120

The Challenges of Implementing AI 122

AI Strategy Components 126

Steps to Develop an AI Strategy 127

Some Assembly Required 129

Creating an AI Center of Excellence 130

Building an AI Platform 131

Defining a Data Strategy 132

Moving Ahead 134

Chapter 8: The AI Lifecycle 137

Defining Use Cases 138

Collecting, Assessing, and Remediating Data 143

Data Instrumentation 144

Data Cleansing 145

Data Labeling 146

Feature Engineering 148

Selecting and Training a Model 151

Managing Models 160

Testing, Deploying, and Activating Models 164

Testing 164

Governing Model Risk 165

Deploying the Model 166

Activating the Model 166

Production Monitoring 168

Conclusion 169

Chapter 9: Building the Perfect AI Engine 171

AI Platforms versus AI Applications 172

What AI Platform Architectures Should Do 172

Some Important Considerations 179

Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179

Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180

Should a Business Use Batch or Real-Time Processing? 182

Should a Business Use Monolithic or Microservices Architecture? 184

AI Platform Architecture 186

Data Minder 186

Model Maker 187

Inference Activator 188

Performance Manager 190

Chapter 10: Managing Model Risk 193

When Algorithms Go Wrong 195

Mitigating Model Risk 197

Before Modeling 197

During Modeling 199

After Modeling 201

Model Risk Office 209

Chapter 11: Activating Organizational Capability 213

Aligning Stakeholders 214

Organizing for Scale 215

AI Center of Excellence 217

Standards and Project Governance 218

Community, Knowledge, and Training 220

Platform and AI Ecosystem 221

Structuring Teams for Project Execution 222

Managing Talent and Hiring 225

Data Literacy, Experimentation, and Data-Driven Decisions 228

Conclusion 230

Part IV: Delving Deeper Into AI Architecture and Modeling 233

Chapter 12: Architecture and Technical Patterns 235

AI Platform Architecture 236

Data Minder 236

Model Maker 239

Inference Activator 242

Performance Manager 244

Technical Patterns 244

Intelligent Virtual Assistant 244

Personalization and Recommendation Engines 247

Anomaly Detection 250

Ambient Sensing and Physical Control 251

Digital Workforce 255

Conclusion 257

Chapter 13: The AI Modeling Process 259

Defining the Use Case and the AI Task 260

Selecting the Data Needed 262

Setting Up the Notebook Environment and Importing Data 264

Cleaning and Preparing the Data 265

Understanding the Data Using Exploratory Data Analysis 268

Feature Engineering 274

Creating and Selecting the Optimal Model 277

Part V: Looking Ahead 289

Chapter 14: The Future of Society, Work, and AI 291

AI and the Future of Society 292

AI and the Future of Work 294

Regulating Data and Artificial Intelligence 296

The Future of AI: Improving AI Technology 300

Reinforcement Learning 300

Generative Adversarial Learning 302

Federated Learning 303

Natural Language Processing 304

Capsule Networks 305

Quantum Machine Learning 306

And This Is Just the Beginning 307

Further Reading 313

Acknowledgments 317

About the Author 319

Index 321