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Enterprise artificial intelligence transformation : a playbook for the next generation of business and technology leaders / Rashed Haq.

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"Enterprise Artificial Intelligence Transformation helps business and technology professionals master the segments of the AI market that relate to them and achieve, maintain, and grow successful and cost-effective AI. Readers will learn to build and sustain an AI organizational capability at sc...

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Bibliographic Details
Online Access: Full Text (via ProQuest)
Main Author: Haq, Rashed (Author)
Format: eBook
Language:English
Published: Hoboken, New Jersey : John Wiley & Sons, Inc., [2020]
Subjects:
Business enterprises > Technological innovations.
Artificial intelligence > Economic aspects.
Organizational learning.
Organizational effectiveness.
Table of Contents:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks
  • Prologue: A Guide to This Book
  • PART I A Brief Introduction to Artificial Intelligence
  • Chapter 1 A Revolution in the Making
  • 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?
  • 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
  • Chapter 3 AI in E-Commerce and Retail
  • 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
  • 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
  • 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
  • Pharmaceutical Drug Discovery
  • Clinical Trials
  • Disease Diagnosis.
  • Preparation for Palliative Care
  • Hospital Care
  • PART III Building Your Enterprise AI Capability
  • Chapter 7 Developing an AI Strategy
  • 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
  • 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
  • Conclusion
  • Chapter 9 Building the Perfect AI Engine
  • 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?

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