,ntents
1. Cover
2. Preface
3. Acknowledgments
4. CHAPTER 1: Introduction
1. Case Study #1: FANUC Corporation
2. Case Study #2: H&R Block
3. Case Study #3: BlackRock, Inc.
4. How to Get Started
5. The Road Ahead
6. Notes
5. CHAPTER 2: Ideation
1. An Artificial Intelligence Primer
2. Becoming an Innovation-Focused Organization
3. Idea Bank
4. Business Process Mapping
5. Flowcharts, SOPs, and You
6. Information Flows
7. Coming Up with Ideas
8. Value Analysis
9. Sorting and Filtering
10. Ranking, Categorizing, and Classifying
11. Reviewing the Idea Bank
12. Brainstorming and Chance Encounters
13. AI Limitations
14. Pitfalls
15. Action Checklist
16. Notes
6. CHAPTER 3: Defining the Project
1. The What, Why, and How of a Project Plan
2. The Components of a Project Plan
3. Approaches to Break Down a Project
4. Project Measurability
5. Balanced Scorecard
6. Building an AI Project Plan
7. Pitfalls
8. Action Checklist
7. CHAPTER 4: Data Curation and Governance
1. Data Collection
2. Leveraging the Power of Existing Systems
3. The Role of a Data Scientist
4. Feedback Loops
5. Making Data Accessible
6. Data Governance
, 7. Are You Data Ready?
8. Pitfalls
9. Action Checklist
10. Notes
8. CHAPTER 5: Prototyping
1. Is There an Existing Solution?
2. Employing vs. Contracting Talent
3. Scrum Overview
4. User Story Prioritization
5. The Development Feedback Loop
6. Designing the Prototype
7. Technology Selection
8. Cloud APIs and Microservices
9. Internal APIs
10. Pitfalls
11. Action Checklist
12. Notes
9. CHAPTER 6: Production
1. Reusing the Prototype vs. Starting from a Clean Slate
2. Continuous Integration
3. Automated Testing
4. Ensuring a Robust AI System
5. Human Intervention in AI Systems
6. Ensure Prototype Technology Scales
7. Cloud Deployment Paradigms
8. Cloud API's SLA
9. Continuing the Feedback Loop
10. Pitfalls
11. Action Checklist
12. Notes
10. CHAPTER 7: Thriving with an AI Lifecycle
1. Incorporate User Feedback
2. AI Systems Learn
3. New Technology
4. Quantifying Model Performance
5. Updating and Reviewing the Idea Bank
6. Knowledge Base
7. Building a Model Library
8. Contributing to Open Source
9. Data Improvements
10. With Great Power Comes Responsibility
11. Pitfalls
12. Action Checklist
13. Notes
11. CHAPTER 8: Conclusion
1. The Intelligent Business Model
, 2. The Recap
3. So What Are You Waiting For?
12. APPENDIX A: AI Experts
1. AI Experts
2. Chris Ackerson
3. Jeff Bradford
4. Nathan S. Robinson
5. Evelyn Duesterwald
6. Jill Nephew
7. Rahul Akolkar
8. Steven Flores
13. APPENDIX B: Roadmap Action Checklists
1. Step 1: Ideation
2. Step 2: Defining the Project
3. Step 3: Data Curation and Governance
4. Step 4: Prototyping
5. Step 5: Production
6. Thriving with an AI Lifecycle
14. APPENDIX C: Pitfalls to Avoid
1. Step 1: Ideation
2. Step 2: Defining the Project
3. Step 3: Data Curation and Governance
4. Step 4: Prototyping
5. Step 5: Production
6. Thriving with an AI Lifecycle
15. Index
16. End User License Agreement
List of Tables
1. Chapter 2
1. TABLE 2.1 A sample idea bank
2. Chapter 5
1. TABLE 5.1 Sample tech selection chatbot technologies
List of Illustrations
1. Chapter 1
1. FIGURE 1.1 Example of a FANUC Robot
2. FIGURE 1.2 The AI Adoption Roadmap
2. Chapter 2
1. FIGURE 2.1 The Standard Interpretation of the Turing Test
2. FIGURE 2.2 A Neural Network with a Single Neuron
3. FIGURE 2.3 A Fully Connected Neural Network with Multiple Layers
4. FIGURE 2.4 A Venn Diagram Describing How Deep Learning Relates to AI
1. Cover
2. Preface
3. Acknowledgments
4. CHAPTER 1: Introduction
1. Case Study #1: FANUC Corporation
2. Case Study #2: H&R Block
3. Case Study #3: BlackRock, Inc.
4. How to Get Started
5. The Road Ahead
6. Notes
5. CHAPTER 2: Ideation
1. An Artificial Intelligence Primer
2. Becoming an Innovation-Focused Organization
3. Idea Bank
4. Business Process Mapping
5. Flowcharts, SOPs, and You
6. Information Flows
7. Coming Up with Ideas
8. Value Analysis
9. Sorting and Filtering
10. Ranking, Categorizing, and Classifying
11. Reviewing the Idea Bank
12. Brainstorming and Chance Encounters
13. AI Limitations
14. Pitfalls
15. Action Checklist
16. Notes
6. CHAPTER 3: Defining the Project
1. The What, Why, and How of a Project Plan
2. The Components of a Project Plan
3. Approaches to Break Down a Project
4. Project Measurability
5. Balanced Scorecard
6. Building an AI Project Plan
7. Pitfalls
8. Action Checklist
7. CHAPTER 4: Data Curation and Governance
1. Data Collection
2. Leveraging the Power of Existing Systems
3. The Role of a Data Scientist
4. Feedback Loops
5. Making Data Accessible
6. Data Governance
, 7. Are You Data Ready?
8. Pitfalls
9. Action Checklist
10. Notes
8. CHAPTER 5: Prototyping
1. Is There an Existing Solution?
2. Employing vs. Contracting Talent
3. Scrum Overview
4. User Story Prioritization
5. The Development Feedback Loop
6. Designing the Prototype
7. Technology Selection
8. Cloud APIs and Microservices
9. Internal APIs
10. Pitfalls
11. Action Checklist
12. Notes
9. CHAPTER 6: Production
1. Reusing the Prototype vs. Starting from a Clean Slate
2. Continuous Integration
3. Automated Testing
4. Ensuring a Robust AI System
5. Human Intervention in AI Systems
6. Ensure Prototype Technology Scales
7. Cloud Deployment Paradigms
8. Cloud API's SLA
9. Continuing the Feedback Loop
10. Pitfalls
11. Action Checklist
12. Notes
10. CHAPTER 7: Thriving with an AI Lifecycle
1. Incorporate User Feedback
2. AI Systems Learn
3. New Technology
4. Quantifying Model Performance
5. Updating and Reviewing the Idea Bank
6. Knowledge Base
7. Building a Model Library
8. Contributing to Open Source
9. Data Improvements
10. With Great Power Comes Responsibility
11. Pitfalls
12. Action Checklist
13. Notes
11. CHAPTER 8: Conclusion
1. The Intelligent Business Model
, 2. The Recap
3. So What Are You Waiting For?
12. APPENDIX A: AI Experts
1. AI Experts
2. Chris Ackerson
3. Jeff Bradford
4. Nathan S. Robinson
5. Evelyn Duesterwald
6. Jill Nephew
7. Rahul Akolkar
8. Steven Flores
13. APPENDIX B: Roadmap Action Checklists
1. Step 1: Ideation
2. Step 2: Defining the Project
3. Step 3: Data Curation and Governance
4. Step 4: Prototyping
5. Step 5: Production
6. Thriving with an AI Lifecycle
14. APPENDIX C: Pitfalls to Avoid
1. Step 1: Ideation
2. Step 2: Defining the Project
3. Step 3: Data Curation and Governance
4. Step 4: Prototyping
5. Step 5: Production
6. Thriving with an AI Lifecycle
15. Index
16. End User License Agreement
List of Tables
1. Chapter 2
1. TABLE 2.1 A sample idea bank
2. Chapter 5
1. TABLE 5.1 Sample tech selection chatbot technologies
List of Illustrations
1. Chapter 1
1. FIGURE 1.1 Example of a FANUC Robot
2. FIGURE 1.2 The AI Adoption Roadmap
2. Chapter 2
1. FIGURE 2.1 The Standard Interpretation of the Turing Test
2. FIGURE 2.2 A Neural Network with a Single Neuron
3. FIGURE 2.3 A Fully Connected Neural Network with Multiple Layers
4. FIGURE 2.4 A Venn Diagram Describing How Deep Learning Relates to AI
