Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.
Table of Contents
Preface ix
Acknowledgments xi
Chapter 1 Introduction 1
Case Study #1: FANUC Corporation 2
Case Study #2: H&R Block 4
Case Study #3: BlackRock, Inc. 5
How to Get Started 6
The Road Ahead 10
Notes 11
Chapter 2 Ideation 13
An Artificial Intelligence Primer 13
Becoming an Innovation-Focused Organization 23
Idea Bank 25
Business Process Mapping 27
Flowcharts, SOPs, and You 28
Information Flows 29
Coming Up with Ideas 31
Value Analysis 31
Sorting and Filtering 34
Ranking, Categorizing, and Classifying 35
Reviewing the Idea Bank 37
Brainstorming and Chance Encounters 38
AI Limitations 41
Pitfalls 44
Action Checklist 45
Notes 46
Chapter 3 Defining the Project 47
The What, Why, and How of a Project Plan 48
The Components of a Project Plan 49
Approaches to Break Down a Project 53
Project Measurability 62
Balanced Scorecard 63
Building an AI Project Plan 64
Pitfalls 66
Action Checklist 69
Chapter 4 Data Curation and Governance 71
Data Collection 73
Leveraging the Power of Existing Systems 81
The Role of a Data Scientist 81
Feedback Loops 82
Making Data Accessible 84
Data Governance 85
Are You Data Ready? 89
Pitfalls 90
Action Checklist 94
Notes 94
Chapter 5 Prototyping 97
Is There an Existing Solution? 97
Employing vs. Contracting Talent 99
Scrum Overview 101
User Story Prioritization 103
The Development Feedback Loop 105
Designing the Prototype 106
Technology Selection 107
Cloud APIs and Microservices 110
Internal APIs 112
Pitfalls 112
Action Checklist 114
Notes 114
Chapter 6 Production 117
Reusing the Prototype vs. Starting from a Clean Slate 117
Continuous Integration 119
Automated Testing 124
Ensuring a Robust AI System 128
Human Intervention in AI Systems 129
Ensure Prototype Technology Scales 131
Cloud Deployment Paradigms 133
Cloud API’s SLA 135
Continuing the Feedback Loop 135
Pitfalls 135
Action Checklist 137
Notes 137
Chapter 7 Thriving with an AI Lifecycle 139
Incorporate User Feedback 140
AI Systems Learn 142
New Technology 144
Quantifying Model Performance 145
Updating and Reviewing the Idea Bank 147
Knowledge Base 148
Building a Model Library 150
Contributing to Open Source 155
Data Improvements 157
With Great Power Comes Responsibility 158
Pitfalls 159
Action Checklist 161
Notes 161
Chapter 8 Conclusion 163
The Intelligent Business Model 164
The Recap 164
So What are You Waiting For? 168
Appendix A AI Experts 169
AI Experts 169
Chris Ackerson 169
Jeff Bradford 173
Nathan S. Robinson 175
Evelyn Duesterwald 177
Jill Nephew 179
Rahul Akolkar 183
Steven Flores 187
Appendix B Roadmap Action Checklists 191
Step 1: Ideation 191
Step 2: Defining the Project 191
Step 3: Data Curation and Governance 192
Step 4: Prototyping 192
Step 5: Production 193
Thriving with an AI Lifecycle 193
Appendix C Pitfalls to Avoid 195
Step 1: Ideation 195
Step 2: Defining the Project 196
Step 3: Data Curation and Governance 199
Step 4: Prototyping 203
Step 5: Production 204
Thriving with an AI Lifecycle 206
Index 209
