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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
JEFFREY L. COVEYDUC is Vice President and Master Inventor at IBM. His diverse background consists of positions that encompass the creation of innovative, technologically advanced global AI solutions and client adoption. JASON L. ANDERSON is a Partner and CTO with the data consultancy, Comp Three, where he established a new AI line of business. He is also a former IBM Cognitive Architect and Master Inventor. He received both BS and MS degrees in Computer Science from California Polytechnic State University, SLO.
Inhaltsangabe
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
