Artificial Intelligence for Business: A Roadmap for Getting Started with AI

✍ Scribed by Jason L. Anderson, Jeffrey L. Coveyduc

• Publisher: Wiley
• Tongue: English
• Leaves: 198
• Category: Library

✦ Synopsis

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

Table of Contents
Preface
Acknowledgments
CHAPTER 1: Introduction
Case Study #1: FANUC Corporation
Case Study #2: H&R Block
Case Study #3: BlackRock, Inc.
How to Get Started
The Road Ahead
Notes
CHAPTER 2: Ideation
An Artificial Intelligence Primer
Becoming an Innovation‐Focused Organization
Idea Bank
Business Process Mapping
Flowcharts, SOPs, and You
Information Flows
Coming Up with Ideas
Value Analysis
Sorting and Filtering
Ranking, Categorizing, and Classifying
Reviewing the Idea Bank
Brainstorming and Chance Encounters
AI Limitations
Pitfalls
Action Checklist
Notes
CHAPTER 3: Defining the Project
The What, Why, and How of a Project Plan
The Components of a Project Plan
Approaches to Break Down a Project
Project Measurability
Balanced Scorecard
Building an AI Project Plan
Pitfalls
Action Checklist
CHAPTER 4: Data Curation and Governance
Data Collection
Leveraging the Power of Existing Systems
The Role of a Data Scientist
Feedback Loops
Making Data Accessible
Data Governance
Are You Data Ready?
Pitfalls
Action Checklist
Notes
CHAPTER 5: Prototyping
Is There an Existing Solution?
Employing vs. Contracting Talent
Scrum Overview
User Story Prioritization
The Development Feedback Loop
Designing the Prototype
Technology Selection
Cloud APIs and Microservices
Internal APIs
Pitfalls
Action Checklist
Notes
CHAPTER 6: Production
Reusing the Prototype vs. Starting from a Clean Slate
Continuous Integration
Automated Testing
Ensuring a Robust AI System
Human Intervention in AI Systems
Ensure Prototype Technology Scales
Cloud Deployment Paradigms
Cloud API's SLA
Continuing the Feedback Loop
Pitfalls
Action Checklist
Notes
CHAPTER 7: Thriving with an AI Lifecycle
Incorporate User Feedback
AI Systems Learn
New Technology
Quantifying Model Performance
Updating and Reviewing the Idea Bank
Knowledge Base
Building a Model Library
Contributing to Open Source
Data Improvements
With Great Power Comes Responsibility
Pitfalls
Action Checklist
Notes
CHAPTER 8: Conclusion
The Intelligent Business Model
The Recap
So What Are You Waiting For?
APPENDIX A: AI Experts
AI Experts
Chris Ackerson
Jeff Bradford
Nathan S. Robinson
Evelyn Duesterwald
Jill Nephew
Rahul Akolkar
Steven Flores
APPENDIX B: Roadmap Action Checklists
Step 1: Ideation
Step 2: Defining the Project
Step 3: Data Curation and Governance
Step 4: Prototyping
Step 5: Production
Thriving with an AI Lifecycle
APPENDIX C: Pitfalls to Avoid
Step 1: Ideation
Step 2: Defining the Project
Step 3: Data Curation and Governance
Step 4: Prototyping
Step 5: Production
Thriving with an AI Lifecycle
Index
End User License Agreement