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Artificial Intelligence for Business

Author Jason L. Anderson , Jeffrey L. Coveyduc
Release Date: 2020/04/01
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Book Description

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

  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
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