Оглавление
Jason L. Anderson. Artificial Intelligence for Business
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Artificial Intelligence for Business. A Roadmap for Getting Started with AI
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
1. Ideation
2. Defining the Project
3. Data Curation and Governance
4. Prototyping
5. Production
Thriving with an AI Lifecycle
The Road Ahead
Notes
CHAPTER 2 Ideation. An Artificial Intelligence Primer
Natural Language Processing
Programmatic NLP
Statistical NLP
Machine Learning
Markov Chains
Hidden Markov Models
Neural Networks
Image Recognition/Classification
Becoming an Innovation-Focused Organization
Idea Bank
Business Process Mapping
Flowcharts, SOPs, and You
Information Flows
Coming Up with Ideas
Value Analysis
A Value Analysis Example
Sorting and Filtering
Ranking, Categorizing, and Classifying
Reviewing the Idea Bank
Brainstorming and Chance Encounters
Cross-Departmental Exchanges
AI Limitations
Pitfalls
Pitfall 1: A Narrow Focus
Pitfall 2: Going Overboard with the Process
Pitfall 3: Focusing On the Projects Rather than the Culture
Pitfall 4: Overestimating AI's Capabilities
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
Approach 1: Design Thinking
Sample Design Thinking Session
Step 1: Determine Personas
Step 2: Create an Empathy Map
Step 3: Define the Goals
Step 4: Define User Stories
Approach 2: Systems Thinking
Boundaries
Subsystems
Approach 3: Scenario Planning
The Delphi Method
Project Measurability
Balanced Scorecard
Building an AI Project Plan
Pitfalls
Pitfall 1: Not Having Stakeholder Buy-In
Pitfall 2: Inventing or Misrepresenting Actual Problems
Pitfall 3: Prematurely Building the Solution
Pitfall 4: Neglecting to Define Formal Change Request Procedures
Pitfall 5: Not Having Measurable Success Criteria
Action Checklist
CHAPTER 4 Data Curation and Governance
Data Collection
Internal Data Collection: Digital
Internal Data Collection: Physical
Data Collection via Licensing
Data Collection via Crowdsourcing
Leveraging the Power of Existing Systems
The Role of a Data Scientist
Feedback Loops
Making Data Accessible
Data Governance
Creating a Data Governance Board
Initiating Data Governance
HIPAA
GDPR
Are You Being Data Responsible?
Are You Data Ready?
Pitfalls
Pitfall 1: Insufficient Data Licensing
Pitfall 2: Not Having Representative Ground Truth
Pitfall 3: Insufficient Data Security
Pitfall 4: Ignoring User Privacy
Pitfall 5: Backups
Action Checklist
Notes
CHAPTER 5 Prototyping
Is There an Existing Solution?
Employing vs. Contracting Talent
Finding a Firm
The Hybrid Approach
Scrum Overview
User Story Prioritization
The Development Feedback Loop
Designing the Prototype
Technology Selection
Cloud APIs and Microservices
Internal APIs
Pitfalls
Pitfall 1: Spending Too Much Time Planning
Pitfall 2: Trying to Prototype Too Much
Pitfall 3: The Wrong Tool for the Job
Action Checklist
Notes
CHAPTER 6 Production
Reusing the Prototype vs. Starting from a Clean Slate
Continuous Integration
The Continuous Integration Pipeline
True Continuous Integration
Automated Testing
Test Types
AI Model Testing Example
What if You Find a Bug?
Infrastructure Testing
Ensuring a Robust AI System
Human Intervention in AI Systems
Ensure Prototype Technology Scales
Scalability and the Cloud
Cloud Deployment Paradigms
Cloud API's SLA
Continuing the Feedback Loop
Pitfalls
Pitfall 1: End Users Resist Adopting the Technology
Pitfall 2: Micromanaging the Development Team
Pitfall 3: Not Having the Correct Skills Available
Action Checklist
Notes
CHAPTER 7 Thriving with an AI Lifecycle
Incorporate User Feedback
AI Systems Learn
New Technology
Quantifying Model Performance
Precision
Recall
F1 Score
Updating and Reviewing the Idea Bank
Knowledge Base
Building a Model Library
Model Library Components
An Example Model Library Entry
Model Library Solutions
Contributing to Open Source
Data Improvements
With Great Power Comes Responsibility
Pitfalls
Pitfall 1: Assuming a Project Ends Once It Is Implemented
Pitfall 2: Ignoring User Feedback
Pitfall 3: Providing Inadequate User Training
Action Checklist
Notes
CHAPTER 8 Conclusion
The Intelligent Business Model
The Recap
Step 1: Ideation
Step 2: Defining the Project
Step 3: Data Curation and Governance
Step 4: Prototyping
Step 5: Production
Repeat: Thriving with the AI Lifecycle
So What Are You Waiting For?
APPENDIX A AI Experts. AI Experts
Chris Ackerson
1. Given you have expertise with AI and how people interact with it, are there any insights or tips that you'd like to share?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
Jeff Bradford
1. What has been your experience pulling together data for AI purposes and what are your thoughts on the need for building an initial prototype?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
Nathan S. Robinson
1. What are the key benefits of AI based on your experiences using it within your organizations?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts you think are relevant to AI adoption?
Evelyn Duesterwald
1. Given your expertise with AI security, are there any insights or tips that you've found to be effective?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
Jill Nephew
1. What has been your experience pulling together data for AI purposes, and what are your thoughts on the need for building an initial prototype?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
Rahul Akolkar
1. Given you have expertise with AI and how people interact with it, are there any insights or tips that you've found?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
Steven Flores
1. Given your expertise with AI, are there any insights or tips that you've found to be effective?
2. What has been your biggest challenge while adopting AI?
3. What advances in AI do you envision over the next five years?
4. What job functions do you see as a prime target for AI assistance over the next three years?
5. Any other thoughts about AI adoption you'd like to share?
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. Pitfall 1: A Narrow Focus
Pitfall 2: Going Overboard with the Process
Pitfall 3: Focusing On the Projects Rather than the Culture
Pitfall 4: Overestimating AI's Capabilities
Step 2: Defining the Project. Pitfall 5: Not Having Stakeholder Buy-In
Pitfall 6: Inventing or Misrepresenting Actual Problems
Pitfall 7: Prematurely Building the Solution
Pitfall 8: Neglecting to Define Formal Change Request Procedures
Pitfall 9: Not Having Measurable Success Criteria
Step 3: Data Curation and Governance. Pitfall 10: Insufficient Data Licensing
Pitfall 11: Not Having Representative Ground Truth
Pitfall 12: Insufficient Data Security
Pitfall 13: Ignoring User Privacy
Pitfall 14: Backups
Step 4: Prototyping. Pitfall 15: Spending Too Much Time Planning
Pitfall 16: Trying to Prototype Too Much
Pitfall 17: The Wrong Tool for the Job
Step 5: Production. Pitfall 18: End Users Resist Adopting the Technology
Pitfall 19: Micromanaging the Development Team
Pitfall 20: Not Having the Correct Skills Available
Thriving with an AI Lifecycle. Pitfall 21: Assuming a Project Ends Once It Is Implemented
Pitfall 22: Ignoring User Feedback
Pitfall 23: Providing Inadequate User Training
Index
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