Table of Contents
Introduction: How AI Will Redefine Management 1
Five practices that successful managers need to master. Vegard Kolbjørnsrud Richard Amico Robert J. Thomas
Section 1 AI Fundamentals
1 Three Questions About AI That Every Employee Should Be Able to Answer 11
How does it work, what is it good at, and what should it never do? Emma Martinho-Truswell
2 What Every Manager Should Know About Machine Learning 17
A nontechnical primer. Mike Yeomans
3 The Three Types of AI 27
First, understand which technologies perform which types of tasks. Thomas H. Davenport Rajeev Ronanki
4 AI Doesn't Have to Be Too Complicated or Expensive for Your Business 37
Focus on data quality, not quantity. Andrew Ng
Section 2 Building Your AI Team
5 How AI Fits into Your Data Science Team 47
Get over the cultural hardies and avoid exaggerated claims.
An Interview Hilary Mason Walter Frick
6 Ramp Up Your Team's Predictive Analytics Skills 53
Three pitfalls they need to avoid. Eric Siegel
7 Assembling Your AI Operations Team 61
A top-notch model is no good if your people can't connect it to your existing systems. Terence Tse Mark Esposito Takaaki Mizuno Danny Goh
Section 3 Picking the Right Projects
8 How to Spot a Machine Learning Opportunity 71
What do you want to predict, and do you have the data? Kathryn Hume
9 A Simple Tool to Start Making Decisions with the Help of AI 79
Use the AI Canvas. Ajay Agrawal Joshua Gans Avi Goldfarb
10 How to Pick the Right Automation Project 89
Invest in the ones that will build your organization's capabilities. Bhaskar Ghosh Rajendra Prasad Gayathri Pallail
Section 4 Working with AI
11 Collaborative Intelligence: Humans and AI Are Joining Forces 97
They're enhancing each other's strengths. H. James Wilson Paul Daugherty
12 How to Get Employees to Embrace AI 117
The sooner resisters get onboard, the sooner you will see results. Brad Power
13 A Better Way to Onboard AI 123
Understand it as a tool to assist people rather than replace them. Boris Babic Daniel L. Chen Theodoros Evgeniou Anne-Laure Fayard
14 Managing AI Decision-Making Tools 139
A framework to determine when and how humans need to stay involved. Michael Ross James Taylor
15 Your Company's Algorithms Will Go Wrong. Have a Plan in Place. 147
An AI designed to do X will eventually fail to do X. Roman V. Yampolskiy Section 5 Managing Ethics and Bias
16 A Practical Guide to Ethical AI 155
AI doesn't just scale solutions-it also scales risk. Reid Blackman
17 AI Can Help Address Inequity-If Companies Earn Users' Trust 167
A case from Airbnb shows how good algorithms can have negative effects. Shunyuan Zhang Kannan Srinivasan Param Vir Singh Nitin Mehta
18 Take Action to Mitigate Ethical Risks 179
It starts with three critical conversations. Reid Blackman Beena Ammanath
Section 6 Taking the Next Steps with AI and Machine Learning
19 How No-Code Platforms Can Bring AI to Small and Midsize Businesses 189
Three features to look for as you consider the right tool for your company. Jonathon Reilly
20 The Power of Natural Language Processing 197
NLP can help companies with brainstorming, summarizing, and researching. Ross Gruetzemacher
21 Reinforcement Learning is Ready for Business 205
Learning through trial and error can lead to more creative solutions. Kathryn Hume Matthew E. Taylor
Epilogue
Scaling AI
22 How to Scale AI in Your Organization 217
Invest in processes, people, and tools. Manasi Vartak
Appendix: Case Study: Will a Bank's New 225
Technology Help or Hurt Morale?
Weighing the benefits of AI against the downsides of impersonal decision-making. Leonard A. Schlesinger
Glossary of Key AI Terms 237
Index 243
