s: Build New Capabilities Put Your Data To Work Drive Digital Transformation

Introduction: How AI Will Redefine Management

Five practices that successful managers need to master.

BY VEGARD KOLBJØRNSRUD, RICHARD AMICO, AND ROBERT J. THOMAS

SECTION ONE

AI Fundamentals

1. Three Questions About AI That Every Employee Should Be Able to Answer

How does it work, what is it good at, and what should it never do?

BY EMMA MARTINHO-TRUSWELL

2. What Every Manager Should Know About Machine Learning

A nontechnical primer.

BY MIKE YEOMANS

3. The Three Types of AI

First, understand which technologies perform which types of tasks.

BY THOMAS H. DAVENPORT AND RAJEEV RONANKI

4. AI Doesn’t Have to Be Too Complicated or Expensive for Your Business

Focus on data quality, not quantity.

BY ANDREW NG

SECTION TWO

Building Your AI Team

5. How AI Fits into Your Data Science Team

Get over the cultural hurdles and avoid exaggerated claims.

AN INTERVIEW WITH HILARY MASON BY WALTER FRICK

6. Ramp Up Your Team’s Predictive Analytics Skills

Three pitfalls they need to avoid.

BY ERIC SIEGEL

7. Assembling Your AI Operations Team

A top-notch model is no good if your people can’t connect it to your existing systems.

BY TERENCE TSE, MARK ESPOSITO, TAKAAKI MIZUNO, AND DANNY GOH

SECTION THREE

Picking the Right Projects

8. How to Spot a Machine Learning Opportunity

What do you want to predict, and do you have the data?

BY KATHRYN HUME

9. A Simple Tool to Start Making Decisions with the Help of AI

Use the AI Canvas.

BY AJAY AGRAWAL, JOSHUA GANS, AND AVI GOLDFARB

10. How to Pick the Right Automation Project

Invest in the ones that will build your organization’s capabilities.

BY BHASKAR GHOSH, RAJENDRA PRASAD, AND GAYATHRI PALLAIL

SECTION FOUR

Working with AI

11. Collaborative Intelligence: Humans and AI Are Joining Forces

They’re enhancing each other’s strengths.

BY H. JAMES WILSON AND PAUL DAUGHERTY

12. How to Get Employees to Embrace AI

The sooner resisters get onboard, the sooner you will see results.

BY BRAD POWER

13. A Better Way to Onboard AI

Understand it as a tool to assist people rather than replace them.

BY BORIS BABIC, DANIEL L. CHEN, THEODOROS EVGENIOU, AND ANNE-LAURE FAYARD

14. Managing AI Decision-Making Tools

A framework to determine when and how humans need to stay involved.

BY MICHAEL ROSS AND JAMES TAYLOR

15. Your Company’s Algorithms Will Go Wrong. Have a Plan in Place.

An AI designed to do X will eventually fail to do X.

BY ROMAN V. YAMPOLSKIY

SECTION FIVE

Managing Ethics and Bias

16. A Practical Guide to Ethical AI

AI doesn’t just scale solutions—it also scales risk.

BY REID BLACKMAN

17. AI Can Help Address Inequity—If Companies Earn Users’ Trust

A case from Airbnb shows how good algorithms can have negative effects.

BY SHUNYUAN ZHANG, KANNAN SRINIVASAN, PARAM VIR SINGH, AND NITIN MEHTA

18. Take Action to Mitigate Ethical Risks

It starts with three critical conversations.

BY REID BLACKMAN AND BEENA AMMANATH

SECTION SIX

Taking the Next Steps with AI and Machine Learning

19. How No-Code Platforms Can Bring AI to Small and Midsize Businesses

Three features to look for as you consider the right tool for your company.

BY JONATHON REILLY

20. The Power of Natural Language Processing

NLP can help companies with brainstorming, summarizing, and researching.

BY ROSS GRUETZEMACHER

21. Reinforcement Learning Is Ready for Business

Learning through trial and error can lead to more creative solutions.

BY KATHRYN HUME AND MATTHEW E. TAYLOR

EPILOGUE

Scaling AI

22. How to Scale AI in Your Organization

Invest in processes, people, and tools.

BY MANASI VARTAK

Appendix: Case Study: Will a Bank’s New

Technology Help or Hurt Morale?

Weighing the benefits of AI against the downsides of impersonal decision-making.

BY LEONARD A. SCHLESINGER

Glossary of Key AI Terms

Index