AI is ready for business. Are you ready for AI?
From financial modeling and product design to performance management and hiring decisions, AI and machine learning are becoming everyday tools for managers at businesses of all sizes. But AI systems come with benefits and downsides—and if you can't make sense of them, you're not going to make the right decisions.
Whether you need to get up to speed quickly or need a refresher, or you're working with an AI expert for the first time, the HBR Guide to AI Basics for Managers will give you the information and skills you need to succeed.
You'll learn how to
Understand key AI terms and concepts
Recognize which of your projects would benefit from AI
Work more effectively with your data team
Hire the right AI vendors and consultants
Deal with ethical risks before they arise
Scale AI across your organization
Arm yourself with the advice you need to succeed on the job, with the most trusted brand in business. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges.
Author(s): Harvard Business Review
Series: HBR Guide to AI Basics for Manager
Publisher: Harvard Business Review Press
Year: 2023
Language: English
Commentary: Manages Guide Put Your Data To Work Drive Digital Transformation
Pages: 272
Tags: Manages Guide 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
