HBR Guide to AI Basics for Managers

  • 5h 26s
  • Harvard Business Review
  • Gildan Media
  • 2023

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.

About the Author

Harvard Business Review is the leading destination for smart management thinking. Through its flagship magazine, 12 international licensed editions, books from Harvard Business Review Press, and digital content and tools published on HBR.org, Harvard Business Review provides professionals around the world with rigorous insights and best practices to lead themselves and their organizations more effectively and to make a positive impact.

In this Audiobook

  • Introduction: How Al Will Redefine Management BY VEGARD KOLBJORNSRUD, RICHARD AMICO, AND ROBERT J. THOMAS
  • Chapter 1 - Three Questions About Al That Every Employee Should Be Able to Answer BY EMMA MARTINO-TRUSWELL
  • Chapter 2 - What Every Manager Should Know About Machine Learning BY MIKE YEOMANS
  • Chapter 3 - The Three Types of Al BY THOMAS H. DAVENPORT AND RAJEEV ROMANKI
  • Chapter 4 - Al Doesn't Have to Be Too Complicated or Expensive for Your Business BY ANDREW NG
  • Chapter 5 - How Al Fits into Your Data Science Team AN INTERVIEW WITH HILLARY MASON BY WALTER FRICK
  • Chapter 6 - Ramp Up Your Team's Predictive Analytics Skills BY ERIC SIEGEL
  • Chapter 7 - Assembling Your Al Operations Team BY TERENCE TSE, MARK ESPOSITO, TAKAAKI MIZUNO, AND DANNY GOH
  • Chapter 8 - How to Spot a Machine Learning Opportunity BY KATHRYN HUME
  • Chapter 9 - A Simple Tool to Start Making Decisions with the Help of AI BY AJAY AGRAWAL, JOSHUA GANS, AND AVI GOLDFARB
  • Chapter 10 - How to Pick the Right Automation Project BY BHASKAR GHOSH, RAJENDRA PRASAD, AND GAYATHRI PALLAIL
  • Chapter 11 - Collaborative Intelligence: Humans and Al Are Joining Forces BY H. JAMES WILSON AND PAUL DAUGHERTY
  • Chapter 12 - How to Get Employees to Embrace Al BY BRAD POWER
  • Chapter 14 - Managing Al Decision-Making Tools BY MICHAEL ROSS AND JAMES TAYLOR
  • Chapter 15 - Your Company's Algorithms Will Go Wrong. Have a Plan in Place. BY ROMAN V. YAMPOLSKIY
  • Chapter 16 - A Practical Guide to Ethical Al BY REID BLACKMAN
  • Chapter 17 - Al Can Help Address Inequity-If Companies Earn Users' Trust BY SHUNYUAN ZHANG, KANNAN SRINIVASAN, PARAM VIR SINGH, AND NITIN MEHTA
  • Chapter 18 - Take Action to Mitigate Ethical Risks BY REID BLACKMAN AND BEENA AMMANATH
  • Chapter 19 - How No-Code Platforms Can Bring Al to Small and Midsize Businesses BY JONATHON REILLY
  • Chapter 20 - The Power of Natural Language Processing BY ROSS GRUETZEMACHER
  • Chapter 21 - Reinforcement Learning Is Ready for Business BY KATHRYN HUME AND MATTHEW E. TAYLOR
  • Chapter 22 - How to Scale Al in Your Organization BY MANASI VARTAK