Harvard Business Review - HBR Guide to AI Basics for Managers: Build New Capabilities Put Your Data To Work Drive Digital Transformation

Artificial Intelligence (AI) is having a transformational impact on business. From product design and financial modeling to performance management and marketing spending, AI and machine learning are becoming everyday tools for managers in organizations of all sizes. You cant just leave AI to the experts anymore. If you dont understand the technology at a foundational level, youre not going to be informed enough to make smart decisions that will affect your bottom line. And if you dont realize how AI will change jobsincluding the work that managers and leaders doyou may make a major misstep in your career.

Whether you want to get up to speed quickly, could use a refresher, or are working with an AI expert for the first time, the HBR Guide to AI Basics for Managers will provide you with the information and skills you need. With practical, applicable advice and plain-language takeaways in each chapter, the book will help you take the first steps toward embracing AI and transforming your business.

Youll learn how to:

• Understand key terms and concepts, including machine learning, training data, and natural language processing
• See how automation will change jobs, including your own
• Identify the right projects and processes for applying AI tools
• Help your employees learn the essentials of AI
• Deal with ethical issues and biased results before they come up
• Select quality AI consultants and vendors and work with them effectively
• Build an AI team that fits your most pressing needs
• Communicate better with your machine learning experts and data scientists
• Make a plan for when algorithms make (inevitable) mistakes
• Scale AI across your organization

Five practices that successful managers need to master.

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

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

BY EMMA MARTINHO-TRUSWELL

A nontechnical primer.

BY MIKE YEOMANS

First, understand which technologies perform which types of tasks.

BY THOMAS H. DAVENPORT AND RAJEEV RONANKI

Focus on data quality, not quantity.

BY ANDREW NG

Get over the cultural hurdles and avoid exaggerated claims.

AN INTERVIEW WITH HILARY MASON BY WALTER FRICK

Three pitfalls they need to avoid.

BY ERIC SIEGEL

A top-notch model is no good if your people cant connect it to your existing systems.

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

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

BY KATHRYN HUME