How to Win with Machine Learning (2024)

Summary.

Many companies can dramatically improve their products and services by using machine learning—an application of artificial intelligence that involves generating predictions from data inputs. Amazon, Google, and other tech giants are already experts at taking advantage of this technology. Smaller enterprises and late entrants, however, may be unsure how to do likewise to gain market share for themselves.

This article suggests that early movers will be successful if they have enough training data to make accurate predictions and if they can improve their algorithms by quickly incorporating feedback derived from customers’ behavior. Latecomers will need a different approach to be competitive: The secret for them is to find untapped sources of training or feedback data, or to differentiate themselves by tailoring predictions to a special niche.

• Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management. He is the founder of the Creative Destruction Lab, founder of Metaverse Mind Lab, co-founder of NEXT Canada, and co-founder of Sanctuary. He is also a co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence (Harvard Business Review Press, 2022).

• Joshua Gans is the Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto, and the chief economist at the Creative Destruction Lab. He is a co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence (Harvard Business Review Press, 2022).

• Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School of Management, University of Toronto. He is also the chief data scientist at the Creative Destruction Lab and a co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence (Harvard Business Review Press, 2022).