And how to catch up if you’re lagging behind
From the Magazine (September–October 2020) · Long read
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.
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Idea in Brief
The Challenge
As more companies deploy machine learning for AI-enabled products and services, they face the challenge of carving out a defensible market position, especially if they are latecomers.
How to Get Ahead
The most successful AI users capture a good pool of training data early and then exploit feedback data to open up a value gap—in terms of prediction quality—between themselves and later movers.
How to Catch Up
Latecomers can still secure a foothold if they can find sources of superior training data or feedback data, or if they tailor their predictions to a specific niche.
The past decade has brought tremendous advances in an exciting dimension of artificial intelligence—machine learning. This technique for taking data inputs and turning them into predictions has enabled tech giants such as Amazon, Apple, Facebook, and Google to dramatically improve their products. It has also spurred start-ups to launch new products and platforms, sometimes even in competition with Big Tech.
A version of this article appeared in the September–October 2020 issue of Harvard Business Review.
Read more on Strategy or related topics Joint ventures, Technology and analytics and Information technology and telecom sector
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).
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Read more on Strategy or related topics Joint ventures, Technology and analytics and Information technology and telecom sector