From Prediction to Transformation

To realize their potential, AI technologies need new systems that leverage them.

by

Ajay Agrawal,
Joshua Gans,

and

Avi Goldfarb

by

Ajay Agrawal,
Joshua Gans,

and

Avi Goldfarb

From the Magazine (November–December 2022) · Long read

Ralf Jacobs

Summary.

While the popular view is that insights are the key benefit of artificial intelligence, in truth AI creates value by improving the quality of decisions. The good news is, the opportunities for it to do that in business are countless. But because decisions in one area of an organization usually have an impact on decisions in other areas, introducing AI often entails redesigning whole systems. In that way, AI is similar to groundbreaking technologies of the past, like electricity, which initially was used only narrowly but ultimately transformed manufacturing.

Decisions involve a combination of prediction and judgment, and because AI makes highly accurate predictions, it will shift decision rights to where judgment is still needed, potentially changing who makes decisions and where, when, and how. More-accurate predictions in one part of a value chain will also have ripple effects on other parts. For instance, if a restaurant can reliably forecast the amount of ingredients it needs each week, its orders will fluctuate, making its suppliers’ sales more uncertain. Strong communication is needed to synchronize effort and resources in a system, and modularity will help prevent changes in one area from disrupting others.

Idea in Brief

The Problem

AI predictions often improve the quality of specific decisions but can have a disruptive effect on overall systems of decision-making.

Why It Happens

Decisions are usually a combination of prediction and judgment. When predictions are extremely accurate, as can be the case with AI, decision rights will flow to where judgment is still needed, potentially changing who makes decisions and where, when, and how. And more-accurate predictions in one part of a system will have ripple effects on other parts.

The Solution

In the absence of perfect, instant communication, organizations and whole value chains need to create systems that balance modularity in design with coordination. Modularity enables many decisions to be made on the basis of locally observable factors, while coordination enables decisions to be optimized over a wider network.

Investor: “What will your artificial intelligence do for businesses?”

A version of this article appeared in the November–December 2022 issue of Harvard Business Review.

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).

From Prediction to Transformation

Idea in Brief

The Problem

Why It Happens

The Solution

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