AI Adoption and System-Wide Change

19 Pages Posted: 12 May 2021 Last revised: 8 Mar 2023

See all articles by Ajay K. Agrawal

Ajay K. Agrawal

University of Toronto - Rotman School of Management; National Bureau of Economic Research (NBER)

Joshua S. Gans

University of Toronto - Rotman School of Management; NBER

Avi Goldfarb

University of Toronto - Rotman School of Management

Multiple version iconThere are 2 versions of this paper

Date Written: November 9, 2022

Abstract

Analyses of AI adoption focus on its adoption at the individual task level. What has received significantly less attention is how AI adoption is shaped by the fact that organizations are composed of many interacting tasks. AI adoption may, therefore, require system-wide change which is both a constraint and an opportunity. We provide the first formal analysis where multiple tasks may be part of a loosely or strongly coupled system. We find that reliance on AI, a prediction tool, increases decision variation which, in turn, raises challenges if decisions across the organization interact. Loose coupling by reducing inter-dependencies between decisions softens that impact and can facilitate AI adoption. However, it does this at the expense of synergies. By contrast, when there are mechanisms for inter-decision coordination, AI adoption is enhanced in a tightly coupled environment. Consequently, we show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organizational system.

Keywords: artificial intelligence, machine learning, prediction, systems

JEL Classification: O32, L23

Suggested Citation

Agrawal, Ajay K. and Gans, Joshua S. and Goldfarb, Avi, AI Adoption and System-Wide Change (November 9, 2022). Available at SSRN: https://ssrn.com/abstract=3843684 or http://dx.doi.org/10.2139/ssrn.3843684

Ajay K. Agrawal

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
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Joshua S. Gans (Contact Author)

University of Toronto - Rotman School of Management ( email )

Canada

HOME PAGE: http://www.joshuagans.com

NBER ( email )

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Cambridge, MA 02138
United States

Avi Goldfarb

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada
416-946-8604 (Phone)
416-978-5433 (Fax)

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