AI Adoption and System-Wide Change

Ajay Agrawal, Joshua Gans and Avi Goldfarb

No 28811, NBER Working Papers from National Bureau of Economic Research, Inc

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 organisations 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 modular or non-modular system. We find that reliance on AI, a prediction tool, increases decision variation which, in turn, raises challenges if decisions across the organisation interact. Modularity, which leads to task independence rather than system-level inter-dependencies, softens that impact. Thus, modularity 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 when there is a non-modular 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 organisational system.

JEL-codes: M1 O32 O33 (search for similar items in EconPapers)
Date: 2021-05
Note: PR
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Citations: View citations in EconPapers (5)

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