Working Paper: NBER ID: w28811
Authors: Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb
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.
Keywords: AI adoption; organizational modularity; systemwide change
JEL Codes: M1; O32; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI adoption (C45) | decision variation (D80) |
| modularity (F12) | AI adoption (C45) |
| modularity (F12) | decision-making outcomes (D70) |
| AI adoption (C45) | decision-making outcomes (D70) |
| modularity facilitates AI adoption (C45) | reduces inter-task coordination (L23) |
| AI adoption enhanced by inter-decision coordination (C45) | decision-making effectiveness (D70) |