AI Adoption and System-Wide Change / Ajay K. Agrawal, Joshua S. Gans, Avi Goldfarb.

Format:

Book

Author/Creator:

Agrawal, Ajay K.

Contributor:

National Bureau of Economic Research.

Gans, Joshua S.

Goldfarb, Avi.

Series:

Working Paper Series (National Bureau of Economic Research) no. w28811.

NBER working paper series no. w28811

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2021.

Summary:

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.

Notes:

Print version record

May 2021.