AI Adoption and System-Wide Change

By Ajay Agrawal, Joshua S. Gans, Avi Goldfarb. 2021

38 citations recorded by the source corpus.

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.

Subfields: AI Ethics, Fairness & Society, Human-Computer Interaction

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Citations in the corpus

• GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “Consequently, many studies of machine learning technologies focus on systems-level adoption, arguing that organizational systems may require redesign to effectively take advantage of novel machine learning advancements (Bresnahan, 2019; Agrawal et al., 2021; Goldfarb et al., 2023).”
• Organizational Frictions and Increasing Returns to Automation: Lessons from AT&T in the Twentieth Century (2021, unknown): “Agrawal et al. (2021) argue that artificial intelligence will, in many cases, require “system-wide change”, particularly in non-modular environments.”