Artificial Intelligence in Team Dynamics: Who Gets Replaced and Why?

TL;DR

This paper develops a model to analyze optimal AI deployment in team settings, revealing strategic replacement patterns, effects on wages, and the potential benefits of underutilizing AI resources.

Contribution

It introduces a sequential team production model with peer monitoring to determine optimal AI replacement strategies and their impact on wages and inequality.

Findings

Optimal AI replacement is stochastic and position-dependent.

AI is most beneficial at the beginning and end of workflows.

Underutilizing AI can be optimal, improving wage outcomes.

Abstract

This study investigates the effects of artificial intelligence (AI) adoption in organizations. We ask: First, how should a principal optimally deploy limited AI resources to replace workers in a team? Second, in a sequential workflow, which workers face the highest risk of AI replacement? Third, would the principal always prefer to fully utilize all available AI resources, or are there any benefits to keeping some slack AI capacity? Fourth, what are the effects of optimal AI deployment on the wage level and intra-team wage inequality? We develop a sequential team production model in which a principal can use peer monitoring--where each worker observes the effort of their predecessor--to discipline team members. The principal may replace some workers with AI agents, whose actions are not subject to moral hazard. Our analysis yields four key results. First, the optimal AI strategy stochastically replaces workers rather than fixating on a single position. Second, the principal replaces workers at the beginning and at the end of the workflow, but does not replace the middle worker, since this worker is crucial for sustaining the flow of information obtained by peer monitoring. Third, the principal may optimally underutilize available AI capacity. Fourth, the optimal AI adoption increases average wages and reduces intra-team wage inequality.