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

Xienan Cheng, Mustafa Dogan, Pinar Yildirim

TL;DR

A sequential team production model is developed in which a principal can use peer monitoring--where each worker observes the effort of their predecessor--to discipline team members and the optimal AI strategy stochastically replaces workers rather than fixating on a single position.

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.

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

TL;DR

A sequential team production model is developed in which a principal can use peer monitoring--where each worker observes the effort of their predecessor--to discipline team members and the optimal AI strategy stochastically replaces workers rather than fixating on a single position.

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.

Paper Structure

This paper contains 43 sections, 19 theorems, 140 equations, 1 table.

Table of Contents

  1. Introduction
  2. Model
  3. AI Replacement
  4. Timing
  5. Discussion of Model Setup
  6. AI does not shirk.
  7. Full vs partial replacement of worker tasks.
  8. Limited capacity for AI replacement.
  9. Imperfect Observability.
  10. Analysis
  11. Optimal Compensation for a Fixed Replacement Strategy.
  12. Tradeoffs of Replacement.
  13. Optimal Replacement Strategy
  14. Discussion of Randomization.
  15. Heterogeneous Replacement Risk Based on Worker Position.
  16. ...and 28 more sections

Key Result

Proposition 1

Fix a replacement strategy $x = (x_1, \ldots, x_n)$. The optimal compensation scheme conditional on $x$, denoted by $w^x = (w_1^x, \ldots, w_n^x)$, satisfies the following for each worker $i \in \{1, \ldots, n\}$ with $x_i<1$: where $\zeta_i^x$ is the resulting success rate when worker $i$ shirks:

Theorems & Definitions (43)

  • Definition 1
  • Proposition 1
  • Proposition 2
  • Proposition 3
  • Proposition 4
  • Proposition 5
  • Corollary 1
  • Proposition 6
  • Proposition 7
  • Proposition 8
  • ...and 33 more