How Can Teams Benefit From AI Team Members? Exploring the Effect of Generative AI on Decision-Making Processes and Decision Quality in Team–AI Collaboration

Désirée Zercher, Ekaterina Jussupow, Ivo Benke and Armin Heinzl

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

Abstract: Human teams with distributed knowledge can make high-quality decisions but often fail due to decision-making asymmetries.As AI team members become integrated collaborators, understanding how AI can reduce these decision-making asymmetriesis essential. However, little is known about how AI team members can reduce these asymmetries and whether new AI-specificasymmetries emerge from team–AI collaboration. Building on the information asymmetries model, we conducted an exploratoryexperiment with 215 individuals across 81 teams performing a hidden profile task under three knowledge configurations: (1)human teams with asymmetric knowledge, (2) teams collaborating with AI with centralized knowledge, and (3) teams collab-orating with AI with asymmetric knowledge. Our results show that teams with centralized AI knowledge make more accuratedecisions than human teams due to reduced decision-making asymmetries, trust in AI, beneficial AI information processing,and a balanced AI collaboration focus. In contrast, teams with asymmetric AI knowledge show only moderate reductions indecision-making asymmetries. Moreover, due to emerging AI-specific asymmetries—such as mistrust, nonbeneficial AI infor-mation processing, and a critical AI collaboration focus—these teams fail to outperform human teams. We integrated our find-ings into process models that illustrate how successful team–AI collaboration depends on effective teamwork between humanand AI members.

Date: 2025-05-20
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Published in Journal of Organizational Behavior (2025-05-20) : pp. 1-28

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