How team organization influences the ability to solve automation failures: an experimental study on human–AI decision-making in teams

Abstract

As production environments become increasingly automated and AI-assisted, managing automation failures is a growing challenge. This study examines how team organization—hierarchical versus self-managed—affects team performance in resolving such failures. Using a laboratory experiment simulating a realistic industrial setting, teams operated automated machinery supported by AI-based assistance. We hypothesize that communication mediates the relationship between team organization and performance outcomes (productivity and quality). The results show that self-managed teams communicate more frequently and with higher quality than hierarchical teams, leading to higher productivity and fewer errors. Structural equation modeling confirms that the effect of team organization on performance is fully mediated by communication. These findings highlight the importance of team communication and suggest that revisiting team organization in AI-driven production—by favoring self-management or enhancing communication in hierarchies—may improve performance. The study contributes to human–AI teaming research by integrating organizational design into experimental analysis.