From challenges to opportunities: navigating the human response to automated agents in the workplace

Ivan Ðula (), Tabea Berberena (), Ksenia Keplinger () and Maria Wirzberger ()
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Ivan Ðula: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”
Tabea Berberena: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”
Ksenia Keplinger: Max Planck Institute for Intelligent Systems
Maria Wirzberger: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”

Palgrave Communications, 2024, vol. 11, issue 1, 1-15

Abstract: Abstract Workers are increasingly embracing Artificial Intelligence (AI) to optimise various aspects of their operations in the workplace. While AI offers new opportunities, it also presents unintended challenges that they must carefully navigate. This paper aims to develop a deeper understanding of workers’ experiences with interactions with automated agents (AA) in the workplace and provide actionable recommendations for organisational leaders to achieve positive outcomes. We propose and test a simulation model that quantifies and predicts workers’ experiences with AA, shedding light on the interplay of diverse variables, such as workload, effort and trust. Our findings suggest that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates. Additionally, we find that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation. This interdisciplinary research blends a systems dynamics approach with management theories and psychological concepts, aiming to bridge existing gaps and foster the sustainable and effective implementation of AA in the workplace. Ultimately, our research endeavour contributes to advancing the field of human-AI interaction in the workplace.

Date: 2024
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DOI: 10.1057/s41599-024-03962-x

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