AI Implementation at Work: Worker Preferences for Task Changes

Lara Fleck (), Marie-Christine Fregin, Andries de Grip () and Sanne Steens
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Lara Fleck: ROA, Maastricht University
Marie-Christine Fregin: ROA, Maastricht University
Andries de Grip: ROA, Maastricht University
Sanne Steens: ROA, Maastricht University

No 19076, IZA Discussion Papers from IZA Network @ LISER

Abstract: This paper examines worker preferences for AI-enabled changes in task composition and possibly facilitating job characteristics in a discrete choice experiment among workers in a multinational manufacturing firm. The firm implements an AI tool transforming workers’ core task of classifying electronic components in two successive stages: (1) augmenting AI including a decision support feature and (2) autonomous AI automating easy classification tasks. Workers have to choose between jobs that differ in five characteristics: the AI-enabled change in their classification task, breaks, autonomy, training, and a salary increase. The results show that workers prefer the changes induced by augmenting AI over autonomous AI. Workers would forgo a moderate salary increase when their core task is supported by the augmenting AI. Moreover, the perceived usefulness of the augmenting AI negatively moderates the preference for autonomous AI. These results suggest that human-AI complementarity is favorable while simultaneously posing disadvantages for stepwise implementation strategies.

Keywords: Artificial intelligence (AI); task composition; augmentation; automation; perceived usefulness of technology; willingness to pay; discrete-choice experiment (search for similar items in EconPapers)
JEL-codes: J24 J81 L60 M50 O33 (search for similar items in EconPapers)
Date: 2026-11
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