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When AI works, employees worry: the vigilance cost of AI delegation in hybrid work.

Created on 09 Oct 2026

Authors

Yifan Zhang, Tianyou Li, Chunming Chen

Published in

Frontiers in psychology. Volume 17. Pages 1914119. Epub Sep 24, 2026.

Abstract

Hybrid work and AI-assisted technologies have transformed not only where employees work but also how work remains psychologically active after formal working hours. This study develops the AI delegation paradox, which proposes that although AI can reduce the effort required to execute work tasks, it may simultaneously increase employees' need to monitor, evaluate, and remain accountable for work processes that continue through AI-assisted systems. Building on this argument, the study introduces AI process vigilance (AIPV), a second-order construct capturing sustained cognitive attachment to delegated AI-assisted work through progress monitoring, output quality concern, error anticipation, and loss of control. Using a two-wave survey design with a seven-week interval, data were collected from 642 hybrid employees who used AI tools for work at least weekly. The results supported a sequential pathway in which continuous AI availability was linked to greater AI delegation dependence, delegation dependence predicted higher levels of AI process vigilance, vigilance increased work-related rumination, rumination undermined psychological detachment, and lower detachment was associated with poorer recovery outcomes. The hypothesized moderating role of boundary management preference was not supported. These findings suggest that recovery impairment may arise not only because employees continue working after hours, but also because they remain psychologically engaged with work processes that have been delegated to AI. By demonstrating that responsibility and concern often persist even when task execution is transferred to intelligent systems, the study identifies a previously overlooked recovery cost of AI-assisted work and extends current understanding of recovery, boundary management, and human-AI collaboration in contemporary work environments.

PMID:
42851350
Bibliographic data and abstract were imported from PubMed on 09 Oct 2026.

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