Abstract

The growing integration of Artificial Intelligence (AI) into organizational workflows is fundamentally reshaping work and how individual delegate tasks. This paper examines how workers strive to preserve their personal control through cognitive and behavioral processes shaped by contextual congruence between their preferences for AI delegation and their work environment. We develop the Human-in-Control (HiC) theoretical model, which conceptualizes personal control as a dynamic process rooted in workers’ experience of AI delegation. The model identifies four adaptation pathways through which workers negotiate how direct task control is distributed between themselves and AI systems. Each pathway is characterized by a distinctive configuration of primary and secondary control processes that workers mobilize to maintain, restore, or enhance their personal control. This research contributes to IS scholarship by offering a comprehensive, integrated framework that explains how workers reconfigure their sense of personal control in contexts that encourage or limit AI delegation. It further extends adaptation theory beyond scenarios where technology is integrated into work, addressing conditions where AI delegation is constrained against workers’ preferences, thereby creating distinct forms of disruption through deprivation. Through these contributions, the HiC model provides researchers with a novel perspective for studying delegation to AI, bridging personal control theory with existing adaptation frameworks by addressing critical gaps in our understanding of human-AI work and the control tensions shaping it.

DOI

10.17705/1jais.00971

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