A Human-in-Control Model: Taking the Helm in Navigating AI Delegation

The growing integration of artificial intelligence (AI) into organizational workflows is fundamentally reshaping work and how individuals delegate tasks. This paper examines how workers strive to preserve their personal control through cognitive and behavioral processes shaped by contextual congruence, a concept we introduce to capture the alignment between workers’ preference 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 the experience of AI delegation. The model identifies four adaptation pathways through which workers negotiate how direct task control is distributed between them and the 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 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 the control tensions in human-AI work.