Who’s Doing the Thinking Now? Reflexivity and Oversight in GenAI-Mediated Decision-Making

Generative AI (GenAI) is increasingly embedded in organizational decision-making, reshaping how judgment, oversight, and productivity are enacted in everyday work. While prior research documents performance gains from AI assistance, less is known about how sustained GenAI use affects human reflexivity, i.e., the capacity for critical evaluation, deliberation, and decision override in AI-mediated tasks. This study advances Algorithmic Reflexivity Reallocation Theory (ALERT), which theorizes how repeated algorithmic mediation reallocates cognitive effort away from human judgment through cognitive offloading and automation bias, producing a trade-off between productivity and reflexive oversight. We test ALERT using a three-stage mixed-methods design comprising qualitative grounding interviews, a controlled behavioral experiment, and a large-scale survey-based structural equation model. Behavioral results show that GenAI assistance increases task efficiency but reduces override behavior, particularly when recommendations are plausible yet incorrect. Structural modeling further demonstrates that human–AI trust, task familiarity, and algorithmic structuration predict cognitive offloading, which in turn suppresses override behavior and weakens individual reflexivity while increasing perceived productivity. Transparency emerges as a key design condition that mitigates cognitive offloading and preserves evaluative engagement. By theorizing reflexivity as a reallocatable cognitive and organizational resource, ALERT extends structuration and human–AI interaction research by explaining how productivity gains can coexist with the erosion of reflective judgment under sustained GenAI use. The study contributes a process theory of AI-mediated decision-making with implications for the design and governance of AI-supported work systems.