AI-Driven Decision Fatigue and Its Impact on Managerial Judgment in Hybrid Organizations
DOI:
https://doi.org/10.63345/Keywords:
artificial intelligence, decision fatigue, managerial judgment, hybrid work, algorithmic management, cognitive workloadAbstract
Artificial intelligence can help managers sort information, forecast outcomes, and prepare decisions, yet it can also generate a continuous stream of recommendations requiring review. In hybrid organizations, managers must assess those recommendations while coordinating employees across locations and communication channels. This paper examines whether AI-supported workflows may reduce some forms of effort while increasing the cumulative burden of verification, comparison, and accountability. It uses a structured secondary analysis of thirteen academic and institutional publications covering decision fatigue, human–AI judgment, algorithmic management, and hybrid work. Published findings show substantial productivity gains in several AI-assisted tasks, but do not establish equivalent gains in managerial judgment. A meta-analysis of 106 experiments found that human–AI combinations performed worse than the better standalone human or AI system on average. A separate review found evidence consistent with decision fatigue in 45% of quantitatively assessed cases in healthcare decision-making, although definitions and methods varied. The evidence supports a conditional account: AI may ease managerial work when recommendations are relevant and easy to verify, while frequent prompts, uncertain outputs, and fragmented communication may increase judgment demands. No reviewed study directly estimates the causal effect of AI-driven decision fatigue among managers in hybrid organizations. The paper therefore presents a testable framework and identifies the evidence needed to evaluate that relationship.
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