The Role of Generative AI in Human Managerial Decision-Making: From Information Overload to Information Filtering

The rapid adoption of generative artificial intelligence (GenAI) has intensified debate about the role of artificial intelligence in managerial decision-making. Much of this debate focuses on whether GenAI can generate recommendations, predict outcomes, or participate directly in managerial choice. This article develops an alternative perspective: GenAI may create substantial managerial value when used as an information-filtering mechanism rather than as an autonomous decision-maker. Drawing on research concerning information overload, irrelevant information, decision-support systems, and emerging human-GenAI collaboration, the article examines how generative systems may support the identification, extraction, organization, summarization, and prioritization of decision-relevant information. The synthesis suggests that managerial decision problems often arise not only from information volume but from difficulty distinguishing relevant evidence from contextual noise, redundancy, ambiguity, and low-value detail. At the same time, prior decision-support research shows that technological assistance can introduce new errors, distort attention, and encourage inappropriate reliance. The article therefore proposes a human-centered conceptual framework in which GenAI transforms complex information environments into decision-ready representations while human managers retain responsibility for verification, interpretation, trade-offs, accountability, and final choice. Six propositions are developed concerning information complexity, filtering accuracy, traceability, managerial expertise, algorithmic influence, and decision stakes, followed by managerial implications and a future research agenda for project-based and operational contexts.