Generative artificial intelligence (GenAI) is increasingly embedded in managerial decision-making, where it can summarize information, generate alternatives, evaluate scenarios, and provide recommendations. Yet the managerial value of GenAI depends not only on the quality of its outputs but also on how humans respond to them. Prior research presents an apparent tension. Studies of algorithm aversion show that decision-makers may reject useful algorithmic advice after observing algorithmic error, whereas research on algorithm appreciation shows that people may sometimes rely more heavily on algorithmic than human advice. More recent work raises the opposite concern: managers may over-rely on GenAI because fluent, confident, or authoritative outputs can appear credible even when they are incomplete, biased, or incorrect. This article integrates research on algorithm aversion, algorithm appreciation, human-AI collaboration, managerial expertise, information processing, and GenAI-enabled decision support to develop a framework of calibrated human reliance. The central argument is that effective human-GenAI decision-making requires neither greater trust nor greater skepticism, but alignment between the degree of human reliance and the reliability and decision context of AI advice. The framework distinguishes four reliance states: under-reliance, appropriate rejection, appropriate reliance, and over-reliance. It further proposes that reliance calibration is influenced by four contextual dimensions: managerial expertise, output verifiability, uncertainty, and decision consequences. The article contributes to the emerging literature by distinguishing trust as an attitude from reliance as decision behavior and by shifting managerial attention from whether GenAI should be trusted to when and how much managers should rely on its outputs. Keywords: generative artificial intelligence; trust in AI; reliance; algorithm aversion; algorithm appreciation; over-reliance; human-AI collaboration; managerial decision-making; calibration
Sergey Kutukoff· Zenodo (CERN European Organi...· 0 citations
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. Keywords: generative artificial intelligence; information overload; information filtering; managerial decision-making; decision support; human-AI collaboration; project management; operations management
Sergey Kutukoff· Zenodo (CERN European Organi...· 0 citations
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. Keywords: generative artificial intelligence; information overload; information filtering; managerial decision-making; decision support; human-AI collaboration; project management; operations management
Sergey Kutukoff· Zenodo (CERN European Organi...· 0 citations
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