The article examines hybrid managerial decision-making models that integrate artificial intelligence (AI) tools with human expert judgment in environments characterized by high uncertainty and complex, multi-factor decision contexts. The study outlines the key advantages of AI-enabled analytics (large-scale data processing, forecasting, scenario modeling) and the major limitations that prevent fully automated decision-making in management, including data quality issues, model opacity, bias risks, accountability distribution, and organizational resistance. The paper proposes a staged implementation framework for hybrid decision-support systems, emphasizing the design of human–AI interaction, explainability mechanisms, governance and responsibility allocation, and continuous monitoring with feedback loops. It concludes that hybrid architectures represent a practical and sustainable pathway for organizations seeking to improve decision quality and resilience under uncertainty while maintaining strategic responsibility and contextual interpretation by decision-makers.
artificial intelligence, managerial decision-making, hybrid models, expert judgment, uncertainty, decision support systems, explainable AI, governance
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