AI-Driven Decision Support Systems: Enhancing Organizational Decision-Making Through Intelligent Analytics

In large and complex information environments‚ with time constraints and far-ranging actions to be taken‚ customary models based on the so-called hindsight-based decision model are not sufficient whenever predictive knowledge and rapid reactions are needed․ We develop the concept of AI decision support systems as cognitively extending technologies that complement human reasoning while preserving human judgment․ Using conceptual analysis and a review of the literature‚ the article represents the building blocks of a decision support system‚ such as the data integration architecture‚ machine learning models‚ reasoning mechanisms‚ and the human-friendly interface․ The paper also investigates the critical interactions between the technical, organizational, and human dimensions. Findings highlight the need for a balance of automated and human oversight through adaptive learning‚ explainability‚ and the calibration of trust․ Barriers to implementing machine learning were found to be data quality, organizational preparedness, and bias. Finding the proper balance of machine and human oversight improves decision consistency, cognitive load, and organizational adaptability. The end result is the integration of research in artificial intelligence, organizational behavior, and systems design into a framework for designing, developing, and evaluating smart decision support architectures in complex organizations.