<![CDATA[This study investigates the role of artificial intelligence (AI) and machine learning (ML) in enhancing decision-making processes across healthcare, finance, and logistics sectors. Using a mixed-methods approach, data were collected from 150 organizations, analyzing metrics such as decision-making speed, accuracy, error reduction, and user satisfaction. Findings reveal that AI/ML integration significantly improved decision accuracy and speed, with finance demonstrating the highest increases in efficiency. However, sector-specific challenges—such as regulatory constraints in healthcare and data privacy in finance—highlight the need for industry-specific AI/ML implementation strategies. The results support theories of decision-making and technological innovation, indicating that AI/ML can optimize decision quality when balanced with human oversight. Practical implications suggest that industry leaders adopt phased AI/ML integration strategies to address regulatory and operational barriers, while policymakers could establish frameworks that support responsible AI use. Future research could explore AI/ML’s impact on decision-making in smaller organizations or emerging technologies' influence on decision processes.]]>
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