Comparison of Classical and AI-Based Decision-Making Methodologies

The significance of effective decision-making in management is undeniable, impacting all facets of operations and influencing organizational success. This paper delves into the contrast between traditional and artificial intelligence (AI)-based decision-making methodologies, elucidating their distinctive features in management decisions. Classical decision-making methodologies, such as Cost-Benefit Analysis (CBA), have long served as vital tools in management decisions [1]. The CBA provides a systematic approach to determine the value efficiency of decisions, such as loan approvals. Contrastingly, AI-based decision-making models, exemplified by the Random Forest algorithm [2], introduce a novel approach in decision-making tasks. By processing vast data sets, these AI models offer predictive power and accuracy, especially in complex decisions like loan approvals [3]. By applying both the CBA and Random Forest algorithm to loan approval, the paper reveals notable differences. While CBA depends on human interpretation, the Random Forest algorithm harnesses data-driven insights, offering potential improvements in decision-making accuracy and efficiency. Understanding both classical and AI-based methodologies is crucial for managers in today’s data-rich business environment. The paper emphasizes that AI methodologies should be viewed as enhancements, not replacements, of traditional methods. It advocates for further research into hybrid models that integrate traditional methodologies with AI, potentially boosting the effectiveness and accuracy of managerial decision making. A comprehensive understanding of both approaches allows for more informed and efficient decision-making processes.