APPLICATION OF MACHINE LEARNING METHODS IN ORGANIZATIONAL DECISION SUPPORT SYSTEMS

Abstract

Under the conditions of accelerated digital transformation and exponential growth of unstructured data, classical management methods fail to provide the required speed and accuracy of business decisions. This article investigates the integration of Machine Learning (ML) algorithms into organizational Decision Support Systems (DSS). The study reveals a fundamental contradiction between the exponential increase in operational variables and the limited cognitive capacity of human decision-makers, leading to risks of subjectivity and operational lag. The objective of the research is to systematize methodological approaches to deploying ML models in DSS, evaluate their economic and operational efficiency, and identify implementation barriers. Based on a comprehensive analysis, a classification of ML methods by management task segments is proposed, alongside a quantitative risk assessment and operational cost reduction analysis. Practical case studies highlighting hybrid ML architectures in retail, finance, and industrial sectors are formulated. The practical value of the study lies in actionable guidelines for corporate executives to overcome technological and organizational hurdles during the digitalization of decision-making processes.