Artificial Intelligence-Driven Decision Support Systems for Strategic Management: A Machine Learning Framework for Organizational Performance

Abstract

Artificial Intelligence (AI) driven Decision Support Systems (DSS) help transform the field of strategic management through rapid, accurate and data-driven decision-making in increasingly complicated business environments. This article is centered on a comprehensive machine learning approach for AI-enabled strategic decision support involving all aspects of data acquisition, predictive analytics models, intelligent recommendation systems and collaboration between humans and AI. The framework suggested consists in the integration of data coming from the enterprise, customer relationship management systems, financial databases and market intelligence services and results obtained through machine learning technologies. The process of decision-making involves both the utilization of recommendation systems to develop possible strategies based on analytical results and human involvement ensuring that the outcome is ethical, clear and considerate. The research shows that AI-enabled DSS helps to enhance decision-making quality, operational efficiency, competitive edge, innovation capabilities, and organizational agility as a result of continuous learning. In addition, the paper argues that explainable AI, data governance, organizational preparedness, and responsible AI governance are important factors for successful implementation. Despite numerous advantages available, there are some challenges connected with data quality, transparency of algorithms, data confidentiality, safety, and managerial acceptance that should also be taken into account. The conceptual framework proposed emphasizes a focus on effective cooperation of sophisticated AI technologies with rational judgments that assist decision-makers while staying accountable and trustworthy. The results imply that AI-enabled decision support systems can be essential for businesses striving to adopt digitalization, ensure sustainability, and maintain long-term competitiveness in the context of intelligent organizations. Further studies should investigate target industries, Explainable AI (XAI), ethical frameworks of governance, and the integration of new technologies such as Digital Twins, Generative AI, and autonomous decision-making systems.