AI and Fuzzy Multi-Criteria Decision-Making for Organizational Performance and Strategic Management

Abstract

Multi-Criteria Decision-Making (MCDM) has become an indispensable analytical approach for supporting organizational performance evaluation and strategic management in increasingly complex and uncertain business environments. Modern organizations operate under multiple, often conflicting objectives involving financial performance, operational efficiency, sustainability, innovation, stakeholder satisfaction, risk mitigation, and competitive positioning. Conventional single-criterion evaluation techniques frequently fail to capture these multidimensional relationships, necessitating structured decision-making frameworks capable of integrating qualitative and quantitative information. MCDM methodologies enable systematic prioritization of strategic alternatives by incorporating diverse evaluation criteria, expert judgments, uncertainty considerations, and organizational objectives into a unified decision-support process. Recent developments integrating hybrid optimization techniques, fuzzy environments, and data-driven analytics have significantly enhanced decision transparency, robustness, and adaptability across industries. This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes. The study further proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness. The findings are expected to provide valuable theoretical insights and practical guidance for researchers, policymakers, and organizational leaders seeking evidence-based strategic management solutions.