Next-Generation MCDM: How AI is Revolutionizing the Decision Support Process

Abstract

Multi-Criteria Decision Making (MCDM) has long been essential for addressing complex decision problems involving numerous, often conflicting, criteria. However, conventional MCDM still faces limitations related to subjective bias, data uncertainty, and methodological rigidity. This paper explores how cutting-edge Artificial Intelligence (AI) technologies can fundamentally transform the MCDM process. Building on a novel “divide-and-inject” framework, the study systematically decomposes decision problems into modular subproblems and injects tailored AI solutions at each step. This approach enables dynamic formation of criteria, objective weighting, automated alternative generation, and intelligent method selection, while ensuring transparency and scalability through step-local explainability and governance. Case studies from diverse domains such as healthcare, public policy, and education demonstrate the framework's adaptability and practical impact. The findings highlight AI not only as an auxiliary tool, but as an integral, orchestrated agent for building robust, adaptable, and interpretable MCDM systems. The paper concludes with recommendations for future research, including hybrid symbolicdeep learning models, cross-domain validation, and real-time feedback integration to foster a new generation of intelligent, explainable decision support systems.

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