AI-Driven Decision Support Systems for Business Transformation

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Prof. Mireille Duvant

Abstract

Artificial Intelligence (AI)-driven Decision Support Systems (DSS) have emerged as a transformative force in modern organizations, enabling data-driven decision-making, operational efficiency, and strategic business innovation. By integrating advanced technologies such as machine learning, deep learning, natural language processing, predictive analytics, and big data, AI-powered DSS provide organizations with the capability to analyze complex datasets, identify hidden patterns, forecast future trends, and generate actionable insights in real time. These intelligent systems support decision-makers across various functional areas, including finance, marketing, supply chain management, human resource management, healthcare, and manufacturing, thereby enhancing organizational agility and competitiveness. the role of AI-driven Decision Support Systems in facilitating business transformation by examining their technological foundations, practical applications, implementation strategies, and organizational benefits. It further discusses key challenges associated with AI adoption, including data quality, cybersecurity, ethical concerns, algorithmic bias, privacy protection, regulatory compliance, and workforce readiness emerging developments such as explainable AI, generative AI, cloud-based decision platforms, edge intelligence, and autonomous decision-making systems that are reshaping the future of business intelligence. Based on an extensive review of recent literature, AI-driven Decision Support Systems are becoming indispensable for organizations seeking to improve decision accuracy, optimize resource utilization, enhance customer experiences, and achieve sustainable competitive advantage in an increasingly digital business environment.

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Original Research Articles

How to Cite

Prof. Mireille Duvant. (2026). AI-Driven Decision Support Systems for Business Transformation. International Insurance Law Review, 34(2), 27-34. https://doi.org/10.65677/

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