Authors : Dileesh Chandra Bikkasani

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/5dekjrv7

Scribd : https://tinyurl.com/3j6w8mam

DOI : https://doi.org/10.5281/zenodo.14964501

Abstract : Artificial Intelligence (AI) transforms business intelligence (BI) by enhancing decision-making speed, accuracy, and depth in today’s data-driven landscape. Traditional Decision Support Systems (DSS), once foundational to BI, struggle to handle modern data's complexity, scale, and diversity, often resulting in limited decision-making agility. Integrating AI into DSS has become essential to bridge this gap, enabling these systems to process vast datasets in real-time and make predictive, data-informed recommendations. This study presents an AI-powered DSS framework designed to address the limitations of conventional DSS by incorporating machine learning, natural language processing, and adaptive feedback mechanisms. Through real-world simulations and industry-specific use cases, the framework demonstrates marked improvements in decision quality, response times, and user satisfaction compared to traditional systems. Findings suggest that AI-driven DSS can substantially enhance BI processes, equipping organizations with a proactive, scalable approach to decision support. By addressing key technical and ethical challenges, this research offers valuable insights for businesses aiming to leverage AI to stay competitive in increasingly complex environments, positioning AI-powered DSS as critical to the future of BI.

Keywords : Artificial Intelligence, Business Intelligence, Decision Support Systems, Predictive Analytics, Framework Development.

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