Title: Innovations and Discoveries in the Multidisciplinary Research
Chief Editor: Dr. Padmavathi S. M.
Associate Editor: Dr. Poonam Sachin Kadlag
Co-Editor: Dr. Shailaja A Akkur
ISBN: 978-81-69857-36-9
Chapter: 2
DOI: https://doi.org/10.59646/785/2
Authors: Dr. V. A. Jane, Dr. K. Loura Jency, Dr. S. Sathyapriya, Dr. J. Antony John Prabhu, and Dr. S. Lakshmanan
Abstract
Artificial Intelligence (AI) has become a transformative force in the development of Decision Support Systems (DSS), enabling organizations to make faster, more accurate, and data-driven decisions. By integrating machine learning, deep learning, natural language processing, and predictive analytics, AI-powered DSS can process large volumes of structured and unstructured data, identify hidden patterns, and generate actionable insights across diverse sectors such as healthcare, finance, education, manufacturing, agriculture, and public administration. Unlike traditional decision support systems that rely primarily on predefined rules and historical data, AI-enhanced systems continuously learn from new information, adapt to changing environments, and improve the quality of recommendations over time.
This review explores the major AI models used in decision support systems, examines their practical applications across different industries, and discusses the opportunities they offer for improving operational efficiency, strategic planning, and risk management. The paper also highlights important challenges associated with AI-driven decision-making, including concerns related to data quality, algorithmic bias, lack of transparency, privacy and security issues, ethical considerations, and the difficulty of interpreting complex AI models. Furthermore, it emphasizes the importance of human oversight, responsible AI governance, and explainable AI techniques to ensure that automated recommendations remain reliable, fair, and accountable.
Keywords: Artificial Intelligence, Decision Support Systems, Machine Learning, Deep Learning, Predictive Analytics, Explainable AI, Data-Driven Decision Making, Human-AI Collaboration, Ethical AI, Intelligent Systems.