Intelligent Decision Support Systems for Business and Public Policy

Jennifer Chayes

Intelligent Decision Support Systems for Business and Public Policy

Author(s)	Jennifer Chayes
Country	United States
Abstract	Intelligent Decision Support Systems (IDSS) have become essential tools for improving strategic decision-making in business and public policy by integrating Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, Cloud Computing, Internet of Things (IoT), Business Intelligence (BI), Geographic Information Systems (GIS), Digital Twins, and Explainable Artificial Intelligence (XAI). Unlike traditional Decision Support Systems (DSS), IDSS employ intelligent algorithms capable of learning from structured and unstructured data, generating predictive insights, optimizing complex decisions, and supporting real-time evidence-based policymaking. Across sectors such as finance, healthcare, manufacturing, public administration, education, agriculture, transportation, and environmental management, intelligent decision support enhances organizational performance, operational efficiency, risk management, resource allocation, and sustainable development. This study investigates Intelligent Decision Support Systems for business and public policy through a multidisciplinary perspective. A qualitative and analytical research methodology based on secondary data from business management, economics, information systems, public administration, computer science, operations research, engineering, and policy studies is employed to examine IDSS architectures, enabling technologies, application domains, implementation frameworks, governance mechanisms, ethical considerations, and future research opportunities. Particular emphasis is placed on Artificial Intelligence, predictive analytics, knowledge management, Explainable AI, digital governance, business intelligence, and human–AI collaboration. The findings indicate that IDSS significantly improves decision accuracy, strategic planning, operational efficiency, policy effectiveness, organizational agility, and public service delivery by enabling data-driven and evidence-based decisions. Intelligent analytics facilitate forecasting, scenario modelling, optimization, fraud detection, crisis management, and resource planning while strengthening transparency and accountability. However, challenges including data quality, algorithmic bias, cybersecurity risks, interoperability issues, digital skills shortages, privacy concerns, regulatory complexity, and organizational resistance remain major barriers to successful implementation. The study concludes that integrating Artificial Intelligence, explainable analytics, secure digital infrastructure, and interdisciplinary collaboration provides a comprehensive pathway toward resilient, transparent, and sustainable intelligent decision support for business and public policy.
Keywords	Intelligent Decision Support Systems, Artificial Intelligence, Business Intelligence, Public Policy, Predictive Analytics, Explainable AI, Decision-Making, Digital Transformation.
Field	Engineering
Published In	Volume 3, Issue 6, November-December 2021
Published On	2021-12-12

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Decision Support Systems for Business and Public Policy

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