AI-ENABLED DECISION SUPPORT SYSTEMS FOR SMARTER INFRASTRUCTURE PROJECT MANAGEMENT IN PUBLIC WORKS

This paper presents a comprehensive conceptual framework for the integration of AI-enabled Decision Support Systems (DSS) into infrastructure project management, with a focus on enhancing cost-efficiency, resource optimization, and multi-stakeholder coordination in U.S. public works. As infrastructure projects become increasingly complex and data-intensive, the adoption of intelligent systems capable of processing real-time information and generating actionable insights is crucial for timely and effective decision-making. The study explores the role of artificial intelligence, including machine learning, predictive analytics, and natural language processing, in conjunction with enterprise platforms such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Geographic Information Systems (GIS). Through a meta-analysis of 178 empirical studies and case evidence from state and federal infrastructure programs, the paper identifies critical enablers for successful implementation, including data interoperability, explainable AI interfaces, and integration with existing digital workflows. The proposed framework emphasizes dynamic scheduling, risk forecasting, lifecycle asset management, and compliance monitoring as core functional pillars of AI-DSS in infrastructure contexts. Furthermore, the study highlights institutional and governance considerations, such as change management, algorithmic accountability, and user adoption challenges, which significantly influence system performance. This contribution aligns with broader national goals of digital transformation, transparency, and sustainability in public sector infrastructure development.