AI-Driven Project Management for Construction SMEs: A Framework for Cost and Schedule Optimization

Abstract

Efficient project management remains a persistent challenge for small and medium-sized enterprises (SMEs) in the U.S. construction industry, where delays and budget overruns are prevalent. This study proposes an AI-driven project management framework tailored to SMEs, integrating predictive scheduling, resource allocation, and real-time progress monitoring. By leveraging machine learning models and cloud-based visualization tools, the framework generates adaptive schedules and optimizes task prioritization. A case study using representative project data demonstrates that the AI-enhanced system reduces schedule variance and cost deviation compared to traditional critical path methods. Results indicate that SMEs can achieve significant improvements in project predictability and resource efficiency without incurring the high costs of enterprise-level tools. The proposed framework contributes to national priorities in infrastructure development by enabling SMEs—who comprise the majority of U.S. construction firms—to deliver projects with greater timeliness, cost-efficiency, and resilience.