Next-Generation Construction Intelligence: A Reference Model for Data-Driven, AI-Enabled Construction Enterprises

Abstract

In order to overcome the problems of cost overruns, timetable delays, safety hazards, and disjointed decision-making, the construction sector is progressively embracing digital and AI-enabled solutions. By combining enterprise-wide decision-support systems, AI-driven analytics, and multi-source data, this study offers a reference model for next-generation construction intelligence. The study creates a multi-layered architecture that includes data acquisition, management, analytics, decision support, and governance using a hypothetical, model-driven methodology. It then assesses the architecture's efficacy using simulated datasets, scenario-based analysis, and expert-informed validation. The findings show that while encouraging proactive and flexible business operations, the approach greatly improves project planning, risk management, productivity, and safety monitoring. Furthermore, responsible AI adoption and long-term sustainability are ensured by including ethical and governance aspects. The report offers a theoretical and practical framework to direct construction companies toward AI-enabled, data-driven transformation.