AI-DRIVEN PROCESS OPTIMIZATION FRAMEWORKS FOR MEGA INFRASTRUCTURE PROJECTS UNDER SAUDI VISION 2030

Abstract

The current research paper proposes an AI-driven framework for optimizing processes of mega infrastructures within the framework of the Saudi Vision 2030 program. The authors are convinced that AI technology should not be perceived as something separate from other phenomena; on the contrary, it is the layer of orchestration allowing linking such components as planning information, BIM models, digital twin infrastructures, IoT technologies, logistics processes, data, and decision-making processes at the executive level in the feedback loop. The present paper provides a systematic review of the most recent studies in the field of applying AI technology in construction management, BIM intelligent systems, digital twin infrastructure, engineering with digital modeling and simulation, environmental analyses for construction and operation, and the transition to the Digital Saudi Arabia which have been published between 2020 and 2025. The possibility of applying machine learning, computer vision, generative optimization, predictive control, and dashboard intelligence to enhance the reliability of the schedule, efficiency in material use, predictability of finances, quality assurance and control, and risk transparency during the management of large-scale projects is studied. The present research is concentrated on identifying the process architecture including such components as data model standardization, stage gate decision and management, interoperability, notifications and explanations, and human intervention which are essential for implementing the AI technology and achieving its maximum advantages. As a result of the research, the authors propose an AI-Driven Process Optimization Framework for Saudi Mega Projects (AIPOF-SMP).