Assessment of AI development level in construction cost management using AHP- cloud model: a case study of China’s Yangtze River Delta Region

Abstract

Artificial Intelligence (AI) integration is crucial for solving traditional inefficiencies and data fragmentation in construction cost management, driven by the demands of Industry 4.0. However, a significant gap exists in holistically evaluating AI development levels, lacking a framework and empirical analysis that systematically bridges macro-environmental factors and micro-organizational readiness. This study aims to assess the AI development level in construction cost management across China’s Yangtze River Delta (YRD) region. A novel PEST-O (political, economic, social and technological-organizational) framework was established, and an integrated Analytic Hierarchy Process (AHP) and cloud model approach was used to analyze expert data. Findings indicate the YRD’s overall AI development is “average” (Ex = 52.71), revealing a critical imbalance. Although the Political dimension shows strong support, the organizational, technological, and social dimensions remain “worse,” confirming organizational incapacity as the primary bottleneck. Regionally, Shanghai and Zhejiang significantly outperform Jiangsu and Anhui. This research theoretically validates the PEST-O model and provides quantitative diagnosis. Practically, the findings of this research provide policymakers and other stakeholders with a diagnostic tool tailored to specific regions. They also help facilitate the implementation of differentiated measures for leading and lagging regions.