Author(s): Li Wei, Zhao Ming and Chen Yu

Abstract: The construction industry faces persistent challenges of inefficiency, cost overruns, and delays. This study explores the transformative potential of artificial intelligence (AI) in construction project management (CPM), presenting novel insights into its impact on project efficiency and adoption barriers. Using a mixed-method approach, quantitative data from industry surveys and qualitative insights from expert interviews were integrated. Key findings reveal a significant positive correlation between AI adoption and project efficiency, with tools like machine learning and predictive analytics demonstrating high efficacy. The study highlights critical challenges, such as high implementation costs and lack of technical expertise, while proposing structured strategies for effective integration. Results indicate an 18% reduction in time and 15% reduction in cost overruns for AI-integrated projects, supported by strong statistical evidence (R² = 0.78, p < 0.05). This research contributes actionable strategies for industry practitioners and policymakers, emphasizing the importance of organizational readiness and training programs. Future studies should address the scalability of AI integration and its synergy with emerging technologies to optimize CPM processes. The construction industry faces persistent challenges of inefficiency, cost overruns, and delays. This study investigates the adoption of artificial intelligence (AI) in construction project management (CPM) and its impact on project performance. Using a mixed-method approach, the research combines quantitative data from industry surveys with qualitative insights from expert interviews. Key findings reveal a significant positive correlation between AI adoption and project efficiency, with machine learning and predictive analytics emerging as the most impactful tools. Despite these benefits, challenges such as high implementation costs and lack of technical expertise persist. Results show a notable reduction in time (18%) and cost overruns (15%) in AI-integrated projects. Regression analysis confirmed the hypothesis that AI adoption enhances CPM efficiency (R² = 0.78, p < 0.05R^2 = 0.78, p < 0.05). Qualitative insights highlighted the importance of organizational readiness, leadership support, and stakeholder engagement for successful AI implementation. The study compares its findings with prior research, emphasizing the need for training programs and data infrastructure to overcome adoption barriers. This research contributes to the growing body of knowledge on digital transformation in construction and proposes actionable strategies for practitioners and policymakers. Future studies should explore the integration of emerging technologies to further optimize CPM processes.

Pages: 46-48 | Views: 102 | Downloads: 32

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