Artificial Intelligence in Construction: Applications, Benefits, and Adoption Challenges

Abstract

Artificial intelligence (AI) has emerged as a transformative technology with significant potential to address long-standing challenges in the construction industry, including low productivity, safety risks, cost overruns, and inefficiencies. This study presents a structured and critical review of AI applications in construction, synthesizing existing literature to examine key technologies, practical implementations, benefits, challenges, and future research directions. A structured narrative review approach was adopted, drawing on peer-reviewed studies sourced from major academic databases such as Scopus, Web of Science, and ScienceDirect. The findings indicate that AI technologies—including machine learning, computer vision, robotics, and their integration with building information modelling (BIM), internet of things (IoT), and digital twins—are increasingly applied across various stages of the construction lifecycle. These applications demonstrate considerable potential to enhance safety performance, improve productivity, optimize resource utilization, strengthen quality control, and support sustainable construction practices. However, the review also reveals that AI adoption remains fragmented and limited in practice, with many applications confined to experimental or pilot stages. Key challenges identified include data fragmentation, lack of standardization, high implementation costs, limited technical expertise, organizational resistance, and regulatory uncertainty. The study highlights that these barriers are interconnected and extend beyond technological constraints, reflecting the complex socio-technical nature of the construction industry. This study contributes to the literature by providing a comprehensive and critically synthesized understanding of AI in construction, identifying key research gaps, and proposing future directions. These include the development of standardized data frameworks, large-scale empirical validation, integration of AI with emerging digital technologies, and advancement of explainable and trustworthy AI systems. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to facilitate effective and responsible AI adoption in the construction sector.