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The adoption of artificial intelligence (AI) in the construction industry is shaped by a complex interplay of technological, organizational, regulatory, and market-driven factors. This study empirically examines the critical drivers of AI adoption in construction projects within the context of a developing economy. Using survey data and principal component analysis, 25 initial drivers were distilled into seven coherent components. These factors were subsequently mapped into four theoretical categories: technological, organizational, environmental policy and sustainability, and market & ecosystem drivers. This creates a conceptually grounded framework that bridges empirical evidence with theoretical understanding. Findings indicate that AI adoption extends beyond technology readiness, encompassing organizational priorities such as productivity, quality control, operational efficiency, regulatory & safety compliance, sustainability & environmental considerations, and market-driven imperatives. The study confirms that safety and sustainability emerge as distinct, but interrelated drivers, reflecting the growing role of policy and responsible practice in AI integration. The study contributes a multidimensional framework that offers both academic and practical insights for AI adoption strategies in construction. For practitioners, the findings highlight the importance of aligning technological investments, digital transformation initiatives, and operational workflows with organizational and market needs. For policymakers, the study underscores the value of governance frameworks, standards, and incentives that support safe, sustainable, and effective AI deployment.
Dosumu et al. (Fri,) studied this question.