BARRIERS AND SUCCESS FACTORS TO ADOPTING ARTIFICIAL INTELLIGENCE IN PROCUREMENT FOR CONSTRUCTION PROJECTS

Abstract

Artificial Intelligence (AI) adoption within construction procurement has the potential to significantly enhance efficiency, transparency, and decision-making; however, its adoption within the construction industry remains limited. This research investigates the critical barriers and success factors influencing AI adoption in procurement for construction projects, with a focus on the Malaysian context. A survey-based research design was employed, yielding 133 valid responses from construction professionals involved in procurement-related activities. Data were analyzed using reliability analysis, normalized mean ranking, agreement tests, overlap analysis, and correlation analysis to identify key patterns and interrelationships. The findings reveal that major barriers include high initial implementation costs, lack of AI skills and expertise, data quality issues, and resistance to organizational change. The critical success factors are strong top management support, workforce training, robust data infrastructure, and a clear AI adoption strategy. The outcomes offer practical insights for stakeholders and policymakers in developing targeted strategies for sustainable AI adoption in construction procurement, improving project performance and supporting the sector’s digital transformation.