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Purpose This study investigates how artificial intelligence (AI) capabilities support perceived organisational competitiveness in project cost management among construction stakeholders in Malaysia, where AI adoption remains emerging and not yet well understood. Design/methodology/approach A questionnaire survey was conducted among Malaysian construction stakeholders involved in project cost management decision making, including developers, contractors, consultants, government agencies and technology providers. Two hundred and seventy-eight questionnaires were distributed and 203 valid responses were obtained. Data were analysed using relative importance index to rank AI capabilities and exploratory factor analysis to identify underlying AI capability dimensions. Expert validation was conducted to confirm the interpretability and relevance of the results. Findings Results show 29 AI capabilities supporting competitive advantage in project cost management cluster into four dimensions: predictive, diagnostic, descriptive and prescriptive. Predictive capabilities were ranked highest, followed by diagnostic capabilities, highlighting the importance of forecasting accuracy, cost monitoring and risk detection. Descriptive capabilities supporting integration of project, market and sustainability cost data were also valued, while prescriptive capabilities received comparatively lower rankings, indicating preference for AI as decision support over full decision automation. Research limitations/implications Results show AI capabilities in project cost management cluster into four dimensions of predictive, diagnostic, descriptive and prescriptive. Predictive capabilities were ranked highest, followed by diagnostic capabilities, highlighting stakeholder's emphasis on forecasting accuracy, verification, discrepancy detection in project cost control. Descriptive capabilities supporting the integration of project, market and sustainability cost data were also valued for improving information visibility and decision coordination, while prescriptive capabilities received comparatively lower rankings, indicating continued preference for AI-driven decision support over full decision automation. Originality/value This study contributes to construction AI research by empirically structuring AI decision capabilities in project cost management and providing contextual insight from the Malaysian construction industry, where AI adoption remains emerging. Extending to resource-based view, findings inform strategic AI investment, policy direction and professional capacity development in construction sector.
Maaz et al. (Mon,) studied this question.