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Purpose Generative artificial intelligence (GenAI) holds significant potential to improve accuracy, reduce uncertainties and support proactive financial decision-making throughout the project lifecycle. Despite these promising capabilities, the adoption of generative AI in construction cost management remains limited and uneven. This study, therefore, aims to examine the barriers to adopting GenAI for cost management in the Nigerian construction industry and to propose viable strategies to overcome them. Design/methodology/approach A quantitative research approach was adopted, with data collected through structured, closed-ended questionnaires administered to construction professionals. The data were analysed using both descriptive and inferential statistical techniques. Findings The study identified nine critical barriers to the adoption of GenAI for cost management in the Nigerian construction industry. Exploratory factor analysis grouped these barriers into two principal components: internal organisational constraints and external risk and environmental uncertainty factors. The results highlight that GenAI adoption is a socio-technical process influenced by both organisational readiness and external conditions. In addition, the study provides practical value by developing a structured matrix that aligns each barrier with targeted strategies across technology, people, process and risk management dimensions, offering actionable guidance for improving adoption. Originality/value This study contributes to digital transformation in construction by providing empirical, context-specific evidence on the adoption of GenAI for cost management in the Nigerian construction industry. It identifies and categorises nine critical barriers into internal organisational constraints and external risk and environmental uncertainty factors. The study further develops a structured matrix that links these barriers to targeted strategies across the technology, people, process and risk management dimensions. In addition, it extends the technology acceptance model by demonstrating how organisational and environmental factors influence GenAI adoption in a complex context.
Oyenubi et al. (Tue,) studied this question.