Despite the growing potential of artificial intelligence (AI) to enhance construction risk management through predictive analytics, automation, and data-driven decision-making, its adoption within developing-country construction industries remains limited and poorly understood. This study aims to examine the key drivers influencing the adoption of AI technologies for construction risk management within the Ghanaian construction industry.
A quantitative survey approach was adopted, underpinned by the technology–organisation–environment framework. Data were gathered from 94 construction professionals in Ghana using a structured questionnaire. Mean score ranking, one-sample t-test, and exploratory factor analysis using principal component analysis with varimax rotation were used for data analysis.
The findings reveal that sustainability reporting pressures, availability of quality data, competitive advantage, cross-functional data integration, and policy focus on climate and resilience are the most influential drivers of AI adoption in construction risk management. The factor analysis further grouped the drivers into four interrelated dimensions: strategic and sustainability-oriented ecosystem enablers; organisational digital maturity and technical capability; market, compliance, and performance-driven enablers; and foundational leadership and performance enablers. The results indicate that AI adoption in construction risk management among the sampled professionals is increasingly driven by sustainability governance, ecosystem-level pressures, and organisational data readiness rather than purely technological considerations.
This study extends AI adoption discourse within construction risk management by providing empirical evidence from a developing-country context and demonstrating the growing influence of sustainability governance and data ecosystem maturity on AI-driven digital transformation.
