Strategic approaches to AI adoption in the construction industry of Ghana

Introduction The integration of artificial intelligence (AI) in the construction industry holds immense potential to enhance productivity, improve decision-making, and increase operational efficiency. However, in many resource-constrained environments, the adoption of AI technologies remains slow owing to infrastructural limitations, skills deficits, regulatory uncertainties, and institutional challenges. This study investigates strategic approaches to AI adoption within the construction industry of resource-constrained environments, using Ghana as a representative case. Methods A quantitative research design was employed, involving 239 responses from construction professionals. The data was analysed using exploratory factor analysis (EFA) to identify and validate the strategic approaches. Results The analysis revealed a three-cluster structure comprising strategic collaboration and governance, operational integration and safety, and workforce development and technical infrastructure. These clusters offer a context-specific approach that captures the multifaceted requirements for AI adoption in resource-constrained construction environments. Discussion Rather than the application of EFA, the contribution of this study lies in the empirically validated framework it produces which clarifies how AI adoption strategies interrelate in the construction industry of Ghana. The study contributes to the growing literature on digital transformation in construction by offering practical, evidence-based guidance to industry leaders, policymakers, and researchers seeking to drive AI adoption in resource-constrained environments.