AI Resource Optimization and Waste Reduction in Ghanaian Construction: The Mediating Role of Process Efficiency

The construction industry in Ghana plays a critical role in national development but continues to face significant challenges related to material waste, operational inconsistencies, and uncontrolled project costs. In response to growing sustainability needs, Artificial Intelligence (AI) has emerged as a viable tool for improving resource utilization and reducing waste across construction processes. This study examines the relationship between AI-driven resource optimization and waste reduction in Ghana’s construction industry, with particular emphasis on the mediating role of process efficiency.Adopting a quantitative cross-sectional survey design, data were collected from 450 construction professionals across six construction subsectors in Ghana. The data were analysed using SPSS, incorporating descriptive statistics, correlation analysis, and mediation analysis through Hayes’ Process Macro Model 4. The results indicate that AI resource optimization exerts a positive and statistically significant effect on waste reduction. Additionally, process efficiency partially mediates this relationship, strengthening the influence of AI-driven resource optimization on waste reduction outcomes.The study provides empirical evidence from a developing economy context, demonstrating that the effectiveness of AI adoption in construction is enhanced when supported by efficient operational processes. The findings offer practical insights for construction firms, industry stakeholders, and policymakers seeking to advance sustainable construction practices, minimize material waste, and support national development objectives aligned with the United Nations Sustainable Development Goals.