Artificial Intelligence Adoption and Perceived Project-Management Benefits in Zambia’s Building Construction Sector: A Cross-Sectional Survey

Abstract

This study examined the adoption of artificial intelligence (AI) for project planning and progress monitoring in Zambia’s building construction sector. A cross-sectional design was used in Lusaka Province. Survey data were obtained from 78 construction practitioners selected from a finite sampling frame of 150, while semi-structured interviews were conducted with 5 purposively selected [stakeholder groups]. Quantitative data were analysed using descriptive statistics, the Relative Importance Index and two-sided one-sample t-tests against the neutral Likert midpoint (3). Reported use was concentrated in natural-language-processing applications (29.5%) and data dashboards (28.2%), whereas 12.8% reported no AI use. Respondents rated perceived planning efficiency above the neutral midpoint (M = 4.21, SD = 0.84), but this cross-sectional evidence does not establish an objective reduction in project delays. The main reported constraints were inadequate formal training, weak organisational strategies, limited technical support, data-governance concerns and scarce local project datasets. Respondents perceived AI enabled practices as beneficial for planning efficiency and potentially useful for earlier identification of delay risks. The findings support phased, context-sensitive adoption accompanied by workforce development, data governance and evaluation using objective project-performance indicators.