Artificial intelligence in property management in Tanzania: understanding operational efficiency and tenant satisfaction

Purpose This study examines the integration of Artificial Intelligence (AI) in property management in Tanzania, evaluating its impact on operational efficiency and tenant satisfaction (SAT) within mixed-use buildings. Design/methodology/approach Employing a mixed-methods design, quantitative data were collected through structured questionnaires from 213 tenants, while qualitative insights were obtained through semi-structured interviews with six property managers (PMs) across six AI-enhanced properties. The survey focused on AI-driven systems in maintenance, energy management, security and tenant engagement. Findings Results indicate significant improvements in energy efficiency, maintenance response times and overall tenant satisfaction. AI applications and automated lease agreement management reduce operational costs by up to 30%. However, high implementation costs, limited technical expertise, infrastructural limitations and cultural resistance present barriers to wider adoption. Research limitations/implications The study is limited to six properties, suggesting the need for expanded research across diverse regions and building types to validate and generalize the findings. Practical implications The insights guide strategic investments in AI technologies, which promote cost-efficiency and enhanced tenant relations. Social implications Enhanced property management through AI fosters improved community trust, a safer and more livable neighborhoods. Originality/value This research contributes to the sparse literature on AI in property management in developing nations by providing empirical evidence from the Tanzanian context and offering recommendations for policymakers, developers and property managers.