Assessing Readiness for Artificial Intelligence Adoption in Structural Design Practices: A Mixed-Methods Study of Government Real Estate Organizations in Anambra State, Nigeria

Abstract

The integration of Artificial Intelligence (AI) into structural engineering practice represents one of the most consequential technological shifts in the built environment sector in recent decades (Elmousalami et al., 2025). This study examined the readiness of government real estate organizations in Anambra State, Nigeria, to adopt AI in structural design for smart city infrastructure. Using a mixed-methods approach, data were collected from 150 questionnaire respondents and 15 key informant interviews across the Anambra State Housing Development Corporation (ASHDC), Ministry of Works, and Ministry of Housing and Urban Development. Overall readiness was found to be moderate to low (mean score = 2.83 out of 5). Organizational factors scored highest (mean = 3.15), while technological infrastructure (mean = 2.60) and human resource capacity (mean = 2.59) were the weakest areas. Accordingly, K-means clustering and Random Forest analysis revealed that external barriers, integration feasibility with existing tools, and prior piloting experience were the strongest predictors of readiness. Participants recognized clear benefits of AI adoption in efficiency, accuracy, and cost savings, yet highlighted persistent barriers including infrastructure deficits, skills gaps, and budget constraints. A conceptual case study illustrates how an AI-augmented workflow could be applied to a typical 2-storey smart housing block. The study concludes with practical recommendations for phased training, infrastructure upgrades, and pilot projects aligned with national digital transformation strategies that increasingly position AI as a core enabler of public sector modernization.