Toward a strategic framework for AI-BIM adoption in Ethiopian road infrastructure management

Purpose Artificial intelligence (AI) and building information modelling (BIM) have transformed the architecture, engineering and construction (AEC) industry in developed countries, but their adoption in Ethiopia's road infrastructure management remains limited. Empirical evidence on adoption benefits, challenges and a context-specific strategic framework for developing countries is limited. This study addresses this gap by exploring the necessity of AI-BIM integration and identifying how it can be effectively implemented in Ethiopia. Specifically, it investigates: (1) professional perception on the benefits and challenges of AI-BIM adoption, (2) critical dimensions for adopting AI-BIM integration and (3) the development of a context-specific strategic framework to guide AI-BIM adoption. Design/methodology/approach This study employs a mixed-method exploratory design, combining a narrative literature review and structured questionnaire survey to investigate AI-BIM adoption in Ethiopian road infrastructure management. The literature review synthesises fragmented theoretical and empirical insights to identify six interrelated strategic dimensions, while the survey captures practitioner perceptions of adoption benefits and challenges. Data were collected from 33 professionals from the Ethiopian Road Administration (ERA) and the Addis Ababa City Road Authority (AACRA) using purposive and convenience sampling. Quantitative data were analysed using descriptive statistics to determine key patterns and priorities, and qualitative insights were thematically synthesised. The integration of findings follows a triangulation approach, enabling the development of a context-specific, empirically grounded, and practically applicable AI-BIM adoption framework. Findings Road infrastructure management practices in Ethiopia are conventional and fragmented. The findings highlighted the potential benefits and challenges of adopting AI-BIM integration. Six critical strategic dimensions were synthesised into a context-specific strategic framework tailored to Ethiopia's road sectors. Research limitations/implications This study provides timely, context-specific insights; however, it is limited by the sample size and focus on selected road sectors and technologies. The findings are primarily context-specific and may not be directly generalisable. Future research could expand the empirical scope, incorporate additional emerging technologies, and validate the framework in other sectors and geographical contexts. Practical implications The proposed framework offers structured and actionable guidance for policymakers and practitioners, emphasising phased implementation, capacity building and interdepartmental collaboration to enable successful AI-BIM adoption. Social implications The study promotes the adoption of AI-BIM integrations to support digital transformation, enhances transparency, improves service delivery, and contributes to sustainable road infrastructure development outcomes. Originality/value This study contributes evidence-based, context-specific insights and a strategic framework for AI-BIM adoption in Ethiopia, bridging the gap between global technological advancement and local implementation realities. It contributes practical guidance for policymakers and practitioners in developing countries rather than theoretical innovation.