Habtu, T. M.; A. Elsaigh, W. and Musonda, I. (2026) Toward a strategic framework for AI-BIM adoption in Ethiopian road infrastructure management. Engineering, Construction and Architectural Management, 33(15), pp. 564-589. ISSN 0969-9988
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; building information modelling; developing country context; digital transformation; road infrastructure management; technology adoption framework |
| Index terms: | dimension, Ethiopia, capacity building, emerging technology, integration, developing country, building information modelling, technology adoption, triangulation, developing country context, sample size, implementation, statistics, practitioner, questionnaire, developed country, service delivery, infrastructure management, collaboration, narrative, evidence, artificial intelligence, sampling, infrastructure development, methodology, survey, transformation, literature review, transparency |
| Subjects: | qualitative and interpretive research, management, Geography, contractual arrangements, infrastructure engineering, organizational analysis, mathematical modelling, evaluation and assessment methods, service delivery, research methods, data collection methods, social and economic development, research design and methodology, development economics, artificial intelligence, data analysis and analytics, health monitoring assessment and metrics, information systems, business, socioeconomic context, practitioner, professional development, innovation and technology management |
| Topics: | Health and Safety, Organizational Design, Human Resources, Engineering Principles, Business Strategy, Procurement, Digital Applications, Research Practice, Project Management, Governance, Information Management, Geographical Context, International Construction, Roles and Professions |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here