ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Construction Management
Strategic Approaches to AI Adoption in the Construction Industry of Ghana
Rexford Henaku Aboagye 1,2
Clinton Aigbavboa 2
Simon Ofori Ametepey 1,2
Hutton Addy 1,2
1. Koforidua Technical University, Koforidua, Ghana
2. University of Johannesburg Faculty of Engineering & the Built Environment, Johannesburg, South Africa
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Abstract
The integration of artificial intelligence (AI) in the construction industry holds immense potential to enhance productivity, improve decision-making, and increase operational efficiency. However, in many developing countriesresource-constrained environments, the adoption of AI technologies remains slow owing to infrastructural limitations, skills deficits, regulatory uncertainties, and institutional challenges. This study investigates strategic approaches to AI adoption within the construction industrysector of developing countriesresource-constrained environments, using Ghana as a representative case. A quantitative research design was employed, involving 239 responses from construction professionals. The data was analysed using exploratory factor analysis (EFA) to identify and validate the strategic approaches. The analysis revealed a three-cluster structure comprising strategic collaboration and governance, operational integration and safety, and workforce development and technical infrastructure. These clusters offer a context-specific approach that captures the multifaceted requirements for AI adoption in resource-constrained construction environments. The use of EFA distinguishes this study from previous work by providing statistically validated insights into how these strategies interrelate. Rather than the application of EFA, the contribution of this study lies in the empirically validated framework it produces which clarifies how AI adoption strategies interrelate in the construction industry of Ghana. The study contributes to the growing literature on digital transformation in construction by offering practical, evidence-based guidance to industry leaders, policymakers, and researchers seeking to drive AI adoption in resource-constrained environments.
Summary
Keywords
AI adoption strategies, Artificial intelligence (AI), Capacity Building, Construction Industry, Developing Countries, Digital infrastructure
Received
01 December 2025
Accepted
20 February 2026
Copyright
© 2026 Aboagye, Aigbavboa, Ametepey and Addy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rexford Henaku Aboagye
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.