A Scoping Review of the Benefits of Ai Adoption in the South African Construction Industry
Year: 2025
Type: article
Abstract: Historically, South Africa has been a nation plagued by socio economic disparities that has negatively impacted its growth and transformation. However in recent years the nation has sought to embrace the rapid changes brought upon by globalisation; leaving governments and establised sectors questioning their relavance and longevity. In order to participate in a digital future, the transformation of governments and its respective sectors are crucial to its sucess. In South Africa, the Artificial Intelligence (AI) market is experiencing a rapid growth that is driven by the emergence of AI policies, and is projected to reach 0.90bn USD in 2024. The AI market is further expected to grow at an annual rate of 28.22%, resulting in a market volume of 4.00bn USD by the year 2030. A theoretical gap exists in current South African literature with regards to the pereceived benefits of AI adoption within the South African construction sector, in resolving infrastructure delivery challenges. An interpretivist philosophy was chosen for the study. This approach allows for a systematic literature review, synthseized via the PRISMA 2020 process; further accompanied by a thematic analysis of qualitative data. NVivo software was utlised as a tool to thematically analyse 222 indexed publications on the role of AI in the construction sector and its perceived benefits, revealing subjective experiences and meanings. Findings illustrate that the integration of AI within the South African construction sector can enhance productivity, efficiency, quality and promote cost reduction. In order for South Africa to exploit the full potential and overarching benefits of AI in the construction sector; the sector and its governing bodies are required to develop a strategic AI framework that carefully considers the social, ethical and economic implications, ensuring that AI benefits are broadly shared, and that the risks are mitigated effectively.
Authors: ZAKHEEYA ARMOED, LINDOKUHLE MOLEFE, LIZWI NGCOBO
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7081934788
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