From data to decisions : examining the Western Cape's use of AI technologies to enhance asset condition assessment strategies in road infrastructure

Abstract

In response to growing infrastructure demands and the need for safer, more efficient road networks, effective asset management is increasingly critical. This paper examines the current and potential use of Artificial Intelligence (AI) technologies in enhancing asset condition assessment strategies in the Western Cape province of South Africa. The study investigates how AI tools, such as predictive analytics, IoT sensors, and machine learning, could support real-time monitoring, performance evaluation, and predictive maintenance. The paper pursues three core objectives. Firstly, it compares traditional asset management and maintenance approaches with AI-based methods to assess their relative efficiencies and operational benefits. Secondly, it evaluates the status of AI adoption in the Western Cape using the Diffusion of Innovation (DOI) framework. Thirdly, it identifies the barriers and opportunities influencing AI implementation within the province’s road infrastructure sector. Findings indicate that while there is growing policy interest in AI technologies, practical implementation remains limited. In contrast, national authorities like large state-owned entity under the authority of the Ministry of Transport. have made notable progress through AI-aligned systems, providing a precedent for provincial application. The study concludes that AI integration could significantly enhance road asset management by improving decision-making, resource optimization, and infrastructure resilience. Strategic recommendations include developing an AI integration policy, launching pilot projects, building technical capacity, and strengthening public-private partnerships. These insights offer valuable guidance for other regions facing similar infrastructure management challenges.