Adoption of artificial intelligence-driven predictive maintenance in South Africa’s coal mines

Abstract

The adoption of artificial intelligence (AI) in sectors such as banking and manufacturing is advancing rapidly; however, the mining industry in South Africa, particularly coal mining, exhibits a much slower rate of adoption. This study examined the use of AI for predictive maintenance in South Africa’s coal mines, employing the Technological, Organisational, and Environmental (TOE) framework and the Technology Acceptance Model (TAM) to understand the factors influencing technology adoption in the coal mining industry. The literature review emphasised the benefits of AI in predictive maintenance, particularly in reducing operational costs and enhancing maintenance efficiency. Despite these advantages, most coal mines still rely on traditional maintenance strategies such as preventative, condition- based, and reactive maintenance. The study aimed to examine current maintenance practices, the types of AI predictive technologies adopted, the enablers and barriers to adoption, and overall perceptions towards AI in coal mining. To achieve this, a qualitative approach was used, involving interviews with maintenance engineers, and a reflexive thematic analysis was conducted to identify the emerging themes. Thematic analysis identified four main themes: technological advancements in maintenance practices, the role of digital innovation in enabling predictive and efficient maintenance, the need for strategic transformation in maintenance to achieve organisational resilience, and the constraints hindering AI adoption. Although some larger mining companies are starting to use digital tools like condition monitoring, most operations remain cautious. Barriers, including high costs, limited access to AI technology, a lack of local support, and low awareness; especially among junior miners; are slowing the shift to AI-driven predictive maintenance. Despite the challenges, the study found a generally positive attitude amongst maintenance engineers who participated in the research towards AI adoption, suggesting that with sufficient investment in infrastructure, capacity-building, and support systems, the coal mining industry could shift to more efficient, data-driven maintenance models. The TOE and TAM frameworks offered insights into the importance of organisational readiness, leadership support, and perceived ease of use. The study concludes by recommending targeted interventions to address the identified gaps. It proposes future research that extends beyond coal mining to explore AI adoption across South Africa’s broader mining sector and the impact of AI-driven maintenance on mines' operational costs in South Africa.