While at the 2026 Investing in African Mining Indaba in Cape Town, Boston Consulting Group’s (BCG) Dr Akash Singh, Platinion Principal IT Architect at BCG Johannesburg, and Puso Thahane, partner at BCG Johannesburg, talked to Bizcommunity about how GenAI is reinventing South African mine maintenance.
GenAI is reinventing mine maintenance by synthesising structured and unstructured data, such as sensor feeds and technician notes, to predict failures, run diagnostics, order parts in real-time, and coordinate in-field support.
It uses an orchestration layer that connects people, equipment, parts, and real‑time data to enable coordinated and increasingly autonomous maintenance operations.
When applied in maintenance, AI has been shown to increase fleet availability by up to ~15% and reduce maintenance costs by ~10%.
The duration of technician job execution can also be reduced by up to 20% as AI enhances visibility, integrates previously siloed systems, and enables faster, more accurate decisions on mining sites.
GenAI is designed to augment, not replace, mine maintenance workers.
It is most effective when embedded into existing workflows and used to create early predictions of equipment failure, helping technicians plan work orders, order parts proactively and execute work more efficiently with better diagnostics and coordinated support.
Companies can integrate AI by creating an orchestration layer that connects all maintenance data, integrating GenAI platforms with ERP, EAM, and IT/OT systems, and adopting modular solutions that don’t require advanced digital maturity.
Organisational readiness and embedding GenAI into established workflows are essential for successful adoption.
By improving visibility and coordination across equipment, people, and data, GenAI reduces the safety risks caused by unplanned maintenance and isolated predictive systems.
Better diagnostics and real‑time support help prevent hazardous breakdowns and enable safer technician interventions.
Mine maintenance will continue to shift toward automated maintenance ecosystems, where GenAI can autonomously predict possible equipment failures, identify required parts and spares and repair equipment and link to existing work order management systems to support human-led scheduling, parts coordination, and work-order execution.
Maintenance operations will become more integrated across sites, with technicians relying on real-time data for diagnostics, planning, and work execution support.
The performance gains seen today, 15% availability, 10% cost savings, and 20% job optimisation, are expected to become standard.
