Mining enters its AI era

Mining teams across Africa and the Middle East are exploring or actively implementing AI to enhance various stages of operations.

For example, AI-assisted analysis of ore samples during exploration improves the speed and accuracy of identifying ore bodies.

Predictive maintenance powered by AI also minimises equipment downtime, keeping operations running smoothly.

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In mine planning and operations, AI is helping to optimise processes like drilling and blasting.

By leveraging data from geology and mine-planning systems, miners can precisely position drill holes and customise the delivery of explosives.

This approach reduces energy consumption, processing requirements, and carbon emissions across the value chain.

Smart mining

At Rio Tinto’s Richards Bay Minerals in KwaZulu-Natal, AI is used as part of the company’s smart mining strategy to model ore bodies, manage equipment dispatch, and control blasts.

Similarly, Kilken Platinum in Thabazimbi has adopted advanced AI systems to monitor and optimise production at its platinum tailings retreatment plant, ensuring operational safety and efficiency.

According to a global study conducted by Tata Consultancy Services (TCS) on AI adoption in the energy and resources sector, which included 48 mining companies, more than 92% of organisations are at some stage of AI implementation.

Overcoming automation challenges

Many mining operations in Africa and the Middle East already use autonomous equipment for tasks like haulage, drilling, and blasting.

Remote operations centres are also common, allowing for safer and more efficient asset monitoring.

However, traditional automation often shifts bottlenecks to other parts of the mining process.

The focus will remain on collaboration between human expertise and AI systems, ensuring that mining companies can realise the full potential of AI without compromising on safety, community welfare, or environmental goals.

AI has the potential to make automation more dynamic and adaptive through technologies such as digital twinning and deep learning.

These systems offer a holistic view of complex mining environments, improving coordination across the value chain.

Safety upgrade

AI also enhances safety in hazardous working conditions.

For instance, AI-driven models can predict seismic activity, allowing workers and machinery to be moved out of harm’s way.

Other AI applications include using computer vision to ensure the integrity of blast holes, detect residual explosives, and predict safety risks caused by human error.

Additionally, generative AI can analyse engineering documents, identifying unclear language or outdated formats that may cause issues.

Future priorities for AI in mining

Seema Mehra, TCS VP and business head for energy, resources, and utilities in APAC and MEA, highlights four emerging trends for AI in the mining sector:

Supporting core priorities: AI accelerates four key priorities in mining: safety, operational efficiency, production optimisation, and emissions reduction.

An assistive role: AI acts as a recommendation tool, providing insights while human expertise remains critical for decision-making and corrective action.

Growing role of generative AI: While generative AI is rapidly gaining traction in corporate functions, its potential in mining processes, including exploration and production, is becoming more widely recognised.

Data foundations matter: Organisations with robust digital infrastructures, including secure data management and IoT networks, can develop and deploy AI models much faster.

“Diesel-powered loading and hauling can account for up to half the emissions across the mining value chain,” says Mehra.

“We are working with miners to develop autonomous haulage solutions using electric mining trucks, a significant step towards decarbonising pit-to-port operations.”

Reduce waste

AI is also being explored to improve mineral recovery and reduce waste during beneficiation processes, lowering reagent usage, energy consumption, and carbon emissions.

The ability to design modular and flexible mining processes using AI could help reduce the industry’s reliance on large, capital-intensive operations, making mining more agile.

As AI continues to evolve, it is poised to become a key enabler of safer, smarter, and more sustainable mining operations.