AI Takes Hold in African Mining, From Zambia’s Copper Fields to Mali’s Autonomous Gold Mine

Artificial intelligence is increasingly reshaping mining operations across Africa, with major projects deploying the technology to optimize resource extraction, improve worker safety and enhance asset performance — developments that coincide with KoBold Metals’ AI-driven push to develop one of the world’s largest undeveloped copper deposits in Zambia.

KoBold Metals is using AI-powered data analysis to advance the Mingomba project, which as of March 2026 is considered one of the largest and highest-grade undeveloped copper deposits in the world. The project is transitioning from an exploration success to a full-scale mine construction effort.

Across the continent, several other major operations are already putting AI to work. In Botswana, Botswana Diamonds launched a nationwide exploration program in 2024 using AI to analyze large geological datasets, helping identify new diamond-bearing zones and targets for copper, cobalt and gold in a fraction of the time required by traditional methods.

In South Africa, Kilken Platinum uses AI-driven monitoring at its Thabazimbi plant to track production metrics and safety protocols in real time. Rio Tinto employs AI for ore body modeling, intelligent blast control and fleet dispatch at Richards Bay Minerals, while Kumba Iron Ore uses AI-equipped drones for aerial surveys and blast clearances.

In Mali, Resolute Mining’s Syama Mine is advancing toward becoming the world’s first fully autonomous underground gold mine, deploying AI for autonomous haulage and drilling operations.

The efficiency and safety gains from these deployments are measurable. Continuous operation of autonomous haul trucks can increase productivity by up to 30%, while AI-driven predictive maintenance has the potential to reduce equipment downtime by an estimated 35%. AI systems used to predict hazardous conditions and monitor worker fatigue have been credited with reducing accident rates by up to 80% in some environments. Some sites are also using algorithms to optimize water and energy consumption during high-intensity processing stages such as crushing and milling.