Botswana Minerals says it has identified 36 copper anomalies across two of its northern Botswana licences using artificial intelligence-assisted exploration technology, as mining companies increasingly adopt AI tools to speed up mineral discovery and cut exploration risk.
The AIM- and Botswana Stock Exchange-listed exploration company announced on Thursday that the targets were identified through an AI-driven analysis of geological, geophysical and geochemical datasets covering part of its eight-licence portfolio in northern Botswana. The anomalies have been grouped into six exploration “corridors” that the company believes could host copper mineralisation.
The development highlights the growing use of artificial intelligence in mineral exploration, where companies are increasingly relying on machine learning to process vast datasets and identify patterns that may be missed through conventional analysis.
Botswana Minerals used Planetary AI’s Xplore platform for the assessment. According to the company, the system combines machine-learning techniques with geological expertise to analyse multiple layers of exploration data, including geological mapping, fault structures, magnetic and gravity surveys, geochemistry and remote sensing information.
The company said the AI models were trained using data from copper mines around the world to identify geological similarities between known deposits and Botswana Minerals’ licences.
“There is no doubt that AI techniques are revolutionising identification of mineral targets,” said Botswana Minerals chairman John Teeling in the statement. “The ongoing analysis of our huge database continues to provide outstanding results.”
The licences are located along a geological corridor linking Namibia’s Damara Belt to the Central African Copperbelt in Zambia and the Democratic Republic of Congo, one of the world’s most productive copper regions. Botswana Minerals believes the area remains underexplored despite sharing geological characteristics with major copper-producing districts.
According to the company, the AI-assisted study identified geological signatures associated with several styles of copper mineralisation, including sediment-hosted systems and iron oxide copper gold (IOCG) deposits. The anomalies are associated with major fault systems, favourable carbonate host rocks and alteration zones that could indicate hydrothermal mineral activity.
The company compared the geological setting of its targets to major global deposits such as Kamoa-Kakula in the DRC and Olympic Dam in Australia, while cautioning that the references are intended only as geological analogues rather than indicators of potential scale.
Botswana Minerals plans to begin field exploration work within the next three months to verify and rank the anomalies ahead of possible drilling campaigns. It also intends to extend the AI-assisted analysis across its remaining six licences in northern Botswana.
The announcement comes as demand for copper continues to rise globally due to the metal’s importance in electric vehicles, renewable energy infrastructure and data centres powering AI systems themselves. That demand has intensified interest in new copper discoveries across Africa, particularly in underexplored regions adjacent to established mining belts.
AI adoption in mining exploration has accelerated over the past few years as junior explorers seek cheaper and faster ways to narrow down drilling targets. Companies are increasingly partnering with specialised AI firms to analyse decades of historical exploration data alongside satellite imagery and geophysical surveys.
For Botswana, where diamonds have historically dominated the mining sector, successful copper discoveries could help diversify the country’s mineral economy amid growing investor interest in critical minerals tied to the global energy transition.