Mineral exploration is key in securing the raw materials needed for technology, construction, and renewable energy sectors. As demand for critical minerals such as lithium and cobalt continues to rise, efficient and reliable exploration methods are crucial to maintaining a steady supply.
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Advanced technologies allow companies to locate and assess mineral deposits more efficiently to help reduce environmental impact and costs.
Artificial intelligence (AI) has improved the speed and quality of mineral discoveries, which is essential for supply chains in the US and globally. This article looks at the current use of AI in mineral exploration, as well as its challenges, implementation, and the future of the industry.
Drivers for AI in Mineral Exploration
Mineral demand is surging as nations race to support technology industries, renewable energy, and national defense.
Key minerals such as lithium, copper, and rare earth elements are essential for products ranging from electric vehicle batteries to smartphones.
The US remains highly dependent on imports for 12 out of 50 designated critical minerals, with many sourced from China.
To counter supply risks and geopolitical vulnerabilities, US policy is now focused on reviving domestic mining, fast-tracking permits, and boosting mineral independence.1
AI promises to address two core bottlenecks: discovery speed and supply chain visibility. Traditional exploration is slow and relies on complex geological studies.
AI can analyze large datasets, such as geophysical and chemical information, to spot patterns that humans might miss, potentially shortening discovery times from years to months. AI can also monitor supply chains to track minerals from source to product and find alternative sources if disruptions occur.1,2
How AI Systems Advance Mineral Discovery
AI-powered exploration combines predictive algorithms, machine learning, satellite imaging, and remote sensor data to map mineral potential at large and small scales.
Startups and established mining companies use platforms that ingest historical drilling data, geological maps, magnetic field readings, seismic sensors, and laboratory results, then return predictive maps highlighting the most promising targets for further investigation.1,2
For example, Earth AI utilizes proprietary drilling hardware and predictive analytics trained on decades of data from Australia. In the last year, the technology facilitated the discovery of new indium, nickel, and palladium reserves at a success rate of 75%, far outpacing the traditional industry rate of less than 1%.
Meanwhile, Terra AI builds “underground maps” integrating thousands of geological layers, attempting to halve the 17-year mine development average.1
Legacy mining firms are also integrating AI into their operations. Exiger offers a supply chain management platform that breaks down products into digital twins—virtual representations mapping specific material content.
By tracing supply origins and analyzing billions of commercial and production records, Exiger has identified alternative extraction methods for rare minerals, such as recovering germanium from coal ash and smelter waste. This approach supports risk mitigation when international export restrictions arise.1
Recent Case Studies
In the last few years, there has been a significant increase in AI use and investment. GeologicAI, a Canadian company, raised $44 million in Series B funding from key mining and tech investors. Its AI platform analyzes drill cores on-site, utilizing advanced sensors and unique models to provide real-time mineral data. This information helps guide exploration and extraction efforts. As a result, GeologicAI has expanded to five continents and set new data collection and project management standards.3
Meanwhile, global mining companies such as Tata Steel, Newmont Corporation, and Barrick have embedded AI across the exploration-to-extraction chain.
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Tata Steel runs over 550 AI models for yield optimization and predictive maintenance, while Barrick uses AI-driven resource identification tools and energy optimization systems.
Small, specialized players such as KoBold Metals focus AI purely on data-intensive exploration for energy-critical metals such as cobalt and lithium, pushing the accuracy and efficiency of deposit modeling.4
Challenges of Using AI in Mineral Exploration
Although AI has considerable potential in mineral exploration, it still faces several challenges.
The accuracy of its predictions depends on the quality and availability of geological data, which is often limited in many areas. As a result, new surveys are needed before AI can be effectively utilized. Physical sampling and drilling are still required to confirm AI-predicted deposits, and this ground-truthing is labor- and capital-intensive.1
Adoption barriers also persist across the industry. Many mining firms remain sceptical, influenced by the sector’s traditionally slow technology uptake and ongoing regulatory complexity.
In the US, even the most promising AI-powered discovery efforts face 10-15-year permitting delays. While startup platforms and global digital twins build a compelling case for AI, they cannot sidestep the operational and legal bottlenecks unique to mining.1
Moreover, AI cannot replace expert geologists and mining engineers. Domain knowledge remains essential for interpreting algorithmic outputs and making final drilling or extraction decisions. The integration of AI into operational workflows requires ongoing collaboration between data scientists, geologists, and regulatory experts.1
How AI is Shaping the Supply Chain
The application of AI in mining goes beyond exploration and includes supply chain management as well. With the help of AI, modern supply chain management can generate digital twins of products, map supply origins, and calculate exposure to geopolitical or market risks. This capability is especially valuable in the current climate, with certain elements often subject to sudden export restrictions. In such scenarios, AI can identify alternate extraction paths or substitute supply streams, supporting resilience.1
Another important trend is blockchain integration. Companies are using AI to detect anomalies and track transactions along the mineral supply chain, which boosts transparency for regulators, consumers, and investors. This connection is integral for meeting environmental, social, and governance (ESG) standards and proving ethical sourcing of critical materials.1,5
The Future of AI in Mineral Exploration
The landscape of mineral exploration will continue to evolve as adoption spreads. In the last year, increased venture capital in exploration startups, new cross-sector partnerships such as accelerators pairing mining majors with AI ventures, and improved regulatory frameworks for digital tools all point toward sustained growth.
Companies will blend domain expertise with AI practitioners, integrating predictive analytics, real-time sensing, and supply chain visualization from early-stage surveying to international distribution.1,3
However, ongoing scepticism, limitations due to data quality, and the slow pace of permitting are significant hurdles.
As mineral demand rises, the pressure to refine and prove AI systems in real-world mining environments will intensify. AI will likely remain a tool to augment, not replace, human expertise as geologists, engineers, and policymakers adapt to the complexity of global mineral supply.1
Countries seeking mineral independence will need cutting-edge software and a willingness to invest in foundational data gathering, regulatory adaptation, and international partnerships. The combination of advanced analytics, skilled human judgment, and thoughtful governance appears essential for deploying AI effectively.
References and Further Reading
- Mok, A. (2025). AI could be the US's secret weapon in the race to mine more minerals — if it can prove itself. Business Insider. https://www.businessinsider.com/critical-minerals-mining-us-ai-exploration-supply-chain-import-reliance-2025-8
- AI Applications in Mineral Exploration: 2025 Top Trends. Farmonaut. https://farmonaut.com/mining/ai-applications-in-mineral-exploration-2025-top-trends
- Quesnel, J. (2025). GeologicAI secures $44M to advance AI-driven mineral discovery. Canadian Mining Journal. https://www.canadianminingjournal.com/news/geologicai-secures-44m-to-advance-ai-driven-mineral-discovery/
- Top 24 Global Mining Companies Driving AI Transformation in 2025. (2025). Omdena. https://www.omdena.com/blog/top-24-global-mining-companies-driving-ai-transformation-in-2025
- AI in Mining Industry: 7 Power Trends for 2025. Farmonaut. https://farmonaut.com/mining/ai-in-mining-industry-7-power-trends-for-2025
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