Why AI will change how we find, mine and process critical minerals

Artificial intelligence is likely to change exploration, mining, and processing of critical minerals in profound ways. A useful way to understand this transformation is by comparing AI in mining to the most sophisticated AI system in operation today: the self-driving car. As in autonomous driving, AI in mining is not a single tool, but a system that brings together data, models, and decision-making under uncertainty. Existing and developing applications already show how this transition is beginning to take shape.

In critical mineral exploration, an AI-assisted approach has already transformed the way companies operate, making exploration more cost-effective. At the same time, AI in exploration clearly requires human intelligence. While AI is strong in exploring various plans computationally through numerical modelling, it lacks a geologist’s creativity and intuitive reasoning based on past experience when interpreting data. Rather than replacing expertise, AI is optimally placed to help reduce uncertainty about the possible presence of economically viable deposits. This reduction in uncertainty is achieved by sequentially performing multiple data acquisition campaigns, while fully factoring in the three-dimensional subsurface in all of its complexity.

The role of AI extends beyond exploration. Sustainable growth for equitable prosperity requires new technology investments as well as policy changes at both national and international levels. In this context, AI can help recommend optimal actions for mining companies, investors, governments, and NGOs, taking into account the complex interaction of geological, economic, environmental, sustainability, and geopolitical factors. AI can also help transform current supply chains in line with chosen ESG standards. When decisions are complex, sequential and involve many competing criteria over time, an AI-assisted approach consistently outperforms human activity alone.

AI also has the potential to reduce unexpected and costly surprises in mining and processing by optimising operational planning. Such planning depends on understanding an orebody’s significant three-dimensional variations in terms of key mining and metallurgical factors. By integrating these variations into planning decisions, AI supports more robust and resilient operations.

In mineral processing, AI provides clear optimisation opportunities in areas currently ripe for innovation. This includes the introduction of new processing technologies, as well as the combined optimisation of processing steps such as grinding, flotation, leaching, and extraction. Importantly, AI can support the reprocessing of critical minerals from mine tailings, while reducing the environmental impacts associated with mining. By recommending the type and quantity of data to acquire, AI can help assess the economic potential of metal extraction from tailings.

These applications reflect the direction mining must now take. Mining for the future, grounded in trust, enabled by transformation and accelerated by technology. Artificial intelligence is increasingly central to how we can responsibly accelerate the discovery of critical minerals.

I will explore how AI is accelerating the discovery of critical minerals in greater depth in the upcoming webinar “The Future of AI in Exploration, Mining and Processing of Critical Minerals”, organised by the World Mining Congress. The webinar will take place on 17 February 2026, at 13:30 PST. Registration is available at: https://bit.ly/4bMmJuT

Author bio

Jef Caers Mineral-X, Stanford University Member of the Program Committee, World Mining Congress 2026

Read the article online at: https://www.globalminingreview.com/special-reports/12022026/why-ai-will-change-how-we-find-mine-and-process-critical-minerals/