AI, critical minerals & the future of mining

AI is no longer a future concept in mining. It is rapidly becoming the operating system of how we discover, develop and responsibly extract critical minerals.

Jef Caers (Mineral-X, Stanford University) as a member of the Programme Committee of World Mining Congress 2026, will have the privilege to discuss how quickly AI is reshaping our industry not as a standalone tool, but as an integrated decision-making ecosystem.

To understand the shift, consider the world’s most sophisticated AI deployment today: the autonomous vehicle. A self-driving car is not one algorithm. It is a system, sensors, models, probabilistic reasoning, continuous feedback loops,  operating under uncertainty. Mining is moving in exactly this direction.

From intuition alone to intelligence amplified

AI-assisted exploration is already transforming how we search for critical minerals.

But as Jef Caers states, AI does not replace the geologist. AI excels at evaluating thousands of geological scenarios, integrating geophysical, geochemical, remote sensing and drilling datasets within complex 3D subsurface models. It performs sequential decision-making, recommending what data to acquire next to reduce uncertainty around economically viable deposits. However, what it lacks is creativity, intuition and the experiential reasoning of an expert geoscientist.

AI as a strategic compass

Sustainable mineral supply requires more than orebody discovery. It demands: policy alignment, ESG integration, supply chain transparency, geopolitical risk awareness, long-term economic modelling

AI can evaluate these multidimensional interactions simultaneously, something humans struggle to do consistently across time horizons and competing objectives.

When decisions become sequential, uncertain and multi-criteria, AI-supported systems demonstrably outperform isolated human judgement. This capability is not theoretical. It is emerging now.

Operational resilience through 3D intelligence

Every orebody is heterogeneous. Spatial variability in grade, hardness, mineralogy and metallurgical response determines project performance. AI can integrate these three-dimensional variations directly into operational planning: mine scheduling, blending optimization, throughput management, energy efficiency. The result? Fewer costly surprises. More predictable outcomes. Greater resilience.

Operational tesilience through 3D intelligence

Mineral processing is ripe for AI-enabled transformation. By jointly optimising grinding, flotation, leaching and extraction, rather than treating them as isolated steps,  AI unlocks new efficiency pathways.

Even more critically, AI enables smarter reprocessing of tailings. By recommending what data to acquire and modelling economic potential under environmental constraints, AI can help recover critical minerals from historical waste while reducing ecological impact. This is where technology meets responsibility.

World Mining Congress 2026

As part of WMC 2026, Jef Caers recently explored these themes during his webinar. The next opportunity to continue the conversation in person will be at WMC 2026, Peru 24-26.06.26

Image source: WMC 2026 official webpage