How AI is reshaping mining in Canada’s northern regions

A derivative of this article was published in North of Ordinary Business in November 2025.

Mining is at a Crossroads in the North

With global demand surging for critical minerals like lithium, cobalt, and rare earth elements, the mining sector faces a dual challenge: how to increase output, and how to reduce environmental and social risks.

Artificial Intelligence (AI) is emerging as a powerful tool to meet this challenge, offering the potential to make mining smarter, safer, and more sustainable. For the Yukon, AI could help unlock remote resources, prevent environmental disasters, and improve operational efficiency. Rolling out AI tools is not without its challenges, including availability and quality of data for training AI algorithms, as well as finding or training appropriate staff.

There is also a caveat: if deployed without ethical and inclusive governance, AI could deepen inequalities and perpetuate colonial practices. According to Margaret Yun-Pu Tu, a PhD candidate in law at the University of Washington, in her article in Policy Options magazine, “AI technologies often misinterpret or ignore Indigenous cultures, languages and contexts. [AI] is a potential new form of colonization – one that risks marginalizing their languages, cultures and agency unless meaningful safeguards are established”.

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How AI is Transforming the Mining Lifecycle

AI is already reshaping mining around the world, and Canada is no exception. Its applications span the entire mining value chain, from exploration and drilling to maintenance, logistics, and environmental monitoring. AI-powered platforms using satellite imagery, geophysical surveys, or historical drill data to identify promising mineral targets. These systems can detect subtle patterns invisible to human analysts, speeding up discoveries and reducing environmental disruption.

 Yukon Metals makes use of AI tools for exploration activities.

 According to Jim Coats, an Executive VP with the firm, “AI can more rapidly find a place that has the right conditions to have minerals under the surface”. On the drilling side of things, Coates says that “incorporating AI can be less environmentally impactful than traditional methods. Eventually you may be able to reduce your drilling effort significantly”.

Alpha-EL is an Indigenous-owned Yukon-based company using AI for environmental monitoring purposes. Their CORAX VISION AI System is a portable 360-degree remote viewing camera system backed by artificial intelligence.  It tracks objects and can provide real-time situational awareness of wildlife and wildfire. For example, the system can be used for caribou identification for regulatory reporting purposes. John Jensen, the company CEO and president stated “traditional methods—camera traps and aerial surveys are expensive, suffer from analysis bottlenecks due to data overload, have inherent detection failures and species bias, and are restricted by habitat accessibility. CORAX VISION improves accuracy, and efficiency and has specialized wildlife-specific AI models”.

Andrew Menezes, an Environmental Manager with Casino Mining, stated that “AI-backed camera systems provide real-time monitoring, reducing the need for expensive aerial surveys and invasive methods. They can detect individual animals, provide more accurate data and enable real-time decision-making”. Autonomous hauling trucks and smart crushers and mills us AI—it enables machines to make decisions based on real-time data, reducing the need for human intervention in hazardous environments.

AI systems are predictive and can analyze sensor data to anticipate equipment failure allowing for proactive maintenance. This reduces downtime, improves safety, and cuts costs. AI has capabilities to support environmental monitoring—AI tools can integrate data from satellites, and sensors to monitor water quality, emissions, and biodiversity in real-time. These systems offer early warnings of tailings dam failures or other environmental risks. In the Yukon and Territories, several companies are already testing these innovations.

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Lode Gold, Triumph Gold, and Metallic Minerals are using AI for exploration in the Yukon, while Minerva Intelligence and GoldSpot Discoveries have brought machine learning to the region’s geological modeling.

The Government of Canada and Government of Northwest Territories' core scanning initiative (a pilot project to scan, digitize and analyze existing drill cores from the Northwest Territories Geological Survey’s collection) is also laying the groundwork for a more digitized future.

No hallucinations

Given that ChatGPT and similar tools have been known to make up facts, give contradictory information, insist that 2+2=5, or suggest that you clean rice with bleach to clean it before cooking, you can be forgiven for wondering if using AI tools used in the mining industry might be a really bad idea.

The AI used in mining operates very differently. These systems don’t “chat” or write essays; instead, they focus on highly specific tasks like analyzing sensor data, detecting anomalies, or optimizing drilling patterns. Importantly, they are not prone to the kind of “hallucinations” language models can produce—where the AI makes up plausible sounding but false information. In mining, AI systems are typically grounded in real-time data, physics-based models, or tightly constrained training sets. Their success depends not on creativity, but on accuracy, reliability, and the ability to spot patterns difficult for human analysts to detect (e.g. because of the amount of data involved, or the speed of analysis).

Could AI have helped prevent the June 2024 heap leach failure? AI systems could have synthesized real-time data (Forbes: AI Unearths New Potential In The Mining Industry) from geotechnical sensors, identified warning signs of instability, and triggered alerts. Advanced anomaly detection algorithms might have picked up subtle changes in drainage, material behavior, or ground movement that human monitoring missed. Predictive models could have simulated scenarios under stress and flagged risk days or weeks ahead.

Data Sovereignty: Whose Data Fuels AI?

AI systems depend on massive datasets to function—but who owns the data? For Indigenous communities in Canada, this question isn’t abstract. It's about governance, self determination, and consent. Indigenous Data Sovereignty is the principle that Indigenous peoples have the right to control data generated on or about their communities and territories.

 Yukon First Nations have taken proactive steps in this space. Former Vuntut Gwitchin chief Dana Tizya-Tramm has called for an AI network to support land-use planning. The Council of Yukon First Nations (CYFN) has established a data initiatives team, and organizations like Nadlii are promoting ethical AI and IDSov practices, which includes meaningful engagement and collaboration with Indigenous communities throughout the AI development process.

AI tools can support indigenous group in understanding the data collected. According to Coates, “AI tools can provide the data in a format that is consumable by indigenous groups, who may have various interests such as wildlife, ice, or permafrost.”

The AI Gap: Barriers in the North

Despite the potential, AI adoption in the North faces formidable hurdles. Many northern sites lack high-speed internet, which hampers cloud computing and real-time analytics, though satellite systems may offer workarounds. The upfront investment in sensors, computing hardware, and software licenses may push these tools out of reach, especially for smaller operations. Older mines may lack clean, digitized data. Inconsistent or siloed records make it difficult to build reliable AI models. According to Reuters, AI deployment “requires skillsets in data science, machine learning, and digital [literacy]”.

The Yukon lacks access to training programs or specialists, making some AI deployments impractical. It’s also moving very fast. According to Coates, “The main barrier to adoption is that the technology is changing so rapidly”. A Smarter Future—On Our Terms AI holds the key to a safer, more sustainable future. One could argue that it is no longer optional for the mining sector in Canada’s North. But technology alone won’t cut it. The region must embrace inclusive innovation—one that respects Indigenous sovereignty, invests in local skills, and ensures the benefits of AI to all.