Stop Digging: Why AI and Machine Learning Are Making Traditional Mining Obsolete.

The global mining industry faces relentless pressure to "do more with less," struggling to meet massive material demands while constrained by costs, resource scarcity, and safety imperatives. In response, a powerful new technological revolution is sweeping through the sector: Artificial Intelligence (AI). This is not merely an incremental improvement; it is a fundamental redefinition of how minerals are found, extracted, processed, and transported. Technologies like machine learning (ML), deep learning (DL), and Generative AI (Gen AI) are moving rapidly from experimental concepts to essential tools. As stated by S&P Global in February 2025, it is now "nearly impossible to envision a significant-scale mining operation that does not incorporate technology at some stage of the mining value chain".

AI is accelerating change across the entire mining process—from enhancing mineral discovery and enabling autonomous operations to predicting catastrophic equipment failures and optimizing resource flow. This "democratization of AI" is bringing the workforce closer to crucial data, enabling organizations to extract greater value from their operations than ever before. This technological shift is not optional; it is the necessary foundation for efficiency, safety, and productivity in the modern era.

The New Gold Rush: Smarter Exploration and Discovery

The search for new mineral deposits has traditionally been a time-consuming and costly endeavor. AI is fundamentally changing this by injecting speed and precision into mineral exploration.

When treated as part of a systematic approach, AI algorithms leverage vast datasets of precompetitive geoscience information to identify subtle patterns that may indicate valuable mineralization. This powerful analytic capability helps geologists better inform their exploration programs, generating significant savings in both cost and time. Specifically, AI helps "hone drill programs," eliminating targets with a low probability of returns, thereby ensuring companies spend money drilling where success is most likely.

Major players, including BHP and Ivanhoe Electric, are already harnessing this potential. BHP has utilized ML, combined with human expertise, to successfully discover new copper deposits in both Australia and the USA. Meanwhile, Ivanhoe Electric is using ML to accurately detect the presence of sought-after sulphide minerals—including copper, nickel, gold, and silver—at depths exceeding 1.5 kilometers. This technology is integrated with Ivanhoe Electric’s proprietary electrical geophysical surveying transmitter, named Typhoon, which offers high precision deep underground while minimizing land disturbance. This ability to see deeper and more accurately, coupled with unprecedented speed, makes AI the new essential tool for mineral discovery.

Automated Operations: The Driverless Mine

Perhaps the most visible sign of AI’s revolutionary impact is the rise of autonomous and remote operations. This allows companies to increase safety by taking truck operators out of harm’s way, reducing the risks associated with working around heavy machinery.

Rio Tinto, an early adopter, has deployed AI extensively in its Western Australia iron-ore operations. Since 2019, its autonomous rail system, AutoHaul long-distance trains, have traveled over 7 million kilometers. Furthermore, Rio Tinto operates a fleet of more than 130 autonomous trucks managed by a central controller. This autonomous haulage system (AHS) uses predefined GPS courses, automatically navigating roads and intersections, and constantly pinpointing the location, speed, and direction of all vehicles.

The economic benefits are stark: Rio Tinto estimated that, on average during 2018, its autonomous trucks operated 700 hours more than conventional trucks, achieving 15% lower costs, demonstrating clear productivity gains.

Underpinning this massive level of automation is Rio Tinto’s Mine Automation System (MAS), a network application that pulls data from 98% of the company's sites. The MAS uses sophisticated algorithms to provide this information in a common format. Critically, because autonomous equipment is often made by different manufacturers, the MAS enables these different systems to work together. Rio Tinto uses AI within MAS to automatically generate orebody models, organize equipment dispatch, and predict and control blasts. They have even optimized the speed and reduced queuing of autonomous trucks, noting that these seemingly minor adjustments produce "significant gains in productivity".

The Pit-to-Port Perfection

The use of AI extends far beyond the mine pit, optimizing the entire supply chain—a complex feat humans cannot manage alone.

BHP credits AI as a key factor in making its Western Australia Iron Ore (WAIO) business one of the world’s lowest-cost major iron ore producers. WAIO is an enormously complex operation connecting multiple mines, rail systems, and a port. All these "touchpoints" are controlled through a remote operations center. BHP uses AI as a decision support system, aiding team members who make the ultimate decisions with powerful computer processing ability.

At Port Hedland, BHP’s export facility uses eight automated ship loaders, operated remotely from Perth, which handle 1,500 bulk ore carriers annually. By automating these ship loaders, BHP calculated an increase in production of more than 1 million metric tons per year. This increase resulted from greater precision, reduced spillage, faster load times, and equipment optimization.

Australian producer Fortescue Metals Group (FMG) also uses AI to orchestrate its integrated supply chain from the pit to the port via its state-of-the-art control center, The Hive. The Hive remotely manages autonomous mining equipment, including over 200 haul trucks and 4,000 ore cars. The integration of advanced AI functions into FMG’s process control systems continuously monitors hazards and optimizes the scheduling of critical tasks, such as synchronizing train control and scheduling decisions, enhancing efficiency and safety.

Simulation and Strategy: The Digital Twin

A cornerstone of AI integration is the digital twin—a virtual replica used to model, simulate, and predict operational outcomes. Digital twins allow companies to test the potential impacts of different strategic decisions before implementing them in real life.

BHP uses digital twins combined with Gen AI across its operations, including at BMA and Copper South Australia. By modeling the BMA value chain, for instance, proprietary tools using AI and advanced analytics were able to predict operational performance and identify improvements that led to an increase in productive movement by 10% per annum.

Gen AI has democratized access to these powerful models. Non-technical users can now perform scenario analysis and design decisions using natural language queries. For example, a user can simply ask: "What are the realistic production ranges over the next five years?" or "How does the value chain bottleneck shift?". This integration provides faster insights and uncovers hidden performance improvement opportunities.

Digital twins are also crucial in metallurgical processes. Newmont’s Lihir gold plant in Papua New Guinea implemented Metso’s Geminex metallurgical digital twin to optimize material flow and manage the variability of the ore feed. By combining physics-based models with AI-driven ML algorithms, the Geminex system continuously adapts to maximize plant performance.

Looking ahead, Gen AI promises to revolutionize mine design itself. Instead of engineers spending months or years optimizing a design, Gen AI could rapidly explore countless possibilities, potentially "inventing" designs that humans might have missed, dramatically accelerating the development and commissioning of mine sites. Furthermore, Canadian miner Teck Resources is working with Skycatch to implement AI-powered digital twins across its global mine sites to quickly contextualize environments and enable faster operational decisions.

Predictive Power: Making Maintenance Smarter

Maintenance costs for mobile and fixed equipment typically account for a staggering 30% to 50% of a mine’s operating expenditure. AI and ML algorithms are tackling this expense head-on by enabling predictive and prescriptive maintenance approaches.

Teck Resources shifted from traditional maintenance methods (like oil checks and vibration monitoring) to leveraging digital signals and ML models to anticipate equipment failures. Using AspenTech’s Mtell solution, Teck found that ML could predict equipment failures that were not obvious or were complex. For instance, Mtell accurately identified impeller clogging issues in a filter feed pump very early on.

By analyzing the millions of data points generated by its mobile fleets globally, Teck uses ML and AI to "predict the unpredictable" and fix problems before they happen, such as electrical failures. The company estimates that this predictive maintenance program holds the potential for more than $1 million in annual savings at just one site, minimizing unplanned maintenance and extending equipment life.

Scaling Value and Protecting Resources

The real financial power of AI is realized when models are built in a modular way that allows them to be adapted and scaled across an entire enterprise.

At Freeport-McMoRan’s Bagdad mine, custom-designed AI models were built to analyze second-by-second performance readings from equipment sensors. Instead of running the processing plant at a single setting all day, the AI enabled operators to adjust settings every hour to maximize production based on the specific type of ore being processed. This quickly boosted production by 5% to 10%.

When Freeport scaled this transformation across all seven of its mines in the Americas, the results were monumental: the company estimates it increased its annual copper production across all sites by 200 million pounds, realized a $350 million to $500 million uplift in EBITDA, and saved $1.5 billion to $2 billion by avoiding the necessity of building a brand-new processing facility.

Finally, AI is critical for sustainability goals. ML algorithms analyze large volumes of operational data to improve the efficiency of energy and water usage. For example, at BHP’s Escondida mine in Chile, AI technology has saved more than 3 gigalitres of water and 118 gigawatt hours of energy since 2022, primarily by optimizing the mine’s concentrator and desalination plant using real-time data analytics.

The integration of AI, ML, and Gen AI is no longer a futuristic vision; it is the current operational reality for leading mining companies. From the deep search for new minerals to the remote optimization of port logistics, AI is proving to be the most critical competitive advantage. By automating routine tasks and providing data-driven insights in microseconds, AI empowers human teams to focus on higher-level problem-solving and strategic decision-making, cementing its role not just as a support tool, but as the engine driving the industry's inevitable transformation.