AI in Mining Industry: 8 Real Uses With Fleet Data

AI in the mining industry is no longer a lab idea. The clearest proof is haulage: the number of autonomous haul trucks working at mines worldwide rose from 2,080 in July 2024 to 3,832 in July 2025, an 84% rise, according to GlobalData figures reported by Mining Technology. Beyond driverless trucks, artificial intelligence now runs autonomous trains, predicts equipment failures, flags hazards around mobile equipment, ranks exploration targets from satellite and survey data, and tunes grinding circuits to cut energy peaks. This guide covers eight real uses, the fleet and safety data behind them, what a project costs and pays back, and how a mine owner can start one without betting the operation on a vendor’s slide deck.

Who this is for: owners and managers of mining businesses, from a single quarry or small open pit to a multi-site producer. Every figure below carries its source and date. Where a number comes from a company press release rather than an independent study, we say so, because vendor results at one site rarely transfer unchanged to another.

AI in the mining industry at a glance: eight uses compared

Artificial intelligence (AI) here means software that learns patterns from data and then makes a prediction, a classification or a control decision. Machine learning (ML) is the most common type in mining: a model is trained on past sensor readings, assays, images or survey grids, then applied to new data. Some uses, such as driverless trucks, combine ML with older rule-based automation, GPS and radar. The table sorts the eight uses by how far they have spread across real mine sites, based on the evidence cited in each section.

How we chose and ordered these uses

We included a use only if we could find a named mine, a regulator dataset or a peer-reviewed paper showing it in real operation, not just a concept. We ordered the list by breadth of real deployment: how many sites and machines use it, as far as public data shows. We left out claims we could not trace to a source, including general “percentage savings” figures that appear on many websites without a study behind them. Vendor results are reported as vendor results, with the company named.

▶ What an AI mining app does and who makes one · 3:52

1. Autonomous haul trucks: the most proven AI in mining operations

Driverless haul trucks are the use with the most public evidence. A central control system assigns each truck a route and a task, and the truck uses GPS, radar, lidar and onboard computers to drive, load, haul and dump without anyone in the cab. People still plan the mine, maintain the trucks and supervise the system from a control room.

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Autonomous haul trucks in operation by country

Number of trucks, July 2024 vs July 2025

Komatsu said in April 2026 that it had commissioned its 1,000th ultra-class autonomous truck fitted with its FrontRunner system, a 930E-5AT with a 290-tonne payload working at Nevada Gold Mines in the United States, and that FrontRunner trucks have moved more than 11.5 billion tonnes of material since the system was first sold in 2008 (Komatsu, April 2026). Nevada Gold Mines is the joint venture run by Barrick with Newmont as partner. Caterpillar, the other long-standing supplier, sells its system as Cat MineStar Command for hauling.

GlobalData’s July 2025 count shows how fast adoption has spread, and where. China moved from 562 autonomous trucks in July 2024 to 2,090 a year later, mostly at coal mines. Australia rose from 927 to 1,024. Canada had 344 and Chile 208 in July 2025. Caterpillar, Komatsu, Tonly and LGMG together supplied 88% of the trucks GlobalData tracks (Mining Technology, GlobalData).

Largest autonomous truck fleets by operator

The biggest single fleets now belong to Chinese coal producers. GlobalData lists CHN Energy with 509 autonomous trucks, Guanghui Energy with 420, BHP with 360 and Rio Tinto with 305. The Baishihu coal mine holds the largest single-site fleet, at 420 trucks. GlobalData expects the global total to approach 5,000 by the end of 2030, with Fortescue’s agreement with Liebherr for 360 battery-electric autonomous trucks among the large additions (Mining Technology, GlobalData).

What operators report from driverless fleets

Operator results are the closest thing to real-world performance data, but they are company statements, not audited studies. Fortescue said autonomous haulage raised productivity at its Pilbara iron ore sites by 30%, and that its fleet of 183 trucks across the Solomon and Chichester hubs had driven more than 52 million kilometres (Mining Technology). Rio Tinto said in 2018 that each autonomous truck ran about 700 more hours than a staffed truck in 2017, at about 15% lower load and haul unit cost, and that no injuries had been attributed to its autonomous trucks since deployment (Global Mining Review).

Gold miners came later. Newmont approved a US$150 million autonomous haulage investment at Boddington in Western Australia in 2020 and said it expected an internal rate of return above 35% and a two-year extension of mine life (Canadian Mining Journal). That expected return was a forecast at approval, not a measured result.

Where autonomous haulage fits, and where it does not

Autonomy pays best where many large trucks run the same haul roads around the clock: big open pits in iron ore, coal, copper and gold. The extra hours come from removing shift changes, breaks and fatigue, and from steadier driving that is gentler on tyres and engines. A small pit with five trucks on one shift has far fewer hours to gain, and the fixed costs of the control system, mine-wide wireless network, survey and change management stay high. Smaller operators are looking at retrofit kits for existing trucks and at autonomy in quarries; GlobalData notes Heidelberg Materials agreed to deploy Pronto’s system on more than 100 quarry trucks over three years. Underground loaders (LHDs) are also automated at many sites, often with an operator supervising several machines remotely.

2. Autonomous trains and drilling

Rail is the second large-scale example. Rio Tinto ran the first fully autonomous heavy-haul ore train in the Pilbara in 2018. Its AutoHaul network carries ore on more than 1,700 kilometres of track from 16 mines to four port terminals, using about 200 locomotives (Rio Tinto, 2018). The gain is less about labour and more about consistency: trains run to a timetable set by software, which smooths deliveries to the port and cuts the long crew drives between remote stations.

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Largest autonomous truck fleets by operator

Autonomous haul trucks in service, July 2025

Autonomous drill rigs work on the same idea at the bench. A drill reads the blast plan, moves to each hole, drills to depth and records penetration rate, torque and vibration as it goes. Those drilling signals are useful data in their own right: they show hard and soft zones that geologists and blast engineers can use to adjust explosive loading, and they can feed ore and waste models. For a primer on rig types, see our guide to types of drilling rigs in mineral exploration, and for the wider machine fleet our list of top mining equipment and machinery.

3. Predictive maintenance: AI for mining equipment reliability

Predictive maintenance is the use most mine owners can start with, because it runs on data many sites already collect. Haul trucks, crushers, mills, pumps and conveyors produce streams of vibration, temperature, oil-condition, pressure and load readings. A model learns what normal looks like for each machine and flags patterns that came before past failures, such as a bearing that runs a little hotter each week or a gearbox whose vibration spectrum starts to change.

The point is to move from two old methods, fixed service intervals and run-to-failure, to servicing based on measured condition. Fixed intervals waste parts that still had life left. Run-to-failure causes unplanned stops, which in mining are expensive because one broken mill or shovel can idle a whole circuit or fleet. A good predictive system lets planners order parts and book crews before a failure, rather than after.

What predictive maintenance needs to work

• Clean failure history. Models learn from past breakdowns. If work orders do not record which part failed and why, the model has little to learn from.
• Enough sensors on the right parts. Many older machines have only basic alarms. Adding vibration and oil sensors to the few assets whose failure stops production often gives most of the value.
• Connectivity. Data must reach a server or the cloud. Underground and remote pits often need a network upgrade first.
• Planners who act on alerts. An alert that no one reads is noise. Agree in advance who checks alerts and how fast.

Published savings vary widely from site to site, so treat any single percentage as a vendor claim until you see results on your own equipment. A pilot on one critical asset class, such as mill bearings or haul truck engines, for six to twelve months is the usual way to test value before a wider rollout. For fleet tracking that sits alongside maintenance data, Farmonaut also offers fleet management tools.

4. AI for mine safety and worker protection

Safety is where AI could matter most to people on site. In the United States, the Mine Safety and Health Administration (MSHA) recorded 28 mining fatalities in fiscal year 2025, down from 41 in fiscal 2023, across about 12,568 mines and 327,465 miners (MSHA At a Glance, FY1978-2025). The chart below shows how the count has moved over ten years.

Chart

US mining fatalities by fiscal year

All US mines, deaths per fiscal year

Powered haulage, meaning mobile equipment such as trucks, loaders, shuttle cars and locomotives, is the largest single cause. MSHA says powered haulage accidents caused 13 of the 33 US mining deaths it counted in 2025 (39%) and 426 of 4,792 nonfatal injuries (MSHA, Powered Haulage Safety). MSHA’s totals differ slightly between pages because some use the fiscal year and others the calendar year.

How AI tools reduce haulage and site risks

• Collision awareness and avoidance. Cameras, radar and tags around a vehicle detect people and other machines, and AI image models classify what they see. The system warns the driver and, on some machines, slows or stops the vehicle.
• Fatigue and distraction monitoring. In-cab cameras track eye closure and head position and alert the driver and the control room.
• Removing people from the danger zone. Driverless trucks and remote-controlled underground loaders take operators out of the riskiest spots, such as unsupported ground and blast areas.
• Ground and slope monitoring. Radar and sensor data on pit walls and tailings dams are analysed for movement trends that come before failure.
• Gas and dust analytics. Underground sensor networks detect rising gas or dust and trigger ventilation changes or evacuation alerts.

Regulators now expect mobile equipment risk to be managed in writing. MSHA’s final rule on surface mobile equipment requires operators to keep written safety programs for mobile equipment at surface mines and surface areas of underground mines, and names powered haulage as a leading cause of serious and fatal accidents (MSHA). Collision avoidance and fatigue systems are among the technologies an operator can evaluate as part of that program. For a wider list of tools, see our guide to mine safety solutions and innovations.

5. AI in mining exploration: finding targets faster

Exploration is where AI could change the economics of the industry the most, because most exploration spending goes on ground that never becomes a mine. AI models in exploration combine many layers of data at once: geological maps, geochemistry from soil and rock samples, magnetic and gravity surveys, drill logs and satellite imagery. They then rank each cell of ground by how closely it resembles places where deposits are known. The output is a prospectivity map, a ranked list of targets for field checks and drilling.

The US government is testing this at national scale. The USGS, DARPA and ARPA-E run a programme called CriticalMAAS (Critical Mineral Assessments with AI Support). USGS staff say a traditional resource assessment takes about two years for one deposit type in one area. DARPA reports that since February 2024 the programme has run about a dozen pilot assessments, and that hackathons showed the AI tools could shorten the assessment workflow to about two and a half days (DARPA, 2025). Part of the work is simply reading old scanned geological maps into usable digital data, a slow manual task that machine learning speeds up. The results still go through USGS scientific review.

In the private sector, KoBold Metals, a company backed by investors including Bill Gates, uses AI models to guide exploration. It announced a large high-grade copper discovery at its Mingomba project in Zambia in February 2024 and said it aimed to start producing copper there within 10 years (CNBC). The discovery was confirmed by drilling; the AI narrowed where to drill.

Limits of AI in exploration

A prospectivity map is a set of exploration targets. It is not a resource estimate and it does not prove a deposit. Models are only as good as their training data, and for rare deposit types there may be few known examples to learn from. Ground truth through mapping, sampling and drilling is still required, and any resource that is reported to investors must follow the reporting code of the relevant market. For background on the deposit models these tools look for, see our guide to types of ore deposits, and for a look at another new imaging method, muon tomography in mineral exploration.

▶ AI in mineral exploration: does it actually find mines? · 4:37

6. Satellite and geospatial AI for mine monitoring

Satellites give a view of every mine and licence area on Earth, repeated every few days, without anyone on site. Machine learning turns that imagery into maps of pits, waste dumps, tailings dams, water ponds and cleared land. A study in the journal Communications Earth and Environment mapped 74,548 mine-area polygons covering about 65,585 square kilometres worldwide from high-resolution satellite imagery (Tang and Werner, Nature portfolio, 2023). The same methods let a company track how fast its own footprint grows, check rehabilitation, or watch for illegal mining on its ground.

For exploration, multispectral sensors such as Sentinel-2 and ASTER record light in bands that respond to iron oxides, clays and other alteration minerals that often surround ore deposits. Radar and elevation data add structure: faults, folds and drainage. AI combines these layers into target maps for early-stage screening, before money goes on field crews and drilling.

Farmonaut is one provider in this space: it screens licence areas from satellite data and delivers ranked exploration targets, usually within 5 to 20 business days from a KML, KMZ or polygon boundary. Results are early-stage exploration targets that come before field work and drilling, not a resource estimate. Developers can also pull Farmonaut satellite data through its satellite API and the weather and satellite API developer documentation.

7. AI in processing plants and energy use

Processing plants were among the first parts of a mine to use advanced control, and AI now extends it. Crushing, grinding and flotation circuits have many settings, such as feed rate, mill speed, water addition and reagent dose, and ore changes hour by hour. Models predict how the circuit will respond and adjust settings to hold throughput and recovery steady.

Energy is a large prize. A 2025 review in Frontiers in Energy Research describes mining as one of the most energy-intensive sectors, responsible for over 10% of global industrial energy consumption, and reports that machine learning models have been used to predict energy peaks in ore processing and to schedule loads (Frontiers in Energy Research, 2025). Grinding is usually the largest energy user in a concentrator, so even small gains in mill control matter.

Sensor-based ore sorting is another processing use. Sensors such as X-ray transmission, laser or colour cameras scan rock on a conveyor, and a model decides in milliseconds whether each piece is ore or waste; air jets then push waste aside. Rejecting waste before grinding saves energy, water and tailings space. It works best where ore and waste look different to the sensor, and must be tested on each ore type.

8. AI for environment, water and tailings

Mines manage water, dust, land disturbance and tailings, and each produces monitoring data. AI is used to forecast water balances, detect seepage or abnormal pond levels, track dust from satellite and ground sensors, and monitor tailings dam movement. Our guide to tailings management software, cost and safety covers that area in depth, and our overview of the effects of mining on the environment explains the impacts these tools are designed to track.

Satellite imagery is also used to measure rehabilitation after mining: vegetation indices show whether replanted land is recovering, which supports closure reporting. The same land restoration methods are used outside mining; see this guide to ecological restoration. Companies that report emissions can pair operating data with carbon footprinting tools. For examples of companies that publish environmental performance, see our list of the most sustainable mining companies.

Traceability, compliance and reporting

Buyers of metals increasingly ask where their supply comes from and how it was produced. Traceability systems record each step from mine to smelter to manufacturer, sometimes on a shared ledger such as a blockchain so that records cannot be changed later. AI helps by reading documents, matching shipments and flagging gaps. Today this is driven mainly by buyers in electronics, batteries and cars, and it is still early: most systems are pilots or cover a few supply chains. Farmonaut’s product traceability tool is one example of this kind of record keeping. Further down the chain, copper rod and wire makers face the same buyer questions; see our list of copper rod and wire rod manufacturers in Europe.

Regulatory reporting is a related use. Mines file production, safety, environmental and royalty returns, often by hand from many spreadsheets. Language models and document tools can draft these from source data, but a responsible person must still check and sign each filing.

What an AI project in mining costs and pays back

There is no single price for AI in mining, because a sensor pilot on one crusher and a mine-wide autonomous fleet differ by several orders of magnitude. The useful numbers are the public ones that show scale. Newmont’s Boddington autonomous haulage approval was US$150 million for one large gold mine (Canadian Mining Journal, 2020). At the other end, a predictive maintenance pilot on a handful of critical assets usually costs the sensors, connectivity, software subscription and staff time.

Value comes from a few sources, and a business case should name which ones it relies on:

• More operating hours from equipment that runs through shift changes or breaks down less often.
• Lower unit cost per tonne moved or processed, from steadier operation, less fuel and longer tyre and component life.
• Fewer incidents, which protects people first and also avoids stoppages, investigations and penalties.
• Better targeting in exploration, which cuts metres drilled on poor ground.
• Lower energy use in grinding and pumping.

The calculator below gives a rough first estimate for the most common case, extra operating hours on a haul fleet. Use your own fleet data. Rio Tinto’s 2017 figure of about 700 more hours per autonomous truck (Global Mining Review, 2018) is one large-mine example, not a benchmark for smaller sites.

How to start an AI project in mining operations

Most AI projects that fail in mining fail on data and people, not on the model. These steps keep a first project small enough to test and clear enough to judge.

Infographic

Starting an AI project in 6 steps

1. 1Pick one problemName a costly issue and its baseline number.
2. 2Audit dataCheck sensors, logs, assays and maps.
3. 3Fix connectivityGet data to one place the team can query.
4. 4Run a pilotSet a period, a success measure and a control.
5. 5Train peoplePlanners and geologists must use the output.
6. 6Scale what workedRoll out only after the pilot beats baseline.

1. Pick one costly problem. Choose a single, measurable issue such as unplanned mill stops, truck idle time, poor drill targeting or energy peaks. Write down today’s baseline number.
2. Audit your data. Check what sensors, logs, assays and maps already exist, how far back they go and how clean they are. Missing failure records or unlabelled drill data are common blockers.
3. Fix connectivity and storage. Make sure data can leave the machine or plant and land in one place that the team can query.
4. Run a pilot with a clear test. Set a period (often six to twelve months), a success measure and a control, such as comparable equipment without the system.
5. Train the people who act on it. Maintenance planners, dispatchers and geologists must trust and use the output. Involve them from the start.
6. Scale only what worked. Roll out to more assets or sites only after the pilot beats the baseline, and keep measuring.

Questions to ask an AI vendor

• Which mines use this today, at what scale, and can we speak to one of them?
• What data does the system need from us, and who owns the data and the trained model?
• How does the model explain an alert or a target, so our engineers can check it?
• What happens when the network drops or a sensor fails?
• What is the full cost over five years: hardware, software, integration, training and support?
• How will we measure success, and what happens if the pilot misses the target?

Challenges and risks of AI in mining

Data quality. Mines often hold decades of data in different formats, units and systems. Cleaning and linking it can take longer than building the model.

Connectivity. Autonomous fleets and real-time monitoring need reliable private wireless networks across a pit or underground, which is a large cost on its own.

Skills and jobs. Automation changes roles: fewer drivers, more controllers, technicians and data staff. Companies that retrain local workers and explain changes early tend to keep community support.

Cybersecurity. Connected and autonomous equipment can be attacked. Control systems need network segmentation, access control and tested manual fallback.

Model risk. A model trained on one ore body or fleet may perform poorly on another. Results must be checked against real outcomes, and humans must keep the authority to override.

Scale. Many published successes come from the largest miners with the largest fleets. Smaller operators should start with uses that need less capital, such as predictive maintenance on key assets, safety systems and satellite screening, before considering autonomy.

The outlook for AI in mining

The evidence points to steady growth in the uses that already work. GlobalData expects the autonomous truck count to approach 5,000 by 2030, led by Chinese coal mines and large iron ore operations in Australia (Mining Technology). Public geological surveys are testing AI to speed up resource assessments, as the CriticalMAAS pilots show. And regulators, starting with MSHA’s mobile equipment rule, are pushing operators to manage the powered haulage risks that collision avoidance and autonomy address.

For a mine owner, the practical question is not whether AI is coming but which single problem on your site is worth testing first, and whether you have the data to test it. If your next decision is where to explore or which ground to drill, satellite screening is a low-capital place to begin; if it is equipment reliability, start with your highest-cost failure.

Frequently asked questions

How is AI used in the mining industry?

AI is used to drive autonomous haul trucks and trains, predict equipment failures, warn of collisions and driver fatigue, rank exploration targets from geological and satellite data, control processing plants and energy use, monitor tailings and land, and support traceability and reporting. Autonomous haulage is the most widely deployed, with 3,832 trucks in operation in July 2025 according to GlobalData.

Which mining companies use AI?

Large producers such as BHP, Rio Tinto, Fortescue, Newmont and Barrick (through Nevada Gold Mines) run autonomous haulage, and Chinese coal producers such as CHN Energy and Guanghui Energy operate the largest fleets. KoBold Metals uses AI in exploration. Many mid-tier and smaller miners use predictive maintenance and safety systems.

How many autonomous haul trucks are there in mining?

GlobalData counted 3,832 autonomous haul trucks worldwide in July 2025, up from 2,080 a year earlier. China had 2,090, Australia 1,024, Canada 344 and Chile 208.

Does AI make mining safer?

It can reduce exposure to the biggest risks. Powered haulage caused 13 of 33 US mining deaths in 2025, according to MSHA, and autonomy, collision avoidance and fatigue monitoring all target that risk. Rio Tinto reported no injuries attributed to its autonomous trucks as of 2018. Safety still depends on procedures, training and maintenance.

Can AI find new mineral deposits?

AI can rank ground by how likely it is to hold a deposit and so reduce wasted drilling, as the KoBold Metals copper discovery in Zambia illustrates. It cannot confirm a deposit on its own: sampling and drilling are still required, and reported resources must follow the relevant code.

How much does AI cost for a mine?

It ranges from a modest sensor and software pilot on a few machines to large fleet projects; Newmont approved US$150 million for autonomous haulage at Boddington. Start with one problem and a pilot budget, and judge the payback on your own baseline.

Will AI replace mine workers?

Automation removes some driving and manual roles and creates others in control rooms, maintenance, data and systems. The change is gradual and depends on retraining. Most sites still need people to plan, maintain, supervise and make final decisions.

What data does a mine need before using AI?

Clean, time-stamped records of the problem you want to solve: sensor readings and failure logs for maintenance, dispatch and GPS data for haulage, assays, drill logs and survey grids for exploration. Reliable connectivity to collect the data is also needed.

Is AI useful for small mining operations?

Yes, for uses that need little capital: predictive maintenance on the one or two machines whose failure stops production, cameras for collision and fatigue warnings, and satellite screening of a licence area before field work. Full autonomy usually pays only on large fleets.

What is the difference between automation and AI in mining?

Automation follows fixed rules, such as a conveyor that stops when a sensor trips. AI learns patterns from data and makes predictions or decisions in new situations, such as spotting a person in a camera image or forecasting a bearing failure. Autonomous trucks combine both.

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