Artificial intelligence (AI) is already proving useful in mining, particularly for predictive maintenance, with algorithms that can detect abnormal equipment behavior, forecast failures, optimize haulage and identify patterns across production and safety data. However, a warning that a bearing may fail in ten days has limited value if it does not influence the maintenance schedule, reserve the right component and account for the effect of downtime on production.
This gap between insight and action is becoming more important as investment accelerates. GlobalData analysis forecasts that mining industry spending on AI will rise from $2.7 billion in 2024 to $13.1 billion in 2029. Capturing a return on that investment will require operators to move beyond isolated analytics tools and connect AI to the systems through which mines are managed.
The next phase will be defined less by whether a model can make an accurate prediction and more by whether the wider operation can respond to it. The progression runs from monitoring and prediction to recommendation, orchestration and, eventually, bounded autonomous control.
What AI already does well in mining
Mining offers fertile ground for AI because modern operations generate large volumes of equipment, geological and process data. AI can support decisions across the mining value chain, using information from sensors and monitoring systems to improve efficiency, cost, safety and environmental performance.
Predictive maintenance is among the most mature applications, using machine-learning models to analyze variables such as vibration, temperature, pressure, electrical current and lubricant condition to identify equipment degradation before a conventional alarm threshold is breached. A 2025 systematic review of AI-driven predictive maintenance in mining examined 166 studies and identified growing use of machine learning, the Internet of Things and digital twins for early fault detection and intelligent asset management.
The shift toward actionable maintenance
Most predictive maintenance systems focus on forecasting failures, but a valuable system should also estimate the remaining useful life of the asset, assess the consequence of failure, and recommend when intervention should take place. It should determine whether the required parts are available, whether appropriately qualified personnel are on shift and whether the work can be combined with another planned shutdown. It should also show planners how acting immediately, deferring the repair or changing the machine’s operating profile would affect production risk.
Furthermore, digital twins provide a controlled environment in which recommendations can be tested before they affect physical equipment. A mining digital twin combines a virtual representation of an asset or process with continuously updated operational data, analytics and simulation. It can be used to test maintenance windows, process set-point changes, equipment outages and safety responses under different conditions.
This marks the transition from predictive to prescriptive maintenance. Rather than adding another alert to an already crowded dashboard, the AI becomes part of the maintenance workflow. Human oversight will remain essential for failures with serious safety or production consequences, and engineers will have a prioritized, evidence-based course of action, eliminating the manual work required to assemble information from separate systems.
The practical barriers to scaling mining AI
The mining industry is increasingly embracing digital technologies and AI-assisted tools. However, there are still challenges to overcome:
● Uneven connectivity: Real-time data requires consistent and reliable connectivity. This can be difficult at mining sites, especially during deep excavation or at remote locations.
● Operating sensors in adverse environments: Dust, water, vibration, temperature variation and physical impacts can create noisy signals, sensor drift or complete data loss.
● Divided data and information silos: Maintenance teams, processing plants, mine planning departments and original equipment manufacturers frequently use different asset names, data structures and time horizons.
● Cybersecurity requirements: The risk changes when AI moves from reading operational data to writing instructions back to equipment or control systems. The US National Institute of Standards and Technology’s Guide to Operational Technology Security emphasizes that operational technology has distinct reliability, performance and safety requirements.
● Workforce readiness: Engineers and operators must understand what a model is recommending, what data it used and when its output should be challenged. That requires hands-on involvement during design and validation, rather than training delivered shortly before launch.
Carroll Technologies Group and the infrastructure behind mining automation
Moving from AI insight to operational action requires reliable communications, trustworthy field data, and interfaces that connect monitoring systems with workers, control rooms, and equipment.
Carroll Technologies Group is a supplier of communications, monitoring, safety, and control technologies for mining and other industrial operations. Working with a broad network of specialist manufacturers, the company integrates and supports systems tailored to the demands of harsh operating environments.
Underground connectivity must support the transfer of voice, data, and alerts in areas where conventional communications are difficult to maintain. Sensors and monitoring equipment must continue operating despite dust, water, vibration, and other harsh conditions. Information from personnel tracking, equipment location, and atmospheric monitoring must also be consolidated to give operational teams a coherent view of conditions across the mine. Carroll’s underground communications portfolio combines voice and data communications with location tracking and atmospheric monitoring. Its Central Communication Center brings information from connected mine systems into a command-and-control environment, allowing operators to monitor personnel locations, environmental conditions, and communications status through a common interface. The center can also provide redundant command posts capable of operating independently during an emergency.
Carroll-supplied communication and monitoring products include:
- PBE Axell leaky feeder systems, which provide radio and data communications above and below ground.
- PBE Axell Page Boss mine phones, including the Model 112 GEN II, Model 112S/S GEN II, Model 111, Model 118, and Model 119.
- PBE Axell MineBoss 2.0 and Belt Boss systems, used for atmospheric, conveyor, fire suppression, dust suppression, fan, airflow, and related monitoring and control applications.
- PBE Axell PAS-ZR proximity alert system, a ruggedized collision-avoidance solution designed for surface and underground vehicles.
- Sybet portable underground communications systems, including SWAR 2EX and SpellCom wireless networks for mines, tunnels, and rescue operations.
- Nerospec vehicle intervention systems, which can integrate with third-party proximity detection and collision-avoidance technologies to control or safely stop mobile equipment.
Carroll supplies tracking and proximity technologies that can identify developing interactions between personnel and machinery, while its work with Nerospec USA extends this approach into controlled vehicle intervention. Rather than limiting the response to a warning, an intervention system can slow or stop equipment when predefined risk conditions are reached. Nerospec’s technology is designed for mixed fleets and can integrate with third-party proximity detection and collision-avoidance systems, supporting incremental deployment without requiring operators to replace every machine.
Carroll Technologies also provides independent advice and can combine technologies from multiple vendors into a customized solution. This is particularly relevant to mines that have accumulated different generations of communications, monitoring, and safety equipment, as scaling digital systems frequently requires new technology to operate alongside existing infrastructure.
Implementation does not end when the equipment is installed. Communications failures, damaged sensors, and unavailable spare parts can quickly undermine the reliability of a connected mining system. Carroll provides network design, installation, training, technical support, servicing, and repair, backed by around-the-clock assistance and a network of distribution and support centers across North America. This service capability helps operators keep integrated systems functioning after the initial deployment.
As AI applications become more closely integrated with production and safety workflows, this supporting infrastructure will be central to converting predictions into dependable operational action.
