Op-Ed: Hard rock, smart tools - how AI is rewriting mining 

The pressure to deliver more critical minerals is real. Simultaneously, expectations around safety, cost control, and environmental performance keep climbing. These two demands are what have created the backdrop for the rise of artificial intelligence in mining. What felt distant a few years ago is now in practice on the ground. AI is helping teams make faster decisions, remove people from harm, and keep plants and fleets running efficiently to meet critical mineral demands. This change reaches across exploration, extraction, processing, maintenance, and planning. The common thread is time. Time saved on decisions. Time gained on equipment uptime. Time that lets a supervisor act in the shift, not a week after reports are compiled. When time improves, safety and cost performance usually follow. Operations that learn while they run Modern mines generate vast data streams. A single company can create tens of millions of new data points every week across plants, pits, and support systems. Historically, it took too long to turn that signal into action. Today, machine learning helps operators visualize trend data as it forms. Crews see what is emerging, not just what happened last month. That changes and optimizes how sites run. Real-time analytics now guide in-shift adjustments that cut water use, energy draw, and reagent consumption. Metallurgists do not need to be on-site to add value. With secure remote visualization, a specialist can review mill performance from another location and coach the local team on setpoints and constraints. Control rooms will follow. The goal is clear views first, then safe remote control where it fits. Automation is part of the same story. Consider inspection and condition checks along long, complex crusher corridors. That work is repetitive and often uncomfortable. It also matters. A sensor-laden quadruped robot can patrol those routes on a fixed schedule. It listens for bearing whine and gear noise. It tracks vibration and temperature. It moves through restricted zones without fatigue. The data goes straight into reliability systems. Maintainers get early notice. Operators get a cleaner picture of risk. The result is fewer surprises and more orderly maintenance windows. The biggest risk is not the algorithm, but data quality. Garbage in still means garbage out. AI helps here as well with models that can flag unlikely values and missing tags. Teams still need to clean historic sources, retire bad sensors, and enforce discipline in new ones. That is unglamorous work– it is also the foundation for every gain that follows. Safety and sustainability that scale AI earns its keep fastest when it removes exposure. Autonomy and remote tools reduce the need to climb structures, walk dusty corridors, or enter zones with active equipment. Drones and fixed sensors extend that reach high walls, stockpiles, and tailings embankments. Fewer entries mean fewer chances for slips, trips, and caught-between incidents. Monitoring also improves at the same time. Environmental performance benefits from the same sorts of feedback loops. Real-time anomaly alerts help teams respond to out-of-bounds readings before small deviations become reportable events. Better metering and predictive controls trim power draw during low-value periods and smarter dosing cuts reagent waste. Over time, those improvements add up. The plant runs closer to target with less variance, which saves cost and lowers impact. One key component that people often forget when integrating new AI practices remains crucial: culture matters. People need to be able to trust that new tools are there to protect them and to make work more rewarding. When leaders focus on care, not just compliance, adoption happens faster. Crews will try new methods more willingly when they are confident that it won’t take away their future with the company. Roles will change. Work will shift from routine exposure to higher-skill oversight. That should be communicated clearly as a benefit to employees and a better path for safety and careers, rather than as a threat. Economics that keep sites viable Unplanned downtime is expensive. Predictive maintenance is one of the clearest wins for AI in mining. Models trained on vibration, temperature, pressure, and lube data can spot failure signatures before they are obvious to the human ear. That allows planners to pull equipment at the right moment. Parts are staged. Technicians are scheduled. The line returns to service on time. Uptime improves without over-servicing assets. Along the same vein, labor constraints are real, but automation can help close the gap. Remote monitoring and semi-autonomous systems let a smaller team cover a larger footprint with better information. Generative tools speed up everyday tasks that drain focus. Drafts of RFPs, policy updates, and training outlines can be produced in minutes, then refined by subject matter experts. Engineers can iterate mine planning models faster. The output still needs human review, but the cycle time drops. That is the purpose of automation policies that still require human value input. Not every site can move to full autonomy. Brownfield operations have fixed roads, berms, and plant layouts. Converting to driverless haulage often requires wider roadways and segregated traffic. The cost to rebuild can be prohibitive. That does not block progress. Many sites can still add targeted autonomy in drilling, dozing, or hauling in specific zones. Others can invest first in advanced analytics, remote support, and robotic inspection. The gains are real without a complete redesign. What comes next The direction is set. Adoption will deepen across the value chain. Drilling rigs will take on more of their own control logic. Plants will tune themselves within guardrails set by metallurgists. Maintenance will shift further toward planned interventions driven by health indicators. Translation and field troubleshooting will work across languages in real time. Each step frees people to do higher-value, more rewarding work. Two enablers will decide who benefits most. The first is leadership in culture. Teams move faster when they know leaders care about people first. Trust makes change possible. The second is discipline in data. Good tagging. Clear ownership. Continuous cleanup of legacy sources. Without that discipline, models chase noise, and confidence fades. Industry collaboration will accelerate the curve. Shared safety lessons, open discussions about data architecture, and joint pilots with OEMs can reduce duplication. Professional societies like the Society for Mining, Metallurgy, and Exploration (SME) help convene that work. We can connect operators, vendors, and researchers. We can publish learnings on what works and what does not. We can keep focus on practical steps that raise safety and performance at the same time. This is not about replacing people. In the near term, it will increase demand for people with new skills: data scientists, control engineers, reliability specialists, and frontline leaders who can bridge operations and analytics. Over time, headcount at some sites may stabilize or fall as new designs take advantage of autonomy. Even then, the work will remain. It will shift to planning, oversight, exception handling, and care for the systems that keep a mine safe and productive. The stakes are high. The world needs more lithium, nickel, copper, silver, and other essential minerals for energy and technology. Communities expect safer jobs and better environmental outcomes. Investors expect disciplined spending and steady output. AI helps align those goals. It gives teams the time and insight to act before problems grow. It makes tough, physical work more predictable. It lets leaders run the business on today’s signal, not last quarter’s lagging report. Mining has always rewarded sound judgment and relentless improvement. AI is another tool set that supports both. Use it to see sooner, decide faster, and keep people out of harm. Build a culture that invites change without fear. Clean up the data and keep it clean. Share what you learn. Do that, and the industry can meet rising demand with safer sites, stronger economics, and a smaller footprint. Mick Routledge is Board Director, Society for Mining, Metallurgy & Exploration (SME); Senior Vice President and Chief Operating Officer of Coeur MiningWeiter zum vollständigen Artikel bei Mining.com Weiter zum vollständigen Artikel bei Mining.com