Mining’s role in the global economy is poised to grow because it supplies materials needed to build digital and energy infrastructure. Demand for critical minerals such as copper, lithium, and nickel, the building blocks of electrification, renewables, and battery storage, is projected to grow significantly through 2040, according to the International Energy Agency.1
Despite its importance, mining remains one of the most hazardous industries worldwide, with widely variable fatality rates across regions. Member companies of the International Council on Mining and Metals recorded 42 fatalities and more than 6,400 recorded workplace injuries in 2024.2 Even in highly regulated markets with strict safety standards, eliminating lost-time injuries and fatalities remains challenging.
The consequences of safety failures are far reaching. The human toll of lives lost or affected by injury is devastating and extends beyond the injured worker to families, coworkers, and communities. In addition, incidents carry financial and reputational costs for companies. Catastrophic mining accidents can lead to cleanup liabilities in the billions, production shutdowns, and share price declines. Reputational damage can persist for years, eroding community trust and investor confidence.
Conversely, organizations with strong safety cultures often benefit from a more engaged and stable workforce, better operational discipline, and fewer disruptions from incidents and unplanned downtime—all of which support increased productivity.
Over the past 15 years, technology improvements have led to declines in mining injuries, though the declines in fatalities have recently stalled. In this article, we examine opportunities for mining companies to apply agentic AI tools as a potential additional initiative to improve productivity and safety.
What has held back mining safety transformation?
Mining remains one of the most hazardous industries, with fatality rates that exceed those in transportation, construction, and manufacturing (Exhibit 1).
Mining is one of the most hazardous industries globally, with fatality rates second only to agriculture.
Note: Data based on Australian safety records and the US Bureau of Labor Statistics as global proxies.
In the United States, the Mine Safety and Health Administration reported a fatal injury rate of ten to 15 deaths per 100,000 workers. This rate has plateaued in the past five years after decades of improvement.3
Meaningful safety transformation remains uneven across the mining sector. Several structural barriers continue to impede progress:
- Organizational fragmentation and unclear accountability persist. Recurring gaps exist between corporate safety frameworks and site-level execution.
- Operators may be anchored in a compliance-driven and lagging-indicator mindset. Even in countries with extensive regulations, operators may be focused on preparing for audits and managing documentation instead of proactively identifying and mitigating hazards.
- Data silos and legacy infrastructure may undermine the impact of digital investments. In some cases, data from advanced sensors installed to monitor slope stability and equipment health is not integrated into company-wide safety dashboards that could allow safety experts to see compounded risk factors.
- The global push to scale production of critical minerals has created capability and contractor management gaps, particularly in emerging markets. Rapid workforce growth and increased reliance on contractors in some regions have introduced variability in safety standards and supervision, including inconsistent training, inadequate management of worker fatigue, and limited frontline oversight.
- The culture and change management required to embed new technologies is substantial. Pilot programs in autonomous haulage, fatigue monitoring, and AI-enabled hazard detection have shown safety benefits, but mining digital transformations typically require sustained leadership alignment, frontline adoption, workforce upskilling, and change management to scale beyond pilots and deliver durable impact.
Automation and remote monitoring are part of the rise of intelligent safety
These obstacles persist against a backdrop of technological changes that have made safety inroads. Beginning in about 2010, automation (including autonomous trucks and other equipment) and remote monitoring began to spread throughout the industry. Companies accelerated their use of these technologies over time, with increased deployment of autonomous haulage and equipment incorporating Internet of Things (IoT) technology in recent years, with human oversight in or on the loop (Exhibit 2).
Over the past 15 years, mining companies have reduced equipment-related fatalities through automation and AI.
These technological solutions may offer additional safety improvements across four complementary levers:
- To control hazards, autonomous drones map and inspect underground shafts, tailings dams, and blasting zones in real time, allowing humans to monitor from a safe distance. IoT-enabled sensors embedded in vehicles, ventilation systems, conveyor belts, crushers, and other equipment continuously monitor air composition, ground stability, and equipment health. Connected through edge computing and 5G networks, these systems provide instant analytics and predictive maintenance information to trained operators, allowing them to make decisions that systematically reduce the likelihood of catastrophic failure.
- To improve health, safety, and environmental conditions, trained operators in centralized control rooms aggregate live operational data into a continuous safety dashboard that can strengthen oversight, standardize governance, and accelerate responses to irregularities. Meanwhile, autonomous trucks and remote-monitoring centers can reduce human exposure to risky situations, and sensor-driven predictive analytics can reduce maintenance-related incidents.
- To change behaviors, smart watches or dash cameras, used with appropriate employee consent and privacy guardrails in place, can allow real-time monitoring of worker fatigue, PPE (personal protective equipment) compliance, and proximity interactions that provide immediate feedback to supervisors and operators, reinforcing safe operating discipline before unsafe acts escalate.
- To educate and train, digital twins replicate entire mines, allowing operators to simulate stress scenarios, maintenance schedules, and safety drills in a virtual environment before implementing changes on-site.
Safety transformations could be redefined by AI
Generative AI and agentic AI systems enhance and may even redefine these levers. Gen AI copilots in control centers summarize safety data, identify root causes of near misses, and draft incident reports in seconds. Although human intervention and monitoring is always required, agentic AI systems that can act autonomously within predefined safety parameters are beginning to orchestrate preventive responses, from adjusting ventilation to rerouting autonomous vehicles around unstable ground.
Miners are experimenting with large language model (LLM)–based hazard and incident analytics that can analyze thousands of hazard reports, near-miss narratives, and investigation summaries to identify recurring precursors (such as disabling or overriding alarms or contractor interface breakdowns) and propose targeted monitoring questions for future supervisors that could avoid incidents.
These approaches have the potential to remove the structural barriers described above, as illustrated by the following examples:
- AI for hazard identification and cross-company coordination. Gen-AI-style foundation models are beginning to reshape frontline safety management, in conjunction with human oversight. Rather than deploying isolated AI tools, one company implemented a domain-trained large AI model designed to support multiple safety and operational applications across its coal operations. The model integrates video feeds, sensor data, and text-based reports to detect hazardous behaviors (such as personnel entering restricted zones), identify abnormal conditions, and enhance centralized remote oversight of underground activities—all with high accuracy—delivered to trained supervisors.
- AI for behavioral change and proactive safety management. An operator of bulk transshipment services equipped its vessels with an AI-enabled video analytics platform that continuously monitors existing CCTV feeds to identify deviations from standard operating conditions, such as crew entering restricted zones, slips or falls on deck, or smoke appearing in the engine room. These alerts are routed to trained safety analysts who verify what the analytics has flagged, check it against other operational data, and rapidly inform the relevant captain with precise instructions, if needed, to act before a minor hazard escalates. Once an event is closed, an AI agent generates a structured incident report that includes root causes of the incident and actionable lessons learned. Finally, a safety specialist evaluates whether the recommendations should be considered for implementation across the fleet.
- AI for better contractor safety management. Mining companies are beginning to use AI and digital tools to strengthen contractor safety management, particularly in high-risk activities where contractor execution, site controls, and real-time monitoring are critical. One global mining company’s AI-backed contractor management framework better integrates contractors and third parties into its systems, processes, and performance management routines.
Companies can build the future of mining safety now
To unlock the full potential of digital and AI-driven safety, leading mining companies treat safety as a core enterprise priority, explicitly linked to productivity; environmental, social, and governance (ESG) performance; and long-term resilience. They translate their goals into an integrated, multiyear road map, underpinned by strong data governance and cybersecurity standards that build the reliability and trust required to scale across sites and functions. In building a road map, several factors are important for success:
- Solutions at scale. Leaders focus on standardizing platforms and architectures, adopting “design once, deploy many” approaches that allow proven solutions to be replicated across operations with interoperability across sites, assets, and vendors.
- Workforce and culture transformation. Mining companies invest in training programs that teach data and AI literacy to those with deep field experience, enabling supervisors and frontline workers to interpret AI insights, exercise informed judgment, and intervene before incidents occur. Immersive training approaches, including augmented reality and mobile learning, help workers practice high-risk scenarios in controlled environments, strengthening a proactive safety culture.
- Strong data and governance foundations. Leading organizations invest early in establishing clear data ownership, common standards, and robust data pipelines that span operational, safety, and environmental domains. As AI plays a greater role in decision-making, transparent and ethical use of data become essential to maintain workforce trust and to meet regulatory expectations.
- Relentless focus on measurable impact. Safety transformation takes hold when innovation is measured against operational outcomes such as fewer fatalities and serious injuries, less human exposure to high-risk tasks, higher equipment uptime, and a stronger license to operate.
Intelligent safety is becoming a foundation of competitive advantage in mining. Safety technology and operational excellence are converging into a single, data-driven ecosystem. Combining automation with AI-powered safety platforms and appropriate human oversight can contribute to improved performance and worker safety, reducing unplanned downtime, improving asset utilization, and protecting workers. The result is mines that are not only safer, but also more productive, efficient, and sustainable.
