Tackling the talent shortage: How AI reduces manual work for mining reliability teams
A 2025 report from EY identified rising costs and productivity as one of the top ten risks and opportunities for mining and metals. The report also noted that workforce expenses are exacerbated by the skills shortage. This shortage is largely brought on by an ageing population; there is an exodus of retiring workers just as the industry needs to scale up to meet demand for critical minerals and metals.
For reliability and maintenance teams on the ground, these pressures are magnified by a reliance on manual processes and traditional approaches to asset health. Maintenance is often performed at fixed intervals, regardless of the asset's age, condition, or operating environment. While condition monitoring may be deployed to some assets, data collection is frequently manual and inconsistent.
Teams also lack the specialized skills and resources to extract meaningful insights from the data they collect. This not only impacts productivity but delays the critical insights needed to optimize maintenance, reliability, and overall performance. It also puts even more pressure on reliability and maintenance teams as it leads to unnecessary maintenance on some assets while failing to prevent unexpected failures on others.
AI and machine learning: Powering a smarter, more efficient operation
The solution to these challenges lies in a fundamental shift towards data-driven operations, powered by AI and machine learning. By operationalizing these technologies, mining operators can automate manual tasks, improve decision making, and drive-up operational efficiency
Applications of AI for mining reliability teams include:
Automated Data Collection: AI-powered systems can automate the collection of sensor and operational data across mining equipment. This reduces the need for time-consuming manual inspections and enables easier and safer collection of data from hazardous or hard to reach environments. Automated data collection also ensures more consistent, real-time data for analysis.
Intelligent Data Analysis: AI and machine learning algorithms can combine and analyze vast amounts of data from different sources, including asset failure mode data and sensor data on current asset condition. AI can give context to this data and translate it into valuable Key Performance Indicators (KPIs) for better decision-making.
Prioritization: AI can filter through the noise to identify alerts that truly matter. This allows teams to better prioritize their efforts, react swiftly when it matters most, and frees up team bandwidth for higher-value work.
Diagnostics and prescription: AI and specifically purpose-built AI, can perform in-depth analysis and diagnosis, and even prescribe solutions to prevent failures before they happen. This is key to moving away from traditional reliability approaches and allows teams to more easily predict and prevent asset failure.
Optimized Maintenance Scheduling: By predicting potential equipment failures, AI allows for the proactive scheduling of maintenance activities. This minimizes disruption to operations and ensures that resources, including personnel and spare parts, are available when needed, moving from a fixed schedule to a more efficient needs-based one.
Scaling asset management: AI enables more scalable, consistent practices across multiple mining sites. For example, AI-powered data collection and analysis reduces manual effort and provides consistent data across sites for improved decision making and prioritization.
Operationalizing AI and machine learning with Cordant™
Cordant™ Machine Health can help mining reliability teams to harness AI not only for productivity gains but for improvements in asset health and reliability.
With a full library of mature data and purpose-built AI to enable prescriptive diagnostics, Cordant™ Machine Health provides a comprehensive solution to asset health. Data is continuously monitored 24/7 and analyzed using our purpose-built AI which significantly minimizes the risk of false alarms and missed faults. Leveraging a multi-layered algorithm, our analytics also provide accurate and actionable machine diagnostics with 99% detection accuracy.
With Cordant™, reliability teams can efficiently:
- Identify where potential issues are and when they happened
- Identify what caused the issue and gain recommendations on how to fix it
- Identify severity to inform decisions on when to take action
Applying AI in this way reduces pressure on reliability teams while also enabling more efficient and reliable operations.
In one example, Israel's largest oil refinery and petrochemical producer deployed an AI-powered Machine Health solution to address high maintenance costs and operational risks associated with their complex machinery. This enabled them to move from a reliance on manual data collection and strategies like route-based maintenance to a more proactive and efficient approach. The results were significant, with the company achieving a 72% reduction in maintenance costs across monitored sites and seeing a 4x return on investment within just six months.
Building towards a more proactive, efficient future
By embracing AI-powered solutions, mining companies can enable more automated, intelligent, and proactive maintenance. This not only addresses the immediate challenges of a skills shortage and rising costs but also builds a more resilient and efficient foundation for the future.
To learn more and discover other approaches to overcoming resource constraints, download our eBook: How to Maximize Reliability in Mining Operations with Limited Resources.