February 12, 2025

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Embracing AI And Emerging Technologies Can Help Transform The Mining Industry

Embracing AI And Emerging Technologies Can Help Transform The Mining Industry

Anthony Milewski is the founder of The Oregon Group and an influential figure in the mining, metals and energy industries.

The mining sector is experiencing a generational shift, with artificial intelligence (AI) and cutting-edge technologies taking a relatively inefficient industry and transforming it through the adoption of new technologies and methods. These innovations offer solutions to long-standing challenges such as cost reduction, environmental concerns, safety and operational inefficiencies. For mining companies looking to stay competitive and become more sustainable, adopting these technologies is not just an option—it’s a necessity.

Key areas where boards and executives can advance the adoption of this new technology, where it can play an important role in modernizing mining companies, include operational efficiency and cost reduction, safety, sustainability, and data-driven decision-making.

And with nearly 50% of industry engineers reaching retirement age in the next 10 years, technology will be critical to addressing some of the shortfalls.

Operational Efficiency And Cost Reduction

AI-powered systems streamline operations by optimizing exploration, drilling and production.

• Exploration And Resource Estimation: Advanced AI algorithms can analyze geological data to more accurately identify high-potential mining zones. For instance, machine learning models can process satellite imagery, seismic data and geochemical datasets to pinpoint resource-rich areas, reducing the need for costly exploratory drilling. It has been reported that the Bill Gates- and Jeff Bezos-backed startup KoBold recently made a large copper discovery in Zambia using this type of technology.

• Predictive Maintenance: Mining equipment is prone to wear and tear, leading to downtime and costly repairs. AI systems use predictive analytics that can monitor equipment performance, forecasting potential failures before they occur. Companies like Rio Tinto and Vale have adopted this technology to minimize unplanned downtime, significantly lowering maintenance costs. Anglo reported unplanned downtime decreasing by as much as 75% using these methods.

• Autonomous Equipment: Automation of haul trucks, drills and loaders can reduce labor costs and increase efficiency. Caterpillar and Komatsu are among the leaders in developing autonomous mining equipment, enabling 24/7 operations with consistent output.

Enhanced Safety

Mining is dangerous, with risks such as cave-ins, equipment failure and machinery accidents. AI-powered safety solutions mitigate these risks by monitoring operations in real-time.

• Worker Safety Monitoring: AI-equipped wearables track worker health metrics and environmental conditions. Alerts are triggered if parameters like oxygen levels or heart rates exceed safe limits.

• Hazard Prediction: Using historical and real-time data, AI models predict potential hazards, allowing timely interventions. For example, AI systems can detect subtle signs of structural instability in mine walls or tunnels.

Sustainability And Environmental Management

Environmental concerns are a growing challenge for the mining sector. AI and advanced technologies help companies meet regulatory requirements while reducing their ecological footprint.

• Efficient Resource Utilization: AI optimizes resource extraction, ensuring minimal waste. For example, ore sorting technology powered by AI can separate valuable minerals from waste material with high precision, reducing energy and water usage.

• Emission Monitoring And Reduction: AI systems monitor greenhouse gas emissions and suggest strategies for reduction. Technologies like carbon capture and storage (CCS), supported by AI analytics, are helping companies achieve carbon-neutral goals.

• Water Management: AI-driven systems analyze water usage in mining operations, identifying inefficiencies and recycling opportunities. Companies like Anglo American have leveraged AI to implement water conservation initiatives.

Data-Driven Decision-Making

AI and big data analytics enable mining companies to make informed decisions by analyzing vast amounts of operational and market data.

• Market Forecasting: AI tools analyze market trends and commodity prices, helping companies strategize production levels and investments.

• Operational Insights: AI dashboards provide real-time insights into mine performance, enabling managers to make quick adjustments.

Addressing Industry Challenges

As with any new technology, there can be significant adoption challenges and pushback within an organization. I have seen a few key areas such as overcoming organizational pushback, high initial costs and a skills gap that can slow down adoption:

Overcoming Organizational Pushback: Executives and managers alike can address pushback by fostering a culture of innovation and transparency. It is important to engage employees at all levels early on in the process and to provide clear communication about the benefits of the technology and address concerns. Training programs are particularly important when AI applications on machinery (such as sensors) require employees with no previous experience to interface with the technology.

Budgeting For High Initial Costs: While the upfront costs of AI technologies can be significant, strategic budgeting and phased implementation can make adoption more manageable. You should prioritize high-impact, low-cost pilot projects that deliver measurable results before scaling up. Viewing these costs as long-term investments rather than short-term expenditures is critical for gaining board approval and securing necessary resources.

Bridging The Skills Gap: In particular, in the mining industry, the transition to AI solutions will highlight a lack of skilled personnel within a given organization. To counteract this, companies should invest in workforce development initiatives such as reskilling programs and the recruitment of tech-savvy professionals. Collaborating with AI vendors to provide hands-on training and support ensures teams are equipped to manage and sustain new technologies.

Notwithstanding these challenges, there are already many success stories of companies that have embraced these new technologies and enhanced their business. A few well-known examples include:

• Rio Tinto’s Cost Savings: Rio Tinto has embraced autonomous haul trucks and drills, achieving a 15% reduction in operating costs.

• BHP’s AI-Driven Maintenance: BHP uses machine learning models to predict equipment failures to reduce maintenance costs.

• Anglo American’s Sustainability Goals: Anglo American employs AI to monitor and manage water usage, aiming to achieve a 50% reduction in freshwater consumption across its operations by 2030.

Conclusion

Companies that invest in these technologies today could lead the industry tomorrow. Boards and executives must embrace these changes and apply them to their businesses to maximize their competitive advantage.

The adoption of AI and emerging technologies is transforming the mining sector, addressing critical challenges like efficiency, safety and sustainability. Mining companies that embrace this digital revolution can not only improve profitability but also secure their place in a rapidly evolving industry. Now is the time to invest in innovation and position your business as a leader in the future of mining.


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