As global demand intensifies for materials critical to semiconductors, electric vehicles, AI and infrastructure, the mining sector is undergoing a period of significant transformation. With many mines contending with a range of challenges, from site remoteness and declining ore grades to heightened environmental scrutiny, companies are increasingly turning to AI as a strategic enabler – one that supports safer, more efficient operations while helping meet evolving ESG expectations. So, what are the key use cases for AI in the mining sector?

Improving equipment reliability and maintenance

AI plays an important role in predictive maintenance and powering digital twins – two increasingly vital components of modern mining equipment. Digital twins, combined with AI monitoring, can harvest and analyse real time sensor data such as vibration monitoring, thermal imaging and operational logs, to identify equipment at risk of failure, sometimes even days in advance. In practice, this can help organisations predict and avoid equipment downtime and extend the lifespan of assets.

Using AI to accelerate exploration and enhance ESG

Machine learning can help companies better manage their resources and make smarter decisions based on up-to-date and real-time data flows. Companies such as Earth AI and Terra AI are applying machine learning to geological, seismic, magnetic and satellite datasets to identify promising drill sites. These tools are helping companies to locate opportunities more quickly, with some reporting significantly higher accuracy compared to traditional methods. In some cases, AI has improved discovery rates by up to 20%, enhanced modelling accuracy by 15% and reduced drilling costs by 25%, showcasing clear return on investment for resource development.

ESG is still a key area of focus for mining companies, and AI is helping businesses adapt to work in a more sustainable manner. For example, AI-driven sentiment analysis is helping companies better understand community concerns, contributing to stronger stakeholder engagement and social licence to operate. Other technologies, such as satellite imagery, drones, Internet of Things (IoT) sensors and analytics, are being used to monitor deforestation, air and water quality, and land degradation, supporting more proactive risk mitigation strategies. Meanwhile, blockchain-enabled traceability is adding transparency to mineral supply chains, particularly for ethically sensitive materials such as cobalt and lithium. As stakeholder demands for ethical and transparent operating models increase, technology will be vital in helping companies meet those expectations.

AI and automation in mining: boosting safety and productivity

Mining remains one of the world’s most hazardous industries. In 2023, the US alone recorded 42 mining fatalities. AI technologies, such as automated mobile machinery, drones, digital twins and wearable sensors reduce such risks by removing people from danger and detecting fatigue or environmental hazards in real time.

Autonomous haulage trucks and drilling rigs reduce safety compromise while increasing uptime. They operate around the clock without risk of fatigue, helping companies act more responsively amid a turbulent economic landscape. Global fleets of these autonomous vehicles have already helped reduce accidents by up to 80%, with forecasts suggesting productivity gains of between 15% and 30%, especially under 24/7 operations.

Strategic priorities for mining leaders

There has been a 51% compound annual growth rate (CAGR) increase in mentions of 'robotics' across mining filings since 2020, signalling growing leadership attention to automation and AI. A 2025 WiFiTalents study projected the AI-in-mining market to reach $3.2bn by 2026, growing at a 40% CAGR. It also reports 45% of mining firms have already adopted some degree of AI, with results pointing to a 30% reduction in equipment downtime, a 10% reduction in labour costs, and a 15% cut to energy use, alongside improvements in ore estimation and recovery rates of between 10% and 25%.

That said, AI adoption challenges remain. A GlobalData survey found that over half of mining stakeholders cite a lack of proven effectiveness as a barrier, with the sector’s capital intensity, regulatory complexity and data quality all limiting factors. Adding to this, improvements in critical mineral supply are often constrained by permitting delays and geopolitical uncertainty.

The inherent uncertainty of mining is a barrier to long-term investment decisions, and the repercussions of this uncertainty extend into how quickly companies adopt technology like AI. But with cost pressures becoming increasingly acute, mining businesses should consider how the evolving role of AI in mining could benefit them.

Key to rolling out AI capabilities is disciplined execution, beginning with targeted pilots, growing internal capabilities and linking digital outcomes to both ESG and commercial objectives.

Leadership teams should remember that AI is no longer a distant prospect, but a strategic lever for efficiency gains and growth. Companies that act decisively will be better positioned to drive efficiency, resilience and sustainable growth across the mining value chain.

How we can help your mining and metals business

We work with a wide variety of clients in the mining and metals sector across the UK and globally, with our expertise spanning:

• Global network/capabilities, including audit and tax
• Global mobility support and tax incentives
• Advice on systems compliance with UK and wider regulations
• Corporate governance, including ESG
• Capital markets transactions
• Model audit and due diligence assignments.

If you would like to discuss the impact for your mining and metals business, please contact David Hough.

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