As Artificial intelligence (AI) becomes increasingly used in most sectors, its relevance has grown, but how exactly will the mining sector be impacted?  According to a report from PWC, AI could contribute up to $15.7 trillion1 to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects.

This article looks at how companies in the mining sector are using AI in their operations to improve productivity, reduce waste, and improve safety outcomes. We provide a global pool of examples, highlighting the successes of companies like Codelco and Cobre Limited (ASX: CBE) in their capacity to implement AI to boost productivity, reduce environmental degradation, and improve safety. 

1. AI and the Environment: 

AI can be used in mining to reduce environmental impact and risk by analysing data quickly and more efficiently. Specifically, AI can also be used to identify specific areas in tenements where operations may be optimised, thereby reducing their impact on the surrounding. In Chile, a mining company called Codelco is using AI to increase copper extraction in old mines as suppliers are increasingly seeking to boost efficiency due to dwindling fresh deposits and rising demand. Codelco launched a data centre that is powered by machine learning in 2020 to reduce falling grades and rising demand as well as limit environmental concerns. A new collaboration between BHP and Microsoft has used AI and machine learning to improve copper drawn from tenements. The BHP Group and Microsoft are working together on improving copper recovery from the Escondida mine in Chile, located in the Atacama Desert. 

2. AI in Mining Exploration

AI can support mining exploration by analysing huge quantities of data and providing insights that help with the identification of on-site targets, leading to greater efficiency on sites with both time and cost. Barrick Gold Corporation, considered one of the largest gold mines, is one company that is currently implementing AI in mining exploration. The company is using algorithms to investigate and process geological data and geophysical data that help identify potential locations to enhance drilling operations. 

3. Predictive maintenance for Mining with AI

A range of AI predictive models can be created to evaluate variables that reflect an asset’s current status, leverage usage trends to make predictions, and provide better information to maintenance teams on equipment failures well in advance. This not only allows for better on-site safety for the workforce, it also allows companies to plan well in advance. ABB Global applies predictive analytics for the maintenance of mining sites through its ABB Ability Predictive Maintenance service. It provides mine operators with user-friendly real-time information and reports on the conditions of each asset. As a result, engineers can perform repairs faster and avoid unnecessary risks.  

4. Safety and Risk Assessments with AI in the Mining Sector

AI  can be used to evaluate and alert possible risks in a mine site, which could be transformational for the mining sector as it supports and promotes a safer and more efficient environment for the human workforce. By optimising systems through better risk management and risk assessments, vital factors such as localised weather forecasting can give Cobre Ltd. (ASX: CBE) an edge over time. As such, the company will be able to make more informed decisions in a variety of aspects regarding their operations. 

5. Sorting of Ores and Minerals with AI

AI-based sorting systems can also be used to identify minerals from waste rock in real time to support recovery rates and rescue the cost of processing. For Cobre Ltd (ASX: CBE), this could reduce the time taken for minerals to reach the market and companies such as Vale in Espírito Santo launched their sites in 2020. With a commitment to both sustainability and safety, the company can use technology to analyse soil samples and make better decisions by maximising sorting during the recovery process. This can cumulatively lead to environmental, health, and safety outcomes on site. 

6. Decision Support Systems driven by AI 

There are lots of benefits to AI being used to support decision-making, including better worker safety, improvement of previously lengthy processes, and cost reduction. In particular, mining company Anglo American has been consistently exploring AI applications in its mining operations in its efforts to be more sustainable and produce less waste. The company has developed AI solutions for mineral exploration and resource estimation and helps workers identify potential mining sites more efficiently. AI tools allow the company to make the best decision possible whilst ensuring all of the above key factors.

Artificial intelligence will bring outsized gains to the global economy and while generative AI and large language models are useful, AI in the mining sector will reduce waste, improve productivity, and facilitate decision-making. With companies like Cobre leveraging environmentally friendly drilling methods and reducing cost with technology, now is the right time to get exposure in some mining stocks.