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Leading artificial intelligence companies in the mining industry

Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own or that help users make decisions.  

Within five years, the deployment of AI will be essential to the survival of companies of all sizes across all sectors. For data on where AI mining firms are likely to be focusing their time and resources, the GlobalData matrix has valuable insights relating to value chain impacts.  

Leading AI vendors in mining include Goldspot Discoveries, Earth AI, Minerva Intelligence, DroneDeploy, Hikvision, Imago, Caterpillar, Komatsu, and Microsoft.  

Additionally, leading adopters of AI in mining include Goldcorp, BHP, Rio Tinto, Freeport-McMoRan, Fortescue, Newcrest, Barrick Gold, and Dundee Precious Metals. 

Landscape for artificial intelligence companies in mining

With falling yields and increasingly hostile locations, AI is more important than ever in the mining industry. 

AI has reached a point where it can effectively impact every section of the mining value chain, from prospecting to extraction, processing, and even marketing.  

AI is making the processes adopted smarter, with the ability to derive more value from existing resources through optimisation in ore processing.  

There is even an environmental case for AI, namely, it can help better target rich ore deposits remotely. This translates to the need for less unnecessary excavation, which can be hugely damaging to the environment.  

GlobalData’s Global Emerging Tech CXO Survey 2020 highlights the importance of AI in mining. Some 75% of mining respondents were currently investing in AI.

Challenges for artificial intelligence companies in mining


Miners must ensure that they are increasing productivity by adopting the latest technology. Mechanisation and monitoring are supporting improvements in productivity and reducing the cost per unit output.  

Cost control

There is increasing upward pressure on costs to mining firms. Several factors have spurred this, including declining commodity prices, longer hail distances, falling ore grades, and rising material and labour costs.  

Supply chain

More disparate ore deposits are pushing mining into remote locations and developing nations. This gives rise to greater challenges in operating an efficient supply chain.

Safety and sustainability

Given the worldwide shift to sustainability in the last few years, mining has come under increasing scrutiny for its damaging environmental practices. 

In addition, safety has become a concern. Mining firms must take more responsibility for ensuring that workers are properly protected on-site by taking active steps to avoid accidents and actively monitoring safety.

Resource development

There is pressure on mining firms to continuously identify viable new mines. This is made more difficult by an environment of declining ore grades. Furthermore, there are rising development costs and more remote deposits


The pandemic posed a significant threat to the mining industry. The main fear is that there could be an outbreak at a mine, which would force operations to a halt, impacting both costs and productivity greatly.  

Additional challenges are also faced in various mining industry endeavours.  One such example is the US Environmental Protection Agency proposing restrictions that would bar the development of the long-delayed Pebble Mine project in Alaska’s Bristol Bay, a region known for its $2.2bn salmon fisheries industry. 

Efficiencies addressed by artificial intelligence in mining

The mining industry is under more pressure than ever to increase efficiencies.  

With declining ore grades and more disparate and remote deposits, greater challenges are being seen when it comes to securing new resources.  

Additionally, rising mining costs drive the need for greater productivity at mining sites worldwide. At the same time, there is a strong focus on ensuring safety and sustainability within mines. 
AI can address many, if not all, of these challenges and inefficiencies via several key technologies in the value chain.  

These include computer vision, smart robots, data science, and machine learning (ML). 

Investment in artificial intelligence companies in mining

When it comes to AI companies in mining, the adoption of this technology continues to grow.  

As we see rising degrees of investment in areas such as predictive maintenance, planning software and communications systems, mines are looking to improve productivity.  

Meanwhile, high priorities for investment over the coming two years include safety technologies such as collision avoidance and fatigue detection.  

As they continue to aim for reduced emissions, mines are also increasingly looking to invest in battery/electric vehicles.  

To this end, 39% of mines expect to invest in these over the coming two years, compared with 23% in the previous survey in Q4 2020. 

AI usage in mining

In terms of the degree to which the respondents’ mines had invested in modern technologies, the most widely adopted of those investigated were mine planning software, mine management software, mine communication systems, and predictive maintenance.  

This is similar to previous surveys, and there were no significant changes in terms of the rankings of the technologies when it came to the levels of investment, aside from an increase for battery/electric vehicles. 

Contrasting the majors, such as Anglo American, Glencore, Rio Tinto, Vale, Newmont, and Barrick, versus the non-majors, the former were, as per previous years, more likely to have invested in these technologies compared to the smaller miners.  

The gap has narrowed slightly, however, for mine management software, battery/electric vehicles, collision avoidance and fatigue detection, though has widened in the case of predictive maintenance and autonomous vehicles, as investment from the majors has increased.  

Global mining growth involving artificial intelligence

Mining firms will spend $218m on AI platforms worldwide by 2024. This is up from $76m in 2019, representing a compound annual growth rate (CAGR) of 23.4%. 

Australasian mines had, on average, the highest penetration of technologies, especially drones, remote control and autonomous vehicles and mine management software.  

Together with African mines, they had the highest expectations overall in terms of investment across all technologies.  

Surface mines had greater levels of investment in drones, communication systems and fatigue detection while underground mines had invested more in remote-controlled and battery-powered vehicles.  

While the latter contributes to the decarbonisation of mine sites, specifically in underground mines their adoption has advanced as they also help to reduce ventilation costs.  

An additional question in this year’s survey examined the use of drones, with the most common uses including surveying and mapping, followed by monitoring and inspection. 

Chinese tech giants – Alibaba, Baidu, and Tencent – all offer extensive ML applications and services and are making considerable inroads, especially in terms of research.  

For example, Baidu, with its PaddlePaddle open-source deep learning platform, added EZDL (an AutoML tool for non-developers) in 2018, federated learning tools in 2019, and a quantum ML toolkit (Paddle Quantum) in mid-2020.

Market forecasts for artificial intelligence companies in mining 

GlobalData estimates that the global AI platform market will be worth $52bn in 2024, up from $28bn in 2019. Total spending on AI technology is almost certainly higher, but it is difficult to estimate.  

There are two primary reasons for this.  

Firstly, AI is an intrinsic part of many applications and functions, making it almost impossible to identify revenue explicitly generated by AI. 

Secondly, the range of sub-sets and technologies that make up AI can be challenging to locate and track. In general, valuations of the overall AI market range from a few billion dollars to several trillion, depending on the source.  

Rather than attempting to size the market, some companies have tried to forecast its economic impact.  

A PwC report in 2017 estimated that AI would add $15.7 trillion to the global economy by 2030 and boost global GDP by up to 14%. 

Global AI platform revenue will reach $52bn by 2024, marking an increase from $28bn in 2019.  

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