Adoption of the latest AI technology to make faster decisions with more accuracy, boosting efficiency through eliminating errors, and improving health and safety measures are the factors expected to provide lucrative opportunities for the artificial intelligence in mining market. The rising demand for advanced technologies, artificial intelligence, and machine learning to spot the location, analyze patterns, and optimize resources to reduce waste drives the growth of the market. In addition, autonomous mining equipment equipped with artificial intelligence is set to increase overall productivity and minimize fatal mistakes. However, limited understanding of deployment of AI‑related technology and poor testing method to generate insights hamper the artificial intelligence in mining market growth.

Key market players – Glencore PLC, China Minmetals Corporation, ArcelorMittal SA, POSCO, BHP Group PLC, Rio Tinto Limited, Vale SA, Jiangxi Copper Ord Shs A, Anglo American PLC, and Aluminum Corp. of China Limited.

Regions Covered – North America (U.S., Canada and Mexico), Europe (Germany, UK, France, Italy and the Rest of Europe), Asia-Pacific (China, Japan, India, South Korea and the Rest of Asia-Pacific), and LAMEA (Latin America, Middle East and Africa)

Mining is not referred as environment-friendly method as it is disruptive and causes negative impact on the environment. The implementation of artificial intelligence in this segment is expected to reduce the environmental impact. The deployment of cameras and sensors in the mines for constant surveillance of the operations is performed inside and outside mines. These devices are able to monitor extraction and mining activities by reducing the spread of waste and harmful materials in the environment. Regular vibrations in the surfaces, temperature changes, and other several events can all be identified by machine learning.

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AI-driven automation enriches digital control systems and programmable logic devices to improve consistency and accuracy at mining sites. It speeds up discovery and exploration phases by predicting target areas and using soil samples for quantification of mineral deposit as well as impurities in the extracted output. Artificial intelligence also integrates robotic automation and IoT to manage autonomous drilling techniques and carrying fleets. It allows digital mines built with sensor network technologies to take advantage of IoT data for real-time surveillance and operations.

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