A Year of Tests and Major Opportunities
Persistent and not always positive market shifts in 2025 became a catalyst for development for many players in Russia’s mining industry. Successful use cases for digital technologies laid the groundwork for scaling digital solutions across the sector.
Moving Into the Digital Domain
In 2025, the adoption of artificial intelligence and IT technologies in Russia’s mining industry remained largely at the pilot stage. Even so, companies that implemented digital tools reported immediate and measurable economic gains. End-to-end digitalisation of extraction processes – from drilling and blasting to ore transportation – has already become an industry-wide necessity.
In drilling and blasting operations, algorithms model drilling patterns and calculate explosive charges while forecasting rock granulometry. This reduces downstream crushing costs. At the ore beneficiation stage, neural networks stabilise flotation and grinding processes.
In transport logistics, AI calculates payload volumes in haul trucks, recognises equipment identifiers, and monitors loading levels. For industrial safety, neural networks track the use of personal protective equipment such as helmets and safety vests, flag hazardous situations, and monitor gas concentrations. In equipment maintenance, algorithms analyse vibration, load, and temperature data to predict failures. In geological exploration, AI accelerates the processing of geophysical data.
Initiatives by Industry Leaders
In 2025, Russia’s largest industrial groups demonstrated tangible progress in the digitalisation of mining operations. A few examples illustrate the trend.
Norilsk Nickel, working jointly with Sber, is developing projects across four areas – large language models, machine learning, process optimisation, and digital skills development for employees.
Alrosa uses AI for geological exploration, PPE monitoring, ore granulometry assessment, robotics, and forecasting diamond prices.
Integrated control centres have also delivered strong results by linking all stages of extraction. One example is the Geometallurgy project for Evraz at its Kachkanar deposit. The system tracks ore quality from the mining face to the processing plant, while automating mining equipment, rail transport, and production lines.
Barriers to Growth and Points of Acceleration
Large-scale deployment of AI in mining is still constrained by several factors. These include a shortage of labelled industrial data for training digital models, fragmented IT systems within enterprises, the need for equipment import substitution, a lack of specialists with AI expertise, and high capital costs.
Government policy can positively influence the pace of digitalisation. Incentives such as subsidies, tax benefits, and concessional financing have proven effective in encouraging adoption of digital technologies. At the same time, bureaucratic procedures and complex project approval processes often slow transformation.
Outlook to 2030
Experts forecast a fourfold expansion of the AI solutions market in the mining and metals sector by 2030. Even now, successful deployments are delivering hundreds of millions of rubles in savings (several million US dollars), improving safety performance, and reducing environmental impact.
To move from pilots to mass adoption, companies will need to modernise legacy infrastructure, build workforce capabilities through training, and integrate new technologies into existing processes.
A comprehensive approach will be critical – combining technological innovation, flexible business processes, and long-term development strategies. While AI in Russian mining is still far from ubiquitous, experience gained from pilot projects already confirms its potential to transform the industry.
