Strategic Intelligence: Artificial Intelligence in Mining
Description
Strategic Intelligence: Artificial Intelligence in Mining
Summary
Mining companies are investing in AI technologies to enhance productivity, safety, cost efficiency, and mineral discovery. Mining companies are pouring investment into AI initiaves and will continue to do so.
Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. AI tools can perform tasks that typically require human intelligence, such as learning from data, recognizing patterns, making decisions, and understanding language or images. AI can boost productivity, enable new insights, and automate routine or dangerous tasks. GlobalData estimates the total AI market will be worth $641.8 billion by 2029, up from $131.3 billion in 2024 at a CAGR of 37.4%.
AI is integrated into key solutions across the mining industry, including predictive maintenance, autonomous equipment, fatigue detection, and advanced data analysis for mineral exploration and operational optimization. As a result, GlobalData forecasts that mining companies’ spending on AI will grow from $2.7 billion in 2024 to $13.1 billion by 2029.
AI enhances operational efficiency in mining AI-powered technologies are helping mines boost productivity and reduce costs across extraction, haulage, and processing. Predictive maintenance platforms have prevented major failures and avoided high-cost asset downtime, while automated trucks, shovels, and drills enable 24/7 operations. Advanced analytics and machine learning also optimize haul routes, reduce idle time, improve payloads, and streamline ore milling to raise recovery and cut energy, water, and reagent use.
AI is making mines safer
Mining remains inherently hazardous. While some firms report zero fatalities, industry-wide deaths persist, harming communities, investors, and operations. AI is reducing these risks by removing humans from dangerous tasks: autonomous and tele-remote trucks and drilling systems reduce exposure to hazardous zones and human error, causing significant reductions in incidents and costs. AI-enabled sensing using light detection and ranging (LiDAR), thermal, gas, and vision systems support pre- and post-blast inspections and search-andrescue missions. Safety-focused AI, such as collision-avoidance and fatiguedetection, further prevents accidents by highlighting and logging unsafe conditions.
Key Highlights
Summary
Mining companies are investing in AI technologies to enhance productivity, safety, cost efficiency, and mineral discovery. Mining companies are pouring investment into AI initiaves and will continue to do so.
Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. AI tools can perform tasks that typically require human intelligence, such as learning from data, recognizing patterns, making decisions, and understanding language or images. AI can boost productivity, enable new insights, and automate routine or dangerous tasks. GlobalData estimates the total AI market will be worth $641.8 billion by 2029, up from $131.3 billion in 2024 at a CAGR of 37.4%.
AI is integrated into key solutions across the mining industry, including predictive maintenance, autonomous equipment, fatigue detection, and advanced data analysis for mineral exploration and operational optimization. As a result, GlobalData forecasts that mining companies’ spending on AI will grow from $2.7 billion in 2024 to $13.1 billion by 2029.
AI enhances operational efficiency in mining AI-powered technologies are helping mines boost productivity and reduce costs across extraction, haulage, and processing. Predictive maintenance platforms have prevented major failures and avoided high-cost asset downtime, while automated trucks, shovels, and drills enable 24/7 operations. Advanced analytics and machine learning also optimize haul routes, reduce idle time, improve payloads, and streamline ore milling to raise recovery and cut energy, water, and reagent use.
AI is making mines safer
Mining remains inherently hazardous. While some firms report zero fatalities, industry-wide deaths persist, harming communities, investors, and operations. AI is reducing these risks by removing humans from dangerous tasks: autonomous and tele-remote trucks and drilling systems reduce exposure to hazardous zones and human error, causing significant reductions in incidents and costs. AI-enabled sensing using light detection and ranging (LiDAR), thermal, gas, and vision systems support pre- and post-blast inspections and search-andrescue missions. Safety-focused AI, such as collision-avoidance and fatiguedetection, further prevents accidents by highlighting and logging unsafe conditions.
Key Highlights
- The report covers how AI overcomes key challenges in the mining industry. These include safety, mineral discovery and exploration, productivity, ore processing, and the environment.
- The 2025 GlobalData mine-site technology adoption survey identified the technologies mining companies are investing in. These include mine planning and scheduling software, collision avoidance, predictive maintenance, fatigue detection, drones, wearables, and autonomous vehicles which commonly have integrated AI features, showing how important AI is to the mining industry.
- This report provides an overview of AI and how it will impact the mining industry.
- The report predicts how AI in mining will evolve, including the key challenges it will solve.
- It includes selected case studies highlighting who is innovating in mining using AI technologies.
- The report also includes a comprehensive data analysis, including market size and growth forecasts for the AI market.
- GlobalData’s thematic research ecosystem is a single, integrated global research platform that provides an easy-to-use framework for tracking all themes across all companies in all sectors.
- This report is essential for senior executives at minining companies to understand the critical benefits from integrating AI technology into their operations. Mining companies who fail to implement AI solutions will fall behind.
- In addition, the report identifies the leading AI adopters in mining, as well as specialist tech vendors in this space.
Table of Contents
33 Pages
- Executive Summary
- Players
- Value Chain
- Advanced AI capabilities
- The Impact of AI on Mining
- How AI helps tackle the challenge of safety
- How AI helps tackle the challenge of mineral discovery and exploration
- How AI helps tackle the challenge of productivity
- How AI helps tackle the challenge of ore processing
- How AI helps tackle the environmental challenges of mining
- Case Studies
- Mantrac’s smart wearables reduced fatigue-related safety events
- ABB has launched a generative AI-powered copilot to aid troubleshooting
- Rio Tinto uses AI in biodiversity efforts at its Weipa mine
- Fleet Space is using AI for the discovery of porphyry copper
- Hexagon’s Drill Assist solution retrofits drills to make them autonomous
- Companies
- Leading AI adopters in mining
- Specialist AI vendors in mining
- Sector Scorecard
- Mining sector scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Glossary
- Further Reading
- Our Thematic Research Methodology
- About GlobalData
- Contact Us
- List of Tables
- Table 1: Leading AI adopters in mining
- Table 2: Specialist AI vendors in mining
- Table 3: Glossary
- Table 4: Further Reading
- List of Figures
- Figure 1: Key players in AI
- Figure 2: The AI value chain
- Figure 3: There are five categories of advanced AI capabilities
- Figure 4: Thematic investment matrix
- Figure 5: Many of the technologies that mining companies are investing in integrate AI
- Figure 6: The average number of mining fatalities fell in 2024, but was still higher than in 2022 and 2020
- Figure 7: Mining executives' priorities for the mining sector
- Figure 8: Mantrac has implemented driver safety systems
- Figure 9: ABB’s tool can be used by field technicians
- Figure 10: Rio Tinto has used AI to improve palm cockatoo research
- Figure 11: Fleet Space has integrated AI in its Exosphere service for mineral exploration
- Figure 12: Hexagon’s Drill Assist uses AI to make drills autonomous
- Figure 13: Who does what in the mining space?
- Figure 14: Thematic screen
- Figure 15: Valuation screen
- Figure 16: Risk screen
- Figure 17: Our five-step approach for generating a sector scorecard
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