Thematic Intelligence: Artificial Intelligence in Mining (2024)

Thematic Intelligence: Artificial Intelligence in Mining (2024) Summary Mining companies are investing in AI to enhance productivity, safety, cost efficiency, and mineral discovery. The impact of AI in mining is already significant and will only grow in the coming years. Artificial intelligence is already impacting the mining industry > For decades, artificial intelligence (AI) was the domain of university and corporate R&D labs. Recent progress in machine learning (ML) on the back of improved algorithms (e.g., Google’s AlphaGo, OpenAI’s GPT-3) and increasing computing power have enabled AI to solve real-life problems. GlobalData estimates the total AI market will be worth $1 trillion by 2030, up from $103 billion in 2023 at a compound annual growth rate of 39%.Mining companies are investing in AI to enhance productivity, safety, cost-efficiency, and mineral discovery. The impact of AI in mining is already significant and will only grow in the coming years. AI enhances efficiency and productivity in mining operations > AI enables mining companies to deploy autonomous machinery and use data analytics to enhance operational efficiency and productivity. Autonomous equipment can operate at peak efficiency around the clock, ensuring uninterrupted operations. Advanced AI algorithms enable predictive maintenance, reducing downtime by preventing accidents before they occur. Predictive maintenance systems can minimize such costs for mining companies while also preventing costly capital expenditure on equipment in cases where assets are irreparable. Advanced AI algorithms also facilitate real-time operational adjustments, further augmenting efficiency. AI helps save lives in the mining industry > The mining industry is extremely dangerous. The US Mine Safety and Health Administration reported 42 mining fatalities in 2023. AI plays a crucial role in enhancing safety in mining by automating many dangerous jobs inside mines. The most effective way to reduce risks around a hazard is to remove the hazard entirely; automation can enable that. AI enables mining companies to use sensors, real-time data, and analytics to understand when changes in factors such as temperature and vibrations can lead to danger. AI-powered wearable sensors can continuously monitor mine workers for signs of drowsiness, fatigue, and physical discomfort, enabling proactive measures to remove workers at heightened risk of accidents. Key Highlights - AI plays a crucial role in enhancing safety in mining by automating many dangerous jobs inside mines. The most effective way to reduce risks around a hazard is to remove it entirely, and automation can enable that. AI is also essential to improving safety through predictive maintenance. AI systems can continuously monitor and analyze operational data to predict potential equipment failures and structural weaknesses, allowing for timely interventions that prevent catastrophic accidents. - While using AI in predictive maintenance will be essential for improving safety in mining, it also plays a key role in boosting productivity. When a piece of machinery is on the verge of failure, it exhibits various signs, such as increased vibrations, overheating, and loss of power. Machine learning can process and understand this data in real-time, using it to predict when a machine is about to fail and stop it from operating before it does so, improving productivity. - Mining companies must prioritize investment in the motion-side of AI to remain competitive. Scope - This report provides an overview of the AI theme 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. - The report also includes a comprehensive data analysis, including market size and growth forecasts for AI. Reasons to Buy - 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 mining 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.


Executive Summary
Players
Value Chain
The Impact of AI on Mining
Case Studies
Market Size and Growth Forecasts
AI Timeline
Signals
Companies
Sector Scorecard
Glossary
Further Reading
Our Thematic Research Methodology
About GlobalData
Contact Us
List of Tables
Table 1: p4: Key players in AI
Table 2: p.5: AI value chain
Table 3: p.18: AI timeline
Table 4: pp.19-21: AI M&A trends
Table 5: pp.26-27: Leading AI adopters in mining
Table 6: pp.28-29: Specialist AI vendors in mining
Table 7: p.30: Thematic Scorecard Company Screen
Table 8: p.31: Thematic Scorecard Thematic Screen
Table 9: p.32: Thematic Scorecard Valuation Screen
Table 10: p.33: Thematic Scorecard Risk Screen
Table 11: pp.34-38: Glossary
Table 12: p.39: Further Reading
List of Figures
Figure 1: p.6: Categories of advanced AI capabilities
Figure 2: p.7: Thematic investment matrix
Figure 3: p.8: Survey data - 'When do you think AI will have a noticeable impact on your mine?'
Figure 3: p.9: Survey data - 'Which of the following would you consider to be the main barriers to investing in AI in the mining sector?'
Figure 4: p.10: Survey data - Top five companies by number of autonomous trucks in surface mines globally, July 2024'
Figure 5: p.12: Fleet Space Technologies' ExoSphere system (Source: Fleet Space Technologies)
Figure 6: p.14: Liebherr's T264 autonomous truck (Source: Liebherr)
Figure 7: p.15: Global AI revenue by product, 2019 - 2030
Figure 8: p.16: Global specialized AI applications revenue, 2019 - 2030
Figure 9: p.17: Geographical split of global AI market 2023
Figure 10: p.22: Number of AI-related patents across the mining industry, January 2014 - H1 2024
Figure 11: p.23: Top five countries for AI-related patent publications in the mining industry, January 2019 - H1 2024
Figure 12: p23: Top five mining sub-sectors by numbers of AI-related patent publications, January 2019 - H1 2024
Figure 13: p.24: Number of AI-related hirings in the mining industry, January 2020 - H1 2024
Figure 14: p.25: Top five active recruiters for AI-related jobs in the mining industry, 2020 - H1 2024
Figure 15: p.40: Thematic Scorecard Methodology

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