Global AI in Mining Market 2025-2035

Description

List of Tables

AI in Mining Market Size, Share & Trends Analysis Report by Component (Hardware, Software, and Service), by Technology (Machine Learning, Computer Vision, Natural Language Processing, Robotics, and Data Analytics), and by Application (Exploration, Geology, Ore Sorting, Equipment Maintenance, Safety and Risk Management, Autonomous Drilling, and Hauling), Forecast Period (2025-2035)

Industry Overview

AI in mining market was valued at $25.8 billion in 2024 and is projected to grow at a CAGR of 16.2% during the forecast period (2025-2035). Artificial intelligence (AI) for mining utilizes advanced computing methods such as automation, machine learning, and predictive analytics to handle large amounts of data, optimize tasks, and streamline technical processes. Uses include predictive maintenance, leading-edge mineral exploration, autonomous operation, safety management, process optimization, and environmental monitoring. AI algorithms analyze live sensor feeds to predict failures, reducing downtime and operational costs. It scans big geology datasets to determine sites with potential, operates self-driving trucks, and enables the imposition of safety regulations. AI also streamlines ore processing, transport, and resource allocation, resulting in increased yields and energy savings.

Market Dynamics

Rising Demand to Reduce Operational and Exploration Costs

Mining companies are implementing AI-led solutions due to the growing demand for operational cost-effectiveness and cost reduction. As per the World Economic Forum (WEF), in September 2024, AI algorithms can easily analyze geological data to predict the location of mineral deposits more accurately and precisely. Several companies employing AI in exploration have reported a 20%-30% reduction in time and costs associated with mineral discovery. Improved data availability through digitalization and IoT inclusion has given significant scope for adopting AI. The growing demand for advanced safety norms in unsafe mining conditions justifies automation and AI adoption.

Various competitive forces are inducing companies to use their strength of AI for real-time decision-making and productivity. Cloud infrastructure expenditures and analytics platforms are facilitating the scalable deployment of AI into mining operations. Several governments and environmental regulations are also driving AI-based monitoring and resource scheduling solutions. Several mining companies are collaborating and working with AI technology companies to create custom applications. The deployment of AI-based solutions has increased due to the lack of workers in remote mining locations. Moreover, various government initiatives for digital transformation and rising investor interest are accelerating AI's adoption in the mining market.

Growing Demand for Productivity and Operational Efficiency

AI technologies are being used by several mining firms to process their operations, automate repetitive tasks, and enhance equipment utilization. For instance, in May 2025, Razor Labs launched DataMind AI Version 4.1, an upgrade that introduced advancements in real-time decision-making, diagnostic precision, and maintenance processes in heavy industries and mining. In order to help maintenance teams protect critical assets, maximize performance, and significantly reduce unscheduled downtime, DataMind AI 4.1 was developed to improve the increasing operational needs of industrial environments.

Market Segmentation

Based on the component, the market is segmented into hardware, solutions, and services.
Based on technology, the market is segmented into machine learning, computer vision, natural language processing, robotics, and data analytics.
Based on the application, the market is segmented into exploration, geology, ore sorting, equipment maintenance, safety and risk management, autonomous drilling, and hauling.

Software: A Key Segment in Market Growth

AI in mining market is expanding rapidly and is primarily driven by the creation of software solutions that enhance operational efficiency, productivity, and safety. AI-based software applications are being increasingly used for predictive maintenance, mineral exploration, ore grade estimation, and autonomous equipment operation. These technologies support mining companies to process large datasets in real-time, improving decision-making and minimizing operating risks. The integration of AI with Geographic Information Systems (GIS) and Internet of Things (IoT) sensors has further made remote monitoring and data collection simple and efficient. ML-supported software solutions can forecast the breakdown of equipment much earlier, resulting in decreased downtime and maintenance expenses.

Regional Outlook

The global AI in mining market is further divided by region, including North America (the US and Canada), Europe (the UK, Germany, France, Italy, Spain, Russia, and the Rest of Europe), Asia-Pacific (India, China, Japan, South Korea, Australia and New Zealand, ASEAN Countries, and the Rest of Asia-Pacific), and the Rest of the World (the Middle East & Africa, and Latin America).

North America Maintains Strong Market Position

The North American AI in mining market is witnessing geographical expansion, owing to the rising demand for automation and operational efficiency in mining operations. The advancement of AI technologies such as ML and computer vision is assisting mining firms to maximize the extraction of resources, improve safety, and reduce the environmental impact. AI is being driven by the need to process data in real-time and perform predictive maintenance that improves equipment availability and operating expenses.

Moreover, AI augmented with robotics and remote sensing technologies improves the effectiveness of exploration and drilling operations. According to the United States Environmental Protection Agency, in October 2024, the US Geological Service estimates that there are over 11,000 active mines in the United States, ranging from sand & gravel pits to coal and gold mines. There are also thousands of inactive mines scattered across the country. Regulatory demands to ensure safer working environments for staff and reduced emissions are driving the adoption of AI. AI is being employed by companies to analyze geological data and make mine planning decisions more effectively. The initiative to digitalize the mining sector is building the correct climate for AI solutions.

Komatsu Ltd., a firm that provides AI-powered predictive maintenance solutions with its SMARTCONSTRUCTION platform. The platform employs AI to collect and analyze data from heavy equipment, offering insights to prevent breakdowns and maximize machine performance.

Market Players Outlook

The major companies operating in the global AI in mining market include ABB Ltd., Caterpillar Inc., Komatsu Ltd., IBM Corp., Microsoft Corp., among others. Market players are leveraging partnerships, collaborations, mergers, and acquisition strategies for business expansion and innovative product development to maintain their market positioning.

Recent Developments

In February 2025, Researchers from the Fraunhofer Institute for Laser Technology ILT worked together with project partners to develop an AI module to help with laser material deposition. This technique is used to repair worn bucket teeth and other tools used in mining. The partners of this project are Canadian companies and research centers. There is a tremendous amount of wear and tear on mining machines.
In September 2024, alwaysAI and Becker Mining Systems partnered to revolutionize the mining industry with cutting-edge Vision AI solutions. The partnership aims to increase productivity, safety, and operational efficiency by developing advanced computer vision applications that address challenges such as restricted area monitoring, PPE compliance, and equipment utilization monitoring. The goal is to reduce reliance on manual supervision, increase efficiency, reduce costs reduction, and increase revenue.
In September 2024, Hexagon collaborated with Augment Technologies, introduced Hexagon Blast Movement Intelligence (BMI), an AI-powered 3D blast movement solution. BMI boosts blasting efficiency and provides high visibility into ore dilution, allowing for informed delineation post-blast. It maximizes yield without needing workers to proceed on the compost pile by using a dump block model.

The Report Covers:

Market value data analysis of 2024 and forecast to 2035.
Annualized market revenues ($ million) for each market segment.
Country-wise analysis of major geographical regions.
Key companies operating in the global AI in mining market. Based on the availability of data, information related to new products and relevant news is also available in the report.
Analysis of business strategies by identifying the key market segments positioned for strong growth in the future.
Analysis of market-entry and market expansion strategies.
Competitive strategies by identifying 'who-stands-where' in the market.

Product Code: OMR2028762

1. Report Summary

Current Industry Analysis and Growth Potential Outlook
Global AI in Mining Market Sales Analysis - Component | Technology | Application ($ Million)
AI in Mining Market Sales Performance of Top Countries
1.1. Research Methodology
Primary Research Approach
Secondary Research Approach
1.2. Market Snapshot

2. Market Overview and Insights

2.1. Scope of the Study
2.2. Analyst Insight & Current Market Trends
- 2.2.1. Key AI in Mining Market Trends
- 2.2.2. Market Recommendations

3. Market Determinants

3.1. Market Drivers
- 3.1.1. Drivers For Global AI in Mining Market: Impact Analysis
3.2. Market Pain Points and Challenges
- 3.2.1. Restraints For Global AI in Mining Market: Impact Analysis
3.3. Market Opportunities
- 3.3.1. Opportunities For Global AI in Mining Market: Impact Analysis

4. Competitive Landscape

4.1. Competitive Dashboard - AI in Mining Market Revenue and Share by Manufacturers
AI in Mining Solution Comparison Analysis
Top Market Player Ranking Matrix
4.2. Key Company Analysis
- 4.2.1. ABB Ltd.
  - 4.2.1.1. Overview
  - 4.2.1.2. Product Portfolio
  - 4.2.1.3. Financial Analysis (Subject to Data Availability)
  - 4.2.1.4. SWOT Analysis
  - 4.2.1.5. Business Strategy
- 4.2.2. Caterpillar Inc.
  - 4.2.2.1. Overview
  - 4.2.2.2. Product Portfolio
  - 4.2.2.3. Financial Analysis (Subject to Data Availability)
  - 4.2.2.4. SWOT Analysis
  - 4.2.2.5. Business Strategy
- 4.2.3. IBM Corp.
  - 4.2.3.1. Overview
  - 4.2.3.2. Product Portfolio
  - 4.2.3.3. Financial Analysis (Subject to Data Availability)
  - 4.2.3.4. SWOT Analysis
  - 4.2.3.5. Business Strategy
- 4.2.4. Komatsu Ltd.
  - 4.2.4.1. Overview
  - 4.2.4.2. Product Portfolio
  - 4.2.4.3. Financial Analysis (Subject to Data Availability)
  - 4.2.4.4. SWOT Analysis
  - 4.2.4.5. Business Strategy
- 4.2.5. Microsoft Corp.
  - 4.2.5.1. Overview
  - 4.2.5.2. Product Portfolio
  - 4.2.5.3. Financial Analysis (Subject to Data Availability)
  - 4.2.5.4. SWOT Analysis
  - 4.2.5.5. Business Strategy
4.3. Top Winning Strategies by Market Players
- 4.3.1. Merger and Acquisition
- 4.3.2. Product Launch
- 4.3.3. Partnership And Collaboration

5. Global AI in Mining Market Sales Analysis by Component ($ Million)

5.1. Hardware
5.2. Software
5.3. Service

6. Global AI in Mining Market Sales Analysis by Technology ($ Million)

6.1. Machine Learning
6.2. Computer Vision
6.3. Natural Language Processing
6.4. Robotics
6.5. Data Analytics

7. Global AI in Mining Market Sales Analysis by Application ($ Million)

7.1. Exploration
7.2. Geology
7.3. Ore Sorting
7.4. Equipment Maintenance
7.5. Safety And Risk Management
7.6. Autonomous Drilling
7.7. Hauling

8. Regional Analysis

8.1. North American AI in Mining Market Sales Analysis - Component | Technology | Application ($ Million)
Macroeconomic Factors for North America
- 8.1.1. United States
- 8.1.2. Canada
8.2. European AI in Mining Market Sales Analysis - Component | Technology | Application ($ Million)
Macroeconomic Factors for Europe
- 8.2.1. UK
- 8.2.2. Germany
- 8.2.3. Italy
- 8.2.4. Spain
- 8.2.5. France
- 8.2.6. Russia
- 8.2.7. Rest of Europe
8.3. Asia-Pacific AI in Mining Market Sales Analysis - Component | Technology | Application ($ Million)
Macroeconomic Factors for Asia-Pacific
- 8.3.1. China
- 8.3.2. Japan
- 8.3.3. South Korea
- 8.3.4. India
- 8.3.5. Australia & New Zealand
- 8.3.6. ASEAN Countries (Thailand, Indonesia, Vietnam, Singapore, And Other)
- 8.3.7. Rest of Asia-Pacific
8.4. Rest of the World AI in Mining Market Sales Analysis - Component | Technology | Application ($ Million)
Macroeconomic Factors for the Rest of the World
- 8.4.1. Latin America
- 8.4.2. Middle East and Africa

9. Company Profiles

9.1. ABB Ltd.
- 9.1.1. Quick Facts
- 9.1.2. Company Overview
- 9.1.3. Product Portfolio
- 9.1.4. Business Strategies
9.2. Accenture PLC
- 9.2.1. Quick Facts
- 9.2.2. Company Overview
- 9.2.3. Product Portfolio
- 9.2.4. Business Strategies
9.3. Autonomous Solutions, Inc.
- 9.3.1. Quick Facts
- 9.3.2. Company Overview
- 9.3.3. Product Portfolio
- 9.3.4. Business Strategies
9.4. Caterpillar Inc.
- 9.4.1. Quick Facts
- 9.4.2. Company Overview
- 9.4.3. Product Portfolio
- 9.4.4. Business Strategies
9.5. Dassault Systemes SolidWorks Corp.
- 9.5.1. Quick Facts
- 9.5.2. Company Overview
- 9.5.3. Product Portfolio
- 9.5.4. Business Strategies
9.6. Hexagon AB
- 9.6.1. Quick Facts
- 9.6.2. Company Overview
- 9.6.3. Product Portfolio
- 9.6.4. Business Strategies
9.7. Hitachi Construction Machinery Co., Ltd.
- 9.7.1. Quick Facts
- 9.7.2. Company Overview
- 9.7.3. Product Portfolio
- 9.7.4. Business Strategies
9.8. IBM Corp.
- 9.8.1. Quick Facts
- 9.8.2. Company Overview
- 9.8.3. Product Portfolio
- 9.8.4. Business Strategies
9.9. Infosys, Ltd.
- 9.9.1. Quick Facts
- 9.9.2. Company Overview
- 9.9.3. Product Portfolio
- 9.9.4. Business Strategies
9.10. Komatsu Ltd.
- 9.10.1. Quick Facts
- 9.10.2. Company Overview
- 9.10.3. Product Portfolio
- 9.10.4. Business Strategies
9.11. Metso Corp.
- 9.11.1. Quick Facts
- 9.11.2. Company Overview
- 9.11.3. Product Portfolio
- 9.11.4. Business Strategies
9.12. Microsoft Corp.
- 9.12.1. Quick Facts
- 9.12.2. Company Overview
- 9.12.3. Product Portfolio
- 9.12.4. Business Strategies
9.13. Micromine
- 9.13.1. Quick Facts
- 9.13.2. Company Overview
- 9.13.3. Product Portfolio
- 9.13.4. Business Strategies
9.14. Rio Tinto Group of Companies
- 9.14.1. Quick Facts
- 9.14.2. Company Overview
- 9.14.3. Product Portfolio
- 9.14.4. Business Strategies
9.15. Sandvik AB
- 9.15.1. Quick Facts
- 9.15.2. Company Overview
- 9.15.3. Product Portfolio
- 9.15.4. Business Strategies
9.16. Sap SE
- 9.16.1. Quick Facts
- 9.16.2. Company Overview
- 9.16.3. Product Portfolio
- 9.16.4. Business Strategies
9.17. Schneider Electric SE
- 9.17.1. Quick Facts
- 9.17.2. Company Overview
- 9.17.3. Product Portfolio
- 9.17.4. Business Strategies
9.18. Siemens AG
- 9.18.1. Quick Facts
- 9.18.2. Company Overview
- 9.18.3. Product Portfolio
- 9.18.4. Business Strategies
9.19. The Weir Group PLC
- 9.19.1. Quick Facts
- 9.19.2. Company Overview
- 9.19.3. Product Portfolio
- 9.19.4. Business Strategies
9.20. Wipro Ltd.
- 9.20.1. Quick Facts
- 9.20.2. Company Overview
- 9.20.3. Product Portfolio
- 9.20.4. Business Strategies