AI In Mining Market To Hit USD 478.29 Billion By 2032, Fueled By Automation, Predictive Maintenance, And Cloud Adoption Globally Report By SNS Insider

(MENAFN- GlobeNewsWire - Nasdaq) AI in Mining Market growing rapidly, driven by automation, predictive maintenance, cloud adoption, and expanding smart applications.

Austin, Sept. 05, 2025 (GLOBE NEWSWIRE) -- The AI in Mining Market size was valued at USD 28.91 billion in 2024 and is projected to reach USD 478.29 billion by 2032, expanding at a robust CAGR of 42.15% over 2025-2032.

The rapid growth is fueled by the rising adoption of automation, predictive maintenance, and data-driven decision-making in mining operations. AI technologies enhance safety, reduce operational costs, and optimize resource utilization by enabling real-time monitoring of equipment and predictive analytics for risk management. Additionally, increasing demand for sustainable mining practices, improved ore discovery, and energy efficiency further drive AI integration, making it a transformative force in the global mining sector.

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The U.S. AI in Mining Market size was valued at USD 7.07 billion in 2024 and is projected to reach USD 114.90 billion by 2032, growing at a CAGR of 41.69% over 2025-2032. Growth is driven by strong investments in smart mining technologies, demand for operational efficiency, enhanced safety measures, and the push for sustainable, data-driven resource management.

Key Companies:

• Accenture
• IBM
• SAP
• Microsoft
• Minerva Intelligence
• Goldspot Discoveries Inc.
• Kore Geosystems
• DroneDeploy
• Datarock
• Earth AI
• ABB
• Sandvik
• Caterpillar
• Komatsu
• BHP
• Rio Tinto
• Rockwell Automation
• Hexagon AB
• Hitachi Construction Machinery

AI In Mining Market Report Scope:

Report Attributes	Details
Market Size in 2024	USD 28.91 Billion
Market Size by 2032	USD 478.29 Billion
CAGR	CAGR of 42.15% From 2025 to 2032
Base Year	2024
Forecast Period	2025-2032
Historical Data	2021-2023
Report Scope & Coverage	Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments	. By Mining Type (Surface Mining, Underground Mining, Others) . By Technology (Machine Learning & Deep Learning, Robotics & Automation, Computer Vision, NLP, Others) . By Deployment (Cloud, On-premises, Hybrid) . By Application (Ore Fragmentation Assessment, Site Inspections, Equipment Maintenance, Autonomous Drilling, Pre & Post Blast Surveys, Others)
Customization Scope	Available upon request
Pricing	Available upon request

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By Mining Type, Surface Mining Leads AI in Mining Market in 2024 with 59% Revenue Share

Surface mining dominated the AI in Mining Market in 2024, securing a 59% revenue share. Its leadership is attributed to large-scale operations, high equipment density, and ease of automation. Open-pit mines leverage AI for fleet management, drone mapping, and real-time environmental monitoring. Accessible terrain and vast resource volumes make surface mining ideal for AI integration, optimizing logistics, safety, and resource extraction.

By Technology, Computer Vision Poised for 46% CAGR Growth in AI in Mining Market from 2025–2032

Computer vision is projected to expand at a CAGR of 46% over 2025–2032, propelled by its rising application in visual inspection, autonomous vehicles, and continuous monitoring. AI-powered image recognition enhances safety surveillance, ore quality assessment, and equipment fault detection. Growing adoption of vision-based automation boosts operational visibility and hazard detection across both surface and underground mining environments.

By Deployment, Cloud-Based AI Solutions Dominate AI in Mining Market in 2024 with 43% Revenue Share

In 2024, cloud-based AI solutions led the AI in Mining Market with a 43% revenue share. Their dominance stems from scalable infrastructure, real-time data accessibility, and centralized AI model training and deployment. Mining firms leverage cloud platforms for remote monitoring across multiple sites, minimizing physical infrastructure needs while benefiting from lower upfront costs, flexible integration, and enhanced operational intelligence.

By Application, Equipment Maintenance Leads the AI in Mining Market in 2024 with 24% Revenue Share

In 2024, equipment maintenance emerged as the leading segment in the AI in Mining Market, capturing 24% revenue share. The dominance stems from mining companies adopting AI-powered predictive diagnostics and condition monitoring to minimize equipment failure, prevent costly downtime, and extend machinery lifespan. This highlights the industry's strong focus on asset reliability, efficiency, and cost optimization through AI-enabled maintenance solutions.

North America Dominates AI in Mining Market in 2024 with 34%, Asia Pacific Forecasted to Record Fastest 44.39% CAGR

In 2024, North America led the AI in Mining Market with a 34% revenue share. The region's dominance stems from advanced digital infrastructure, significant R&D spending, and early adoption of automation. Mining leaders in the U.S. and Canada utilize AI for equipment monitoring, autonomous operations, and environmental compliance. Government support for sustainable practices and tech-driven mining hubs further strengthens its market leadership.

Asia Pacific is expected to grow at the fastest CAGR of 44.39% over 2025-2032. Growth is driven by rapid industrialization, rising mineral demand, and large-scale mining projects in China, Australia, and India. Regional governments promote smart mining initiatives to enhance efficiency and safety. Emerging market players and local tech startups are accelerating AI adoption, fostering digital transformation across diverse mining environments.

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Exclusive Sections of the Report (The USPs) – Check Section 5

• USP 1 – Mining Process Optimization Insights

Provides clients with AI-driven efficiency benchmarks for drilling, blasting, ore sorting, and haulage.

• USP 2 – Predictive Maintenance & Equipment Intelligence

Helps reduce downtime and maintenance costs through AI-enabled asset health monitoring.

• USP 3 – Safety & Risk Mitigation Analytics

Shows how AI enhances worker safety, hazard detection, and operational risk management.

• USP 4 – Energy & Sustainability Impact Assessment

Identifies AI applications in reducing energy consumption, emissions, and environmental footprint.

• USP 5 – Digital Twin & Simulation Use Cases

Helps clients explore real-time mine simulations for planning, productivity, and cost optimization.

• USP 6 – AI Adoption Barriers & ROI Roadmap

Provides clarity on integration challenges, cost-benefit analysis, and ROI expectations for AI in mining.

• USP 7 – Case Studies & Competitive Best Practices

Brings global success stories and competitor strategies to guide effective AI adoption.

About Us:

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