Artificial Intelligence (AI) in Mining - Thematic Research
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Artificial Intelligence (AI) in Mining - Thematic Research
Summary
The mining industry is under more pressure than ever to increase efficiencies. This comes as declining ore grades and more disparate and remote deposits create greater challenges in securing new resources, and rising mining costs drive the need for greater productivity at the mine site. At the same time, there is a strong focus on ensuring safety and sustainability within mines. Artificial intelligence (AI) can address many of these challenges and inefficiencies through several key technologies in the value chain, including computer vision, smart robots, data science, and machine learning.
As the value chain is fragmented, effective collaboration and communication between multiple stakeholders is key. AI-powered tools can reduce exploration costs through the use of drones and computer vision to identify the most likely locations of mineral deposits. Predictive maintenance can ensure that equipment defects are solved before they become extremely costly and ensure that equipment downtime is kept to a minimum, increasing productivity. Smart sensors and cameras aid automated equipment while also monitoring the safety of workers in mines. Productivity in mining has stagnated, and no company can afford to miss the benefits that AI can offer. Using the information in this report, you can formulate an AI strategy for your business.
Key Highlights
- Although uptake of AI technologies in mining has been slow, AI has the potential to deliver tangible benefits across the mining value chain, from discovery through to extraction and maintenance. GlobalData predicts that mining companies will spend $218m annually on AI platforms by 2024.
- Analysis of the impact of AI on the mining industry and how AI technologies can be used to resolve challenges related to cost control, supply chain, productivity, and safety.
- The most expensive disclosed AI-related acquisition in mining since 2016 was Pegasus Aces acquisition of Casetek Holdings, a metal parts manufacturer.
- A case study showing how Goldspots used AI to identify new gold targets and streamline its drilling processes in Canada.
Scope
- The challenges facing the mining industry, together with thorough discussion of how AI can help mitigate these challenges, as well as identify companies for partnerships.
- Global market size (2019) and forecast (2020-2024) of AI platform revenues in the mining industry compiled by GlobalData technology and mining analysts.
- Key mergers and acquisitions (M&As) associated with the AI theme in the mining sector over the last five years including date, deal value, and a brief description of the target company as compiled from GlobalDatas Deals database.
- Profiles of over 25 AI vendors including specialists in the mining industry with details of their AI services.
- Profiles of leading adopters of AI in mining including who they have partnered with for their AI initiatives.
- Case studies of AI implementation in the mining sector and how AI has been used to increase productivity, reduce costs, find new deposits, and improve safety on the mine site.
- Unique thematic scorecard showing 50 global mining companies that predicts the success of mining companies in the next 2-5 years. Companies are scored in 10 themes that will disrupt the mining sector, informed by GlobalDatas comprehensive tracking of AI related deals, job openings, patents ownership, company news, financial and marketing statements.
Reasons to Buy
- Determine and prioritise which AI technologies to invest in at each step of the mining value chain.
- Discover case studies where AI has been used to overcome challenges faced by companies
- Quickly identify leading specialist AI vendors in mining and shortlist potential partners based on their areas of expertise and historic partnerships
- As a technology vendor, identify the areas where the industry is most in need of your services and uncover the areas that are lacking specific AI vendors that might prove profitable areas for expansion. Quantify the global sales opportunity for AI services to the mining industry by accessing GlobalDatas market size and forecasts (2019-2024), produced by our mining and technology analysts.
- Formulate marketing messages that resonate with buyers in the mining sector by identifying the key challenges that the sector faces and understanding how AI is impacting the sector
Summary
The mining industry is under more pressure than ever to increase efficiencies. This comes as declining ore grades and more disparate and remote deposits create greater challenges in securing new resources, and rising mining costs drive the need for greater productivity at the mine site. At the same time, there is a strong focus on ensuring safety and sustainability within mines. Artificial intelligence (AI) can address many of these challenges and inefficiencies through several key technologies in the value chain, including computer vision, smart robots, data science, and machine learning.
As the value chain is fragmented, effective collaboration and communication between multiple stakeholders is key. AI-powered tools can reduce exploration costs through the use of drones and computer vision to identify the most likely locations of mineral deposits. Predictive maintenance can ensure that equipment defects are solved before they become extremely costly and ensure that equipment downtime is kept to a minimum, increasing productivity. Smart sensors and cameras aid automated equipment while also monitoring the safety of workers in mines. Productivity in mining has stagnated, and no company can afford to miss the benefits that AI can offer. Using the information in this report, you can formulate an AI strategy for your business.
Key Highlights
- Although uptake of AI technologies in mining has been slow, AI has the potential to deliver tangible benefits across the mining value chain, from discovery through to extraction and maintenance. GlobalData predicts that mining companies will spend $218m annually on AI platforms by 2024.
- Analysis of the impact of AI on the mining industry and how AI technologies can be used to resolve challenges related to cost control, supply chain, productivity, and safety.
- The most expensive disclosed AI-related acquisition in mining since 2016 was Pegasus Aces acquisition of Casetek Holdings, a metal parts manufacturer.
- A case study showing how Goldspots used AI to identify new gold targets and streamline its drilling processes in Canada.
Scope
- The challenges facing the mining industry, together with thorough discussion of how AI can help mitigate these challenges, as well as identify companies for partnerships.
- Global market size (2019) and forecast (2020-2024) of AI platform revenues in the mining industry compiled by GlobalData technology and mining analysts.
- Key mergers and acquisitions (M&As) associated with the AI theme in the mining sector over the last five years including date, deal value, and a brief description of the target company as compiled from GlobalDatas Deals database.
- Profiles of over 25 AI vendors including specialists in the mining industry with details of their AI services.
- Profiles of leading adopters of AI in mining including who they have partnered with for their AI initiatives.
- Case studies of AI implementation in the mining sector and how AI has been used to increase productivity, reduce costs, find new deposits, and improve safety on the mine site.
- Unique thematic scorecard showing 50 global mining companies that predicts the success of mining companies in the next 2-5 years. Companies are scored in 10 themes that will disrupt the mining sector, informed by GlobalDatas comprehensive tracking of AI related deals, job openings, patents ownership, company news, financial and marketing statements.
Reasons to Buy
- Determine and prioritise which AI technologies to invest in at each step of the mining value chain.
- Discover case studies where AI has been used to overcome challenges faced by companies
- Quickly identify leading specialist AI vendors in mining and shortlist potential partners based on their areas of expertise and historic partnerships
- As a technology vendor, identify the areas where the industry is most in need of your services and uncover the areas that are lacking specific AI vendors that might prove profitable areas for expansion. Quantify the global sales opportunity for AI services to the mining industry by accessing GlobalDatas market size and forecasts (2019-2024), produced by our mining and technology analysts.
- Formulate marketing messages that resonate with buyers in the mining sector by identifying the key challenges that the sector faces and understanding how AI is impacting the sector
TABLE OF CONTENTS
Table of Contents
Executive Summary
AI value chain
Key players in the AI value chain
Machine learning
ML is integral to data-driven businesses
Data science
The data science platform market is booming
Demand for pre- and post-deployment services increase
Talent is scarce and in high demand
Conversational platforms
COVID-19 boosts demand for virtual agents
Which vendors are best-positioned in the conversational platform market?
Computer vision
Facial recognition is a hot topic
Computer vision as a service is driving adoption
AI chips
Homegrown processors are putting pressure on established leaders
Legacy suppliers are stepping up their game
Chinas influence will increase
Smart robots
The co-bot market will grow sharply
Care robots are a small but increasingly important segment
Context-aware computing
There are a wide range of industry-specific applications
Mining challenges
The impact of AI on mining
Case studies
Agnico Eagles use of AI for predictive maintenance
KGHM Polska Mied uses AI to streamline the refuelling process
Jiangzhuang Coal Mines use of Hikvision to reduce accidents
Goldspots use of AI to identify new gold targets
Rio Tintos autonomous train haulage
Fortescues autonomous systems
Barrick uses to AI detects microsleep and distracted driving to improve safety
Freeport McMoRan using AI modeling to increase mill efficiency
FLSmidth increasing processing efficiency through AI
Market size and growth forecasts
Mergers and acquisitions
AI timeline
Companies
Leading AI adopters in mining
Leading AI vendors
Specialist AI vendors in mining
Sector scorecard
Mining sector scorecard
Whos who
Thematic screen
Valuation screen
Glossary
Further reading
GlobalData reports
Our thematic research methodology
About GlobalData
Contact Us
Table 1: Mining challenges
Table 2: Mergers and acquisitions
Table 3: Companies
Table 4: Leading AI vendors
Table 5: Specialist AI vendors in mining
Table 6: Glossary
Table 7: GlobalData reports
Figure 1: Key players in the AI value chain
Figure 2: The AI value chain
Figure 3: Big Tech is dominating ML
Figure 4: Machine learning: leaders and disruptors
Figure 5: Data science: leaders and disruptors
Figure 6: Virtual assistants are now part of everyday life, and they are getting more sophisticated
Figure 7: The enterprise landscape looks different from the consumer one
Figure 8: Conversational platforms: leaders and disruptors
Figure 9: There are four key CV software technologies
Figure 10: Computer vision: leaders and disruptors
Figure 11: There are several types of AI chips
Figure 12: AI chips: leaders and disruptors
Figure 13: Smart robots: leaders and disruptors
Figure 14: Context-aware computing: leaders and disruptors
Figure 15: Is your business currently investing in the following technologies?
Figure 16: Thematic impact assessment
Figure 17: Global AI platform revenue in mining will reach $218m by 2024, up from $76m in 2019
Figure 18: The AI story
Figure 19: Who does what in the mining space?
Figure 20: Our thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance.
Figure 21: Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics.
Executive Summary
AI value chain
Key players in the AI value chain
Machine learning
ML is integral to data-driven businesses
Data science
The data science platform market is booming
Demand for pre- and post-deployment services increase
Talent is scarce and in high demand
Conversational platforms
COVID-19 boosts demand for virtual agents
Which vendors are best-positioned in the conversational platform market?
Computer vision
Facial recognition is a hot topic
Computer vision as a service is driving adoption
AI chips
Homegrown processors are putting pressure on established leaders
Legacy suppliers are stepping up their game
Chinas influence will increase
Smart robots
The co-bot market will grow sharply
Care robots are a small but increasingly important segment
Context-aware computing
There are a wide range of industry-specific applications
Mining challenges
The impact of AI on mining
Case studies
Agnico Eagles use of AI for predictive maintenance
KGHM Polska Mied uses AI to streamline the refuelling process
Jiangzhuang Coal Mines use of Hikvision to reduce accidents
Goldspots use of AI to identify new gold targets
Rio Tintos autonomous train haulage
Fortescues autonomous systems
Barrick uses to AI detects microsleep and distracted driving to improve safety
Freeport McMoRan using AI modeling to increase mill efficiency
FLSmidth increasing processing efficiency through AI
Market size and growth forecasts
Mergers and acquisitions
AI timeline
Companies
Leading AI adopters in mining
Leading AI vendors
Specialist AI vendors in mining
Sector scorecard
Mining sector scorecard
Whos who
Thematic screen
Valuation screen
Glossary
Further reading
GlobalData reports
Our thematic research methodology
About GlobalData
Contact Us
List Of Tables
List of TablesTable 1: Mining challenges
Table 2: Mergers and acquisitions
Table 3: Companies
Table 4: Leading AI vendors
Table 5: Specialist AI vendors in mining
Table 6: Glossary
Table 7: GlobalData reports
List Of Figures
List of FiguresFigure 1: Key players in the AI value chain
Figure 2: The AI value chain
Figure 3: Big Tech is dominating ML
Figure 4: Machine learning: leaders and disruptors
Figure 5: Data science: leaders and disruptors
Figure 6: Virtual assistants are now part of everyday life, and they are getting more sophisticated
Figure 7: The enterprise landscape looks different from the consumer one
Figure 8: Conversational platforms: leaders and disruptors
Figure 9: There are four key CV software technologies
Figure 10: Computer vision: leaders and disruptors
Figure 11: There are several types of AI chips
Figure 12: AI chips: leaders and disruptors
Figure 13: Smart robots: leaders and disruptors
Figure 14: Context-aware computing: leaders and disruptors
Figure 15: Is your business currently investing in the following technologies?
Figure 16: Thematic impact assessment
Figure 17: Global AI platform revenue in mining will reach $218m by 2024, up from $76m in 2019
Figure 18: The AI story
Figure 19: Who does what in the mining space?
Figure 20: Our thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance.
Figure 21: Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics.
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