Why is Mining Struggling to Scale AI?

As artificial intelligence (AI) continues to revolutionize various industries, mining companies are also exploring its potential to optimize operations and drive efficiency. However, despite the promising benefits, mining struggling to scale AI when it comes to the effective implementation of models that account for a dynamic environment.

One of the key challenges mining struggling to scale AI lies in the misconception surrounding abstract concepts like “plug and play” and “modular” solutions. Therefore, it’s essential to dispel the myth that AI implementation is as simple as deploying a pre-packaged model or solution.

Top Five Struggling Reasons

Miners often operate in diverse environments with varying configurations and specific operational requirements. These can include differences in geological conditions, ore characteristics, machinery types, extraction methods, and regulatory constraints. But generally, these are the top key reasons why mining companies may struggle to scale AI initiatives:

Customer implementation

1. Data Accessibility and Quality: Mining operations generate vast amounts of data from sensors, equipment, and geological surveys. However, accessing this data in a structured format and ensuring its constant quality and reliability can be a significant challenge. Without clean and accessible data, AI algorithms may not deliver accurate insights or predictions.

2. Complexity of Mining Operations: Mining operations are inherently complex, involving numerous interconnected processes, constant ore feed variability and stakeholders. Implementing AI solutions across such diverse and intricate systems requires careful planning, coordination, and integration with existing technologies and workflows.


3. Resource Constraints: Many mining companies operate in remote locations with limited access to technical expertise, infrastructure, and resources. Implementing AI technologies often requires significant investments in hardware, software, and specialized skills, which may only sometimes be feasible for smaller or less digitally mature operations.

4. Regulatory and Compliance Challenges: The mining industry is subject to stringent regulations and compliance requirements related to safety, environmental impact, and community engagement. Integrating AI solutions into mining operations requires thorough compliance with regulatory standards, which can slow down the implementation process and increase costs.

5. Cultural and Organizational Resistance: Introducing AI technologies into traditional mining workflows may face resistance from employees accustomed to manual processes or skeptical of new technologies. Overcoming cultural barriers and fostering a culture of innovation and collaboration is essential for successful AI adoption and scalability.

The New Foundation by IntelliSense.io

Operational Technology (OT) networks face limitations in computing power. Generally, this hinders the implementation of complex machine learning (ML) models that rely on substantial historical data for accurate outputs. While ML models can initially run on OT networks, they often require frequent recalibration and adjustments. This leads to increased costs and complexity to maintain synchronization.

Without this recalibration, OT networks may resort to deploying traditional linear models. Given that, it may not effectively capture the dynamics of a dynamic and multi-variate environment over the past two decades.

Customer implementation

IntelliSense.io solutions, powered by brains.app platform, bridges this gap by offering the best of both worlds. It leverages the computational power of the cloud to execute high-fidelity computations while facilitating the deployment of optimization recommendations directly within the OT environment.

In the refered picture above, we present the ‘Thickener Performance Screen’ from our Thickener Optimization Application.  A specialized interface enabling metallurgists to visualize the actual, predicted, simulated, and optimized performance of their thickener circuit. This is possible by using our Scientific AI, that fuses together mechanistic (first principle or physics) models with Machine Learning techniques to uniquely provide real-time predictive intelligence. 

This approach ensures efficient utilization of resources and enables effective decision-making without compromising on accuracy or performance.

Taking the Next Step into AI

Mining companies must recognize the complexity of AI integration and invest in building the necessary infrastructure and capabilities to support scalable and sustainable AI initiatives. This includes prioritizing data management and quality, investing in technology infrastructure, and talent development. Straightaway fosters a culture of innovation and closely aligns AI initiatives with business objectives and regulatory requirements.

By addressing these challenges proactively, mining companies can unlock the full potential of AI to drive transformative change. Overall, helping maintain operations margins and sustainable growth in the industry.

Interested in knowing more?

Mine to Market: Value Chain Optimization

The Stockpile & Inventory Optimization Application is one of a suite of real-time decision-making applications that uses AI to optimize each process; from mine to market.

Our Material Handling model connects these applications to drive even greater efficiency.

Our process optimization apps can be deployed on a specific process bottleneck or expanded across the entire value chain. They are powered by our Industrial AI Decision Intelligence Platform, brains.app.

Stockpile and Inventory Optimization

Stockpile & Inventory Optimization

Grinding Optimization App

Grinding Optimization

Thickener Circuit Optimization

Thickener Optimization

Flotation Circuit

Flotation Optimization

Solvent Extraction Optimization App

Solvent Extraction (SX) Optimization

Leaching Optimization App

Leaching Optimization