• Written by Muhammed Abdulla NC, CEO

Can we really use AI to predict when a crusher will fail, or are we just talking about generating more reports for the maintenance team to sift through? The answer lies in where AI has actually landed in metals and mining so far, and it's not in the operations themselves, but in the office functions that support them.

Where AI has actually landed in metals and mining so far

According to the US Census Bureau's Business Trends and Outlook Survey, only 7.7% of US mining, quarrying, and oil and gas businesses reported using AI in the previous two weeks, and it's mostly in functions whose output is text, not the ones that handle physical work. This is because AI is well-suited to tasks that involve processing and generating text, such as contract management, procurement, and reporting. For example, a procurement manager can use AI to analyze contracts and identify potential risks, freeing up time to focus on more strategic tasks.

This is also reflected in the broader US business landscape, where 14.3% of businesses used AI in sales and marketing in the previous six months, against 1.7% in production of goods and 0.9% in distribution, according to the US Census Bureau's Business Trends and Outlook Survey. This suggests that AI is having a greater impact in office functions than in operational roles.

Grade variability

One area where AI is making a real difference is in grade variability, where the people dealing with it can ask the data in mine planning, fleet management, the process historian, and ERP a direct question instead of waiting for a report. For instance, a geologist can use AI to analyze data from the process historian and identify patterns that indicate changes in grade variability, allowing for more informed decisions about mine planning.

This can have a significant impact on the bottom line, as grade variability can have a major impact on the efficiency and profitability of mining operations. By being able to ask questions and get answers in real-time, geologists and other stakeholders can make more informed decisions and reduce the risk of costly mistakes.

Maintenance backlog

The maintenance backlog is another area where AI is having a real impact, as it allows for more efficient and effective decision-making. Currently, many maintenance decisions wait on a person stitching together two systems, such as the maintenance log and the ERP system. With AI, an answer in the moment can be provided, allowing for more informed decisions about maintenance priorities and resource allocation.

For example, a maintenance manager can use AI to analyze data from the maintenance log and the ERP system to identify the most critical maintenance tasks and allocate resources accordingly. This can help to reduce downtime and improve overall equipment effectiveness.

Energy intensity

Energy intensity is another area where AI is often touted as a solution, but it's harder than it looks. To really make a difference, AI needs to be able to analyze data from a variety of sources, including sensors, meters, and other systems. However, this requires a high degree of data quality and integration, which can be a challenge in many metals and mining operations.

Furthermore, energy intensity is often influenced by a complex array of factors, including equipment performance, operating practices, and external factors such as weather and commodity prices. To really make a difference, AI needs to be able to take into account these factors and provide actionable insights that can inform decision-making. This requires a deep understanding of the underlying systems and processes, as well as the ability to integrate data from multiple sources.

Why most adopters have not changed anything underneath

Despite the hype around AI, many adopters have not changed anything underneath, according to the US Census Bureau's Business Trends and Outlook Survey, where 64.3% of businesses made no change of any kind to adopt AI, and only 6.8% changed how they collect or manage data. This is a problem, as AI is only as good as the data it's trained on, and if the underlying data is poor quality or incomplete, the insights provided by AI will be limited.

This is why it's so important to have a solid data management strategy in place before implementing AI, as well as a clear understanding of what you're trying to achieve. Without this, AI is unlikely to deliver the benefits that it promises. For more information on how to get started with AI, contact us to speak with one of our experts.

What a sensible first quarter looks like for a metals and mining business

A sensible first quarter for a metals and mining business looking to implement AI might involve focusing on one question, one system, and one team. For example, a business might focus on using AI to analyze data from the maintenance log to identify patterns and trends that can inform maintenance decisions. This allows for a focused and targeted approach, and helps to build momentum and credibility for the AI initiative.

It's also important to have a clear understanding of what you're trying to achieve, and to have a solid plan in place for how you're going to get there. This might involve working with a vendor or partner to implement the AI solution, or it might involve building the capability in-house. Either way, it's essential to have a clear roadmap and to be able to measure progress along the way. For more information on how to implement AI in your operations, visit our operations page.

What this does not do, or when it is the wrong choice

While AI has the potential to make a significant impact in metals and mining, it's not a silver bullet, and there are many cases where it's not the right solution. For example, if you're looking to implement a new ERP system, AI might not be the best place to start, as it's unlikely to deliver the benefits you're looking for without a solid foundation of data and process. Similarly, if you're looking to improve safety performance, AI might not be the best solution, as it's unlikely to deliver the benefits you're looking for without a deep understanding of the underlying factors that influence safety performance.

For more information on how to get started with AI in metals and mining, and to learn more about the potential benefits and pitfalls, read our blog on AI and IT investment in metals. This provides a detailed analysis of the opportunities and challenges associated with AI in metals and mining, and offers practical advice on how to get started with an AI initiative.