• Written by Azhar Aliyar, Director

A typical metals and mining business has a spreadsheet that tracks every system, integration, and report, with a separate tab for each new question that needs answering, and a steadily growing total cost of ownership that seems impossible to reverse. The IT function is under pressure to reduce this cost, but the traditional approach of adding more systems and integrations only makes the problem worse.

Where IT money actually goes in a metals and mining business

Most IT spending in metals and mining goes into getting answers out of systems rather than running them, with a significant portion dedicated to building and maintaining reports, dashboards, and integrations. This is because the business is constantly asking new questions, and the existing systems are not designed to provide the answers easily. According to the US Census Bureau's Business Trends and Outlook Survey, 22.4% of US businesses reported using AI in the two weeks to July 2026, but in the metals and mining sector, this figure is much lower, at 7.7%.

This low adoption rate is partly due to the complexity of the systems and the data they hold, which makes it difficult to integrate AI solutions. However, it is also due to the lack of understanding of where IT money is actually being spent, and how AI can help reduce these costs. By understanding the true cost of ownership of their systems, metals and mining businesses can start to make informed decisions about where to apply AI to reduce IT costs.

The report and dashboard backlog

The report and dashboard backlog is a major contributor to IT costs in metals and mining, with each new question requiring a new report or dashboard to be built. This is a costly and time-consuming process, and one that can be replaced by AI-powered question answering systems. These systems can provide answers to ad-hoc questions without the need for a new report or dashboard to be built, freeing up IT resources to focus on more strategic tasks.

For example, a category manager might ask a question about the current stock levels of a particular material, and the AI system can provide an answer in real-time, without the need for a new report to be built. This can help reduce the report and dashboard backlog, and free up IT resources to focus on more strategic tasks, such as executive and strategic leadership.

Extending mine planning, fleet management, the process historian and ERP

Rather than replacing existing systems, AI can be used to extend their capabilities, and provide better answers to the questions being asked. For example, AI can be used to analyze data from the process historian to provide insights into plant performance, or to optimize mine planning and fleet management. This can help defer a replacement programme, and reduce the costs associated with implementing new systems.

By asking better questions of the systems they already own, metals and mining businesses can get more value out of their existing investments, and reduce the need for new systems and integrations. This approach can also help reduce the costs associated with data migration and integration, and provide a more rapid return on investment.

Integration spend

Integration spend is another major contributor to IT costs in metals and mining, with each new system or application requiring a new integration to be built. However, AI can help reduce these costs by joining data at the point of the question, rather than building and maintaining a point-to-point link for each one. This approach can help reduce the complexity and cost of integrations, and provide a more flexible and scalable solution.

For example, a shift supervisor might need to access data from multiple systems to answer a question about plant performance, and the AI system can provide this data in real-time, without the need for a new integration to be built. This can help reduce the integration backlog, and free up IT resources to focus on more strategic tasks, such as transforming metals and mining operations.

Where AI adds cost rather than removing it

While AI can help reduce IT costs in metals and mining, it is not a panacea, and there are areas where it can add cost rather than removing it. For example, the data work and governance required to implement an AI solution can be significant, and cannot be skipped. This includes data cleansing, data normalization, and data governance, all of which require significant resources and investment.

However, by budgeting for these costs honestly, and understanding where AI can add value, metals and mining businesses can make informed decisions about where to apply AI to reduce IT costs. This includes understanding the costs associated with data preparation, and the benefits of using AI to provide better answers to the questions being asked. To get started, businesses can contact us to discuss their specific needs and requirements.

How to tell whether IT spending actually fell

To determine whether IT spending has actually fallen, metals and mining businesses need to measure the reduction in requests and licences, rather than relying on projections or estimates. This includes tracking the number of reports and dashboards being built, and the number of integrations being implemented, as well as the costs associated with these activities.

By tracking these metrics, businesses can get a clear understanding of where AI is adding value, and where it is not. This can help inform future investment decisions, and ensure that AI is being used to reduce IT costs in a meaningful and sustainable way. According to the US Census Bureau's Business Trends and Outlook Survey, 14.3% of US businesses used AI in sales and marketing in the previous six months, and 11.4% used AI in their information technology function, but the key is to understand where AI is being used to reduce costs, rather than just adopting it for its own sake.

What this does not do

This approach to reducing IT costs in metals and mining using AI does not replace the need for strategic planning and investment in IT. It is not a substitute for a well-planned IT strategy, and it is not a silver bullet that can solve all IT cost problems. However, it can be a useful tool in the right circumstances, and can help metals and mining businesses reduce their IT costs and improve their operations.

For example, if a business is already planning to replace its existing systems, then AI may not be the best solution. However, if a business is looking to extend the life of its existing systems, and reduce the costs associated with integrations and report building, then AI can be a valuable addition to its IT strategy. By understanding the limitations and potential of AI, metals and mining businesses can make informed decisions about where to apply it to reduce IT costs.