• Written by Muhammed Abdulla NC, CEO

A spreadsheet used to track maintenance backlog in a metals and mining operation often reveals a stark reality: capable systems go unused, and the reasons for this are deeply rooted in the industry's unique challenges. The consequences of this underutilization can be severe, leading to decreased efficiency and increased costs. In such cases, the business consequence is a significant loss of revenue, which can be attributed to the mechanism of grade variability, maintenance backlog, and energy intensity.

Industry-specific challenges

The metals and mining industry faces distinct challenges that make it harder for enterprise AI adoption to take hold. Grade variability, for instance, refers to the inherent uncertainty in the quality and composition of mined materials, which can significantly impact production planning and decision-making. Maintenance backlog is another major issue, as heavy assets and equipment require regular upkeep to prevent breakdowns and ensure optimal performance. Energy intensity is also a significant concern, as metals and mining operations are often energy-hungry and subject to volatile input prices.

These challenges can make it difficult to implement and utilize AI systems effectively, as they require a deep understanding of the underlying processes and variables that affect the operation. However, it is wrong to assume that these challenges are insurmountable, and that AI adoption is not feasible in the metals and mining industry.

Integrating new capability into existing routines

One way to increase the adoption of enterprise AI in metals and mining is to attach new capability to a routine that already exists. For example, a shift supervisor can use an AI-powered system to analyze data from sensors and machines, and make informed decisions about maintenance and production planning. This approach allows the AI system to augment existing processes, rather than requiring a complete overhaul of existing routines.

A category manager, for instance, can use an AI system to compare suppliers and optimize procurement decisions, taking into account factors such as price, quality, and lead time. By integrating AI into existing routines, metals and mining operations can start to realize the benefits of enterprise AI adoption, such as improved efficiency and reduced costs.

Enabling the right people to ask questions

It is not just a matter of who needs the answer, but also who needs to be able to ask the question in the first place. In metals and mining, this often means enabling front-line workers, such as shift supervisors and maintenance personnel, to ask questions and get answers from AI systems. This requires a user-friendly interface and a system that can understand the context and nuances of the operation.

For instance, a maintenance technician can use a natural language interface to ask an AI system about the optimal maintenance schedule for a particular piece of equipment, taking into account factors such as usage patterns and wear and tear. By enabling the right people to ask questions, metals and mining operations can unlock the full potential of enterprise AI adoption.

To learn more about how AsscherAi can help enable the right people to ask questions, visit our website at AsscherAi.

Rebuilding trust after the first wrong answer

The first wrong answer from an AI system can be a significant setback, as it can erode trust and confidence in the system. However, it is possible to rebuild trust by providing transparent and explainable results, and by continuously improving the system through feedback and iteration.

A worked example of this can be seen in a metals and mining operation where an AI system provides a wrong answer to a question about production planning. The system can provide an explanation of how it arrived at the answer, and the user can provide feedback to improve the system. Over time, the system can learn from its mistakes and provide more accurate and reliable answers.

Limitations and wrong choices

While enterprise AI adoption can bring significant benefits to metals and mining operations, it is not a panacea for all challenges. There are cases where AI adoption may not be the right choice, such as when the operation is subject to extreme variability or uncertainty, or when the cost of implementation outweighs the potential benefits.

In such cases, it may be more effective to focus on other initiatives, such as process optimization or workforce training. To discuss your specific needs and determine whether AsscherAi is the right choice for your operation, contact us at AsscherAi.