• Written by Sonali Subhadarsini, Product Manager

A shift supervisor at a metals and mining operation checks the maintenance log for the day's schedule, only to find that a critical piece of equipment is still offline due to a parts delay, which will likely impact production targets for the week. This is a familiar scenario in an industry where grade variability, maintenance backlog, and energy intensity can make it difficult to maintain productivity. The introduction of AI-powered tools, such as AsscherAi, aims to help alleviate some of these challenges by providing real-time insights into an organisation's own data.

Industry-specific challenges

The metals and mining industry is unique in that it faces a number of challenges that can impact productivity, including grade variability, which can affect the quality and quantity of output, maintenance backlog, which can lead to equipment downtime and reduced capacity, and energy intensity, which can increase costs and reduce profitability. These factors can make it difficult for organisations to maintain a consistent level of production, and can also impact the effectiveness of AI-powered tools. For example, a category manager may struggle to optimise supply chains when input prices are volatile, making it challenging to predict and plan for future needs.

The impact of these challenges can be seen in the day-to-day operations of a metals and mining organisation. A maintenance manager may need to prioritise repairs based on the availability of parts and the criticality of the equipment, while a production manager may need to adjust production schedules to account for changes in grade variability. These decisions can have a significant impact on productivity, and can be supported by the use of AI-powered tools that provide real-time insights into an organisation's own data.

The difference between time saved and time redeployed

While AI-powered tools can help save time by automating certain tasks and providing real-time insights, the actual impact on productivity may be more complex. Time saved is not always time redeployed, as employees may not always be able to use the time saved to focus on higher-value tasks. For example, a shift supervisor may save time by using AsscherAi to quickly answer questions about production data, but may not be able to redeploy that time to focus on process improvements or other strategic initiatives.

This distinction is important, as it highlights the need for organisations to think carefully about how they will use the time saved through the introduction of AI-powered tools. Simply saving time is not enough; organisations must also consider how they will redeploy that time to drive real productivity gains. This may involve providing training and support to employees to help them develop new skills, or identifying new initiatives and projects that can help drive business value.

Self-reported time savings

Self-reported time savings can be a misleading metric, as they often overstate the actual impact of AI-powered tools on productivity. Employees may report that they have saved time, but may not always be able to quantify the actual impact of that time savings. Additionally, self-reported time savings may not account for other factors that can impact productivity, such as changes in grade variability or maintenance backlog.

For example, a category manager may report that they have saved several hours per week by using AsscherAi to answer questions about supplier data, but may not be able to provide detailed information about how that time has been redeployed. This lack of clarity can make it difficult for organisations to understand the actual impact of AI-powered tools on productivity, and can lead to overestimation of the benefits. To get a more accurate picture, organisations may need to use more objective metrics, such as data on production volumes or quality, to assess the impact of AI-powered tools.

Tasks worth removing first

When introducing AI-powered tools, it can be helpful to identify tasks that are ripe for automation or removal. These tasks are often those that are repetitive, time-consuming, and of low strategic value. By removing or automating these tasks, organisations can free up time and resources for more strategic initiatives, and can help drive real productivity gains. For example, a maintenance manager may use AsscherAi to automate the process of generating reports on equipment downtime, freeing up time to focus on more strategic initiatives such as process improvements or capital project planning.

Organisations can start by identifying tasks that are good candidates for automation or removal, and then prioritising those tasks based on their potential impact on productivity. This may involve working with employees to understand their workflows and identify areas where AI-powered tools can add the most value. By taking a thoughtful and strategic approach to the introduction of AI-powered tools, organisations can help ensure that they are using these tools to drive real productivity gains, rather than simply automating existing processes. You can learn more about how AsscherAi can help support this process.

Limitations and wrong choices

While AI-powered tools can be a powerful way to drive productivity gains in the metals and mining industry, they are not a silver bullet. There are certain situations where AI-powered tools may not be the best choice, such as when the data is of poor quality or when the tasks are highly complex or nuanced. In these situations, organisations may need to consider other approaches, such as process improvements or training and development programs. If you are considering introducing AI-powered tools to your organisation, but are unsure about the best approach, you can contact us to discuss your options in more detail.

The introduction of AI-powered tools is just one part of a broader strategy to drive productivity gains in the metals and mining industry. By taking a thoughtful and strategic approach to the introduction of these tools, and by considering the potential limitations and challenges, organisations can help ensure that they are using AI-powered tools to drive real business value. This may involve working with employees to understand their workflows and identify areas where AI-powered tools can add the most value, as well as providing training and support to help employees develop the skills they need to work effectively with these tools.