What AI actually costs in maintenance
AsscherAi's maintenance costs are often underestimated due to overlooked integration and data work expenses, which can be mitigated by attributing spend to specific processes and regularly testing the system's accuracy to prevent incorrect answers and potential equipment failures.
In theory, an AI system like AsscherAi should be a straightforward addition to an organisation's maintenance workflow, answering questions about the organisation's own data in real time. However, in practice, the costs of maintaining such a system can accumulate in unexpected ways, leading to a significant impact on the bottom line.
Integration and data work as the dominant line
The majority of the cost of maintaining an AI system like AsscherAi comes from integration and data work, rather than the cost of the AI model itself. This is because the AI model is only as good as the data it is trained on, and ensuring that the data is accurate, complete, and well-integrated with the rest of the organisation's systems is a time-consuming and labour-intensive process. A maintenance manager, for example, may need to work with a data analyst to ensure that the maintenance log is properly formatted and up-to-date, in order to get the most out of the AI system.
This can be a significant challenge, particularly in organisations where data is spread across multiple systems and departments. However, it is essential to get this right, as poor data quality can lead to inaccurate or misleading results from the AI system, which can have serious consequences in a maintenance context.
Run cost against query volume
Another key factor in determining the cost of maintaining an AI system like AsscherAi is the volume of queries that the system needs to handle. The more queries the system needs to answer, the more resources it will require, and the higher the cost will be. A shift supervisor, for example, may use the AI system to check the maintenance log and answer questions about upcoming maintenance tasks, which can generate a high volume of queries and drive up costs.
However, it is also important to consider the benefits of using an AI system like AsscherAi, particularly in terms of the time and resources it can save. By automating routine queries and providing quick and accurate answers, the AI system can free up maintenance staff to focus on more complex and high-value tasks, which can lead to significant productivity gains and cost savings.
The cost of a wrong answer nobody caught
One of the most significant risks associated with using an AI system like AsscherAi is the potential for the system to provide incorrect or misleading answers, which can have serious consequences in a maintenance context. If a maintenance worker, for example, relies on an incorrect answer from the AI system to make a decision about a maintenance task, it can lead to equipment failure, downtime, and even safety risks.
It is therefore essential to have robust processes in place to verify the accuracy of the AI system's answers and to catch any errors or inconsistencies. This can include regular testing and validation of the AI system, as well as training for maintenance staff on how to use the system effectively and safely. For more information on how to implement an AI system like AsscherAi in a production environment, see our production guide.
Attributing spend to a process rather than a model
When it comes to attributing spend to an AI system like AsscherAi, it is common for organisations to focus on the cost of the AI model itself, rather than the broader process of maintaining and using the system. However, this can be misleading, as the majority of the cost is likely to come from integration and data work, as well as the resources required to support the system and ensure its accuracy.
A better approach is to attribute spend to the specific process or function that the AI system is supporting, such as maintenance planning or equipment monitoring. This can help to provide a more accurate picture of the costs and benefits of using the AI system, and can inform decisions about how to optimise and improve the system over time.
When AI is the wrong choice
While an AI system like AsscherAi can be a powerful tool for maintaining and optimising equipment and processes, there are certainly situations where it is not the best choice. For example, in situations where the data is poor quality or incomplete, or where the queries are highly complex or nuanced, an AI system may not be able to provide accurate or reliable answers.
In these situations, it may be better to rely on human expertise and judgement, or to use alternative technologies such as
- rule-based systems
- expert systems
Frequently asked questions
What are the main costs associated with maintaining an AI system like AsscherAi?
Integration and data work costs dominate the expenses, rather than the AI model itself, due to the need for accurate and well-integrated data.
How does query volume impact the cost of maintaining an AI system?
Higher query volumes require more resources, driving up costs, but can also lead to productivity gains and cost savings by automating routine queries.
What are the risks of incorrect answers from an AI system like AsscherAi?
Incorrect answers can lead to equipment failure, downtime, and safety risks, highlighting the need for robust verification processes and regular testing.
How should organisations attribute spend to an AI system like AsscherAi?
Attributing spend to specific processes or functions, rather than the AI model itself, provides a more accurate picture of costs and benefits, informing decisions on optimisation and improvement.