A readiness checklist for maintenance AI
Implementing AI in maintenance requires a readiness checklist to ensure connected data, agreed-upon definitions, and decided permissions, allowing a maintenance team to ask questions like what equipment is due for maintenance, and receive accurate and helpful insights to plan their work effectively.
A maintenance manager sits at her desk, staring at a dashboard that should be helping her team plan their day, but is instead showing a jumbled mess of incomplete data, a stark reminder that the new AI system is only as good as the information it's given.
Connected data is crucial
The AI system relies on a steady stream of data from various sources, including maintenance logs, equipment sensors, and employee reports, to provide accurate and helpful insights, but without this data, the system is essentially useless, unable to provide the maintenance team with the information they need to do their jobs effectively.
For example, a shift supervisor checks the maintenance log to see what work has been completed and what still needs to be done, but if the data from the sensors on the equipment is not connected to the log, the supervisor will not have a complete picture of the equipment's status, and may send a team to perform unnecessary maintenance, wasting time and resources.
This highlights the importance of having all relevant data connected and accessible to the AI system, to ensure that it can provide the maintenance team with the accurate and up-to-date information they need to plan and execute their work.
Definitions must be agreed upon
Before implementing an AI system, it's essential to agree on definitions for key terms and concepts, such as what constitutes a "maintenance request" or a "work order", to ensure that everyone is on the same page and that the system is configured correctly.
This may seem like a mundane task, but it's crucial to the success of the AI system, as different definitions can lead to confusion and errors, and may even cause the system to provide incorrect or misleading information, which can have serious consequences in a maintenance setting.
Most people get wrong the idea that these definitions can be worked out as they go, but this is wrong, as it can lead to a system that is not tailored to the specific needs of the maintenance team, and may not provide the insights and information they need to do their jobs effectively.
Permissions must be decided in advance
Deciding who has access to what data and functionality within the AI system is critical, as it ensures that sensitive information is only available to those who need it, and that the system is used in a way that is consistent with the organization's policies and procedures.
This is particularly important in a maintenance setting, where access to certain information or systems may be restricted for safety or security reasons, and where unauthorized access could have serious consequences, such as equipment damage or even injury to personnel.
A first question worth answering
When implementing an AI system, it's essential to identify a first question worth answering, one that the maintenance team is already waiting on, such as "what is the status of the current work orders" or "what equipment is due for maintenance", to provide a clear and tangible benefit to the team and to help build trust in the system.
For instance, the maintenance manager can use the AI system to track production schedules and plan maintenance accordingly, ensuring that equipment is properly maintained and that downtime is minimized.
This helps to demonstrate the value of the AI system and provides a foundation for further development and expansion, as the maintenance team becomes more comfortable with the system and begins to see its potential to improve their work.
Limitations of the AI system
The AI system is not a replacement for human judgment and expertise, and there will be times when it is the wrong choice, such as in situations where complex decision-making is required or where the data is incomplete or uncertain.
In these cases, it's essential to have a clear understanding of the system's limitations and to be aware of when it's not the best tool for the job, and to have alternative approaches and solutions in place, such as consulting with experts or using other decision-support tools.
If you're unsure about how to implement an AI system in your maintenance organization, or have questions about how to get started, you can contact us for more information and guidance.
Frequently asked questions
What are the key elements of a maintenance AI readiness checklist?
Connected data, agreed-upon definitions, and decided permissions are crucial for a successful AI implementation in maintenance.
Why is it important to agree on definitions for key terms and concepts in maintenance AI?
Agreed-upon definitions ensure everyone is on the same page, and the AI system is configured correctly to provide accurate and helpful insights.
How can a maintenance team demonstrate the value of an AI system to build trust and foundation for further development?
Identifying a first question worth answering, such as the status of current work orders, provides a clear and tangible benefit to the team and helps build trust in the system.
What are the limitations of an AI system in maintenance, and how can they be addressed?
AI systems are not a replacement for human judgment and expertise, and alternative approaches and solutions should be in place for complex decision-making or incomplete data situations.