Three weeks ago I was standing in a production hall just before seven in the morning, somewhere between the high-bay warehouse and the filling line. Shift handover. A maintenance technician, more than twenty years with the company, was picking up a fault on a packaging machine that had already gone down twice during the night. He read the entry in the system, paused for a second, and said something I have heard in one form or another at almost every plant I visit: “It says the machine stopped. It doesn’t say why.”
What followed is what always follows in those moments. He walked over to the shelf, pulled a binder, flipped through it, put it back. Then he called a colleague who had worked late shift and was, at that hour, asleep. In the end he knew what to do. Not because of the documentation, but because someone else still had it in their head.
This is exactly where the conversation about AI in maintenance starts. And it is exactly where it pays to be precise about what a system is actually meant to do.
In short: AI in maintenance is most useful when it helps technicians retrieve asset-specific information, document work consistently, capture practical knowledge, and analyze reliable maintenance or condition data. It should support qualified maintenance decisions, not replace them.
What AI in maintenance is really about
AI in maintenance helps companies make technical information accessible, turn field work into usable reports, and analyze machine data. Which of these comes first depends on how a system is designed and on what data is actually available. Support for documentation and analysis of condition data are not opposites. They are two ways of looking at the same job: keeping assets running reliably.
For maintenance teams, the value rarely starts with a technology. It starts with a specific question. Where is the information I need? What was written down the last time this happened? And how do I record my own work so the next person can do something with it?
Which tasks can AI support?
One area is making sense of technical text. Manuals, fault reports, and maintenance records hold knowledge that matters for later work, except it sits scattered across PDFs, legacy systems, and free text fields. Fraunhofer Austria’s research on AI for maintenance text documentation describes a project aimed at making entries from industrial maintenance documentation more usable with the help of AI.
For the reporting foundation behind that data, see our guide to digitizing maintenance documentation and what belongs in a good report.
A second area is capturing experience, which is precisely what came out of that colleague’s head on the phone and existed nowhere else. Voice-based assistance systems can record explanations of this kind and give them structure. Fraunhofer IFF’s KIMO approach demonstrates this kind of voice-based knowledge capture: technicians talk through their work in dialogue, while the system structures steps, explanations, typical fault patterns, warnings, and experience. That is one concrete example, not a statement about what every AI product on the market does.
The organizational side matters just as much as the capture technology; our article on knowledge transfer in maintenance looks at that problem in more detail.
The interesting part is less the technology than the timing. Documentation gets created where the work happens, instead of two hours later at a desk, when half the detail has already faded.
How do knowledge work and predictive maintenance relate?
Both run under the same headline, but they start at different points, and in practice they tend to reinforce each other.
One perspective is about information: finding it, understanding it, capturing it. The other is about the condition of a machine and the failures that might be coming. That means looking at operating data for patterns that indicate wear. Fraunhofer IIS describes this use of AI and machine learning for predictive maintenance, including the analysis of operating data for patterns that can indicate wear and possible failures.
The intersection is where it gets interesting. A prediction is only ever as good as the history behind it, and a substantial part of that history is created by the maintenance team itself: in fault descriptions, root causes, and the work that was actually carried out. Where those entries read “machine broken, fixed it,” there is nothing for an analysis to stand on. Where they are captured with structure, patterns start to become visible across assets and across shifts, whether that is a fault mode that keeps coming back or the question of whether a maintenance interval still matches real load.
So the practical question is less either / or and more: where does it make sense to start, and what builds on top of that? For many teams, a reliable data foundation is the first step and, at the same time, the prerequisite for whatever predictive analysis comes later.
Does the existing maintenance software have to be replaced?
No. Replacement is not a general precondition, even if that is often the first worry in the room.
A CMMS manages work orders, asset information, and maintenance history. Modern platforms can also integrate AI, machine learning, ERP systems, and operational technology. IBM’s current CMMS overview describes these capabilities. The sensible move is to look first at what the existing system actually covers, and where an additional solution delivers something concrete on top.
In many plants the gap is not the system itself but the route into it. The data lives in the ERP or the CMMS, but nobody with oil on their hands is typing it into a desktop machine at 6:40 in the morning.
What does a sensible starting point look like?
A clearly bounded test case works best, for example finding technical information for one selected group of assets. It should be settled up front which documents may be used and what a good result actually looks like.
Three questions are usually enough to judge it:
- Is the right information found with less effort?
- Does the answer match the specific asset and the current revision of the document?
- Does the effort for checking and correcting stay reasonable?
One thing to keep in mind throughout: AI output is support, not a verdict. A fluent answer is not evidence that it is technically correct. Decisions about work on machines stay with the qualified people responsible for them. No model changes that.
Frequently asked questions
Does every AI application need additional sensors?
No. Working with existing documents and maintenance history does not generally require additional machine sensors. Condition forecasting does need suitable condition or operating data, but that data does not have to come from new sensors. In many cases it already exists in the control system, the ERP, or the CMMS, and it can be analyzed together with well-captured maintenance history.
Can small maintenance teams use AI?
Team size is not the deciding factor. What matters more is a recurring need for information, suitable data, and a benefit that justifies the effort of introducing and maintaining the system. If anything, small teams feel the absence of the one colleague who knows everything even more sharply.
What is a good first AI use case in maintenance?
Start with one bounded problem where success can be checked: for example, finding the correct technical information for one asset group or turning maintenance notes into structured reports. Define the allowed data sources, the required level of accuracy, and who reviews the output before expanding the scope.
Conclusion
AI in maintenance should be judged against a specific task, not against a promise. Starting with one manageable use case makes it far easier to assess the value, the data gaps, and the limits before more processes get pulled in.
That sentence from the production hall captures the lever pretty well: “It says the machine stopped. It doesn’t say why.” The point is not more data. It is usable knowledge at the moment someone needs it.
That is where Pairio comes in. The product is designed to capture maintenance work by voice, photo, or video, search technical documentation in dialogue, and analyze maintenance history for patterns and optimization potential. Anyone wanting to find out whether that holds up in their own plant is best served by starting with a single use case, on the group of assets where looking for information costs the most time today.