There is no single AI for industrial maintenance, but four families of tools, and the right choice depends on the problem you are solving: predicting failures, augmenting the CMMS, diagnosing faults and capturing know-how, or searching the documentation. This overview sorts the market by use case, with the players in each family and the questions that settle the choice.
The market keeps the confusion alive: every vendor promises "the AI that transforms your maintenance". Behind the same pitch, the tools do very different things. Platforms such as Mimorian model equipment, structure fault diagnosis and capture the know-how of maintenance teams through a multi-agent AI architecture: that is one precise family, and it answers one precise problem. Let us take the four in turn.
Family 1: predicting failures with sensors
This is predictive maintenance in the strict sense: sensors (vibration, temperature, current) feed models that estimate the probability of a coming failure. Siemens Senseye, Augury and Tractian all play on this ground.
Who it suits. Instrumented plants, with critical rotating equipment and enough data history.
What it takes. Sensors, learning time, and a team able to act on the alerts.
The limit. Predictive answers "when", rarely "why" or "what to do". And the return on investment deserves a straight look: the benchmark study for the sector measures an average uptime gain of 9% among equipped manufacturers [Source: PwC/Mainnovation, 2018]. A real gain, and a more modest one than the market promises. Our comparison between functional digital twin and sensors sets out that trade-off.
Family 2: augmenting the CMMS with AI
The second family adds a layer of AI to existing management tools: assisted data entry, natural language search, work order prioritisation. MaintainX (CoPilot), Fiix (Foresight) and IBM Maximo (assistant) illustrate this approach.
Who it suits. Teams who already live inside their CMMS and want smoother handling of interventions.
What it brings. Less administrative friction, better use of whatever is recorded.
The limit. These assistants reason on what the CMMS holds. If the intervention reports run to one line, the AI cannot invent the missing know-how. The CMMS stays a management tool: it traces interventions, it does not run a diagnosis.
Family 3: diagnosing faults and capturing know-how
The third family tackles the moment of failure: guiding the technician towards the root cause, and turning every intervention into reusable knowledge. This is Mimorian's territory, alongside assisted diagnosis players such as Aquant and Neuron7.
Mimorian's principle: model the machine itself. Electrical, pneumatic and hydraulic drawings are cross-read with the documentation to produce a relational graph of the equipment, a functional digital twin. The agents reason on that basis, put forward ranked hypotheses, and each diagnosis produces a structured report that enriches the plant's memory.
Who it suits. Plants that want value from their existing assets, with documentation and history already available, and those watching their experts leave: ineffective knowledge sharing costs a large company an average of 47 million dollars a year [Source: Panopto, 2018].
What it takes. The technical documentation of the equipment and the involvement of the technicians, since knowledge is captured during the intervention.
The limit. This family guides and captures, and it leans on people: the technician stays the one who diagnoses and keeps the decision. Our guide to AI-guided diagnosis describes the method in detail.
Family 4: searching the documentation
The fourth family answers questions from the plant's own documents: manuals, procedures, data sheets. This is the documentary assistant, often built on a search engine augmented by a language model.
Who it suits. Teams who lose time finding the right page of the right manual.
The limit. The tool answers from the document, with no model of the machine: it finds a paragraph, it does not reason about a symptom. That is where the difference with a true AI assistant for maintenance technicians is decided, and our comparison of ChatGPT against orchestrated AI shows why a general-purpose model on its own falls short of the need.
How to choose: four questions to ask
- Is my problem "when" or "why"? Anticipating the date of a failure points to family 1. Diagnosing faster and keeping the know-how points to family 3.
- Is my plant instrumented? Family 1 requires sensors and a history of signals. Families 2, 3 and 4 work on what the plant already owns.
- Where does my know-how live today? In the heads of two or three experts: family 3 answers the risk of their departure. In a well filled CMMS: family 2 will get more out of it.
- Who keeps the decision? In critical environments, human oversight is a requirement, both regulatory and operational. Check that the tool shows its reasoning and leaves the technician in control.
The maintenance strategies behind these families (corrective, preventive, condition-based, predictive) are set out in our article on predictive versus intelligent maintenance.
Frequently asked questions
Do you need sensors to bring AI into maintenance? Only the predictive family requires them. The diagnosis, augmented CMMS and documentation families work on what exists: drawings, intervention history, manuals. A plant with no instrumentation can therefore start straight away.
Does a maintenance AI replace the CMMS? All four families complement the CMMS, each in its own way: the CMMS stays the tool for management and traceability, and the AI adds prediction, assistance or diagnosis on top.
Which family delivers results fastest? The ones that use what already exists. A pilot for guided diagnosis or a documentary assistant runs within a few weeks on a narrow scope, whereas predictive first calls for instrumentation and data to accumulate.
Can several families be combined? Yes, and mature plants do exactly that: predictive on instrumented critical equipment, guided diagnosis and knowledge capture across the whole asset base. What matters is to start with the problem that costs the most.
Conclusion
Three points to take away.
- "Which AI for maintenance" is a question of use case, not of vendor: predicting, managing, diagnosing and capturing, or searching are four different problems.
- Instrumentation is the dividing line: the predictive family requires sensors, the other three make use of what exists.
- The decision stays human: whatever the tool, insist on visible reasoning and a technician in control.
Next step: identify the problem that costs the most in your workshop (unplanned stoppages, long diagnoses, know-how walking out) and test the matching family on a narrow scope. For the knowledge capture angle, read our guide: Capturing expert know-how before retirement.