Generative AI in Maintenance: what is it, how does it work and why adopt it in Engeman®?

What is Generative AI (GenAI)?

First of all, it is worth understanding what differentiates the new generation of AI from the others. Traditional AI is meant to analyze data, find patterns, and generate reports. It helps us understand what happened and, often, what may happen. But it is based on pre-existing answers, on knowledge that is already available.

Generative AI goes further: It is capable of creating new content based on an immense amount of data: texts, images, audio, and codes. It learns from textual, visual, and sound data to generate content such as reports, maintenance plans, operating procedures, and query scripts, among others.

A famous example is ChatGPT, which answers questions clearly, even without having access to a real-time database. In the universe of maintenance, this technology opens possibilities such as assistants who understand standards, manuals, and histories to assist technical teams on a daily basis.

How does Generative AI work?

One way to better exemplify this is to think of Generative AI as an employee who knows the most diverse maintenance manuals, procedures, and histories. When asked about a subject, for example, “how to perform predictive maintenance on electric motors”, he will not look for a ready-made answer. You will certainly build a new answer, combining everything you know about the subject, in a coherent and relevant way. This “digital employee” can help by writing a technical email, summarizing an extensive report, or even suggesting a checklist for inspecting equipment, but all this in an immediate and automated way.

It is from this that the AI begins to predict what would be the most coherent answer to a given question or request. And the better trained and more contextualized you are, the better your answers will be.

Engeman uses advanced models such as GPT-4o mini, an OpenAI Generative AI that can handle extremely long texts (up to 128 thousand tokens, equivalent to a complete technical manual), interpret images, and generate content with great accuracy, all at a reduced cost and with high performance.

What is the user’s role in interacting with Generative AI?

GenAI (Generative AI) comes to add and not replace the technical knowledge of maintenance teams. What defines the quality of the AI’s response is, primarily, the way the question is asked. The clearer and more objective the question, the more relevant the answer will be. And this is only possible with the experience of those who know the process, equipment, and context in which they are operating.

Therefore, more than a ready-made solution, AI should be seen as a working partner: it supports, suggests, and streamlines. But the one who decides, interprets, and puts it into practice is always the professional. In the end, it is this combination of technology and human knowledge that makes the difference in the maintenance routine.

How is Artificial Intelligence used in maintenance?

In practice, the use of Generative AI in Engeman® maintenance software will bring significant gains in time, assertiveness, and productivity in the context of maintenance and asset management. Let’s explore some examples:

1. Quick technical responses:

Have questions about a specific flaw? Need to understand why engine vibration suddenly increased? Ask the AI the question and it will consult everything that has already been recorded about this type of failure and provide an informed explanation. AI is based on its training in maintenance manuals and literature, it provides a clear explanation and even suggestions for actions. This is not a substitute for the human expert, but serves as an instant support for the field team, speeding up preliminary diagnoses and expanding knowledge accessible to all levels of the team. It’s like having an experienced engineer always available to help you.

2. Automatic generation of reports and documents:

Do you know that an inspection report that usually takes a whole morning to write? Within seconds, AI will return structured, coherent, and well-written text, covering the introduction, procedures done, results, and recommendations. In Engeman, you can even indicate a ready-made model, if it exists. Sure, the professional will proofread and adjust details, but it’s much easier to edit a base text than to start from scratch. The time that would be spent writing can be redirected to more strategic activities.

3. Support in creating maintenance plans and procedures:

Developing a maintenance plan from scratch for a new asset can be challenging. Generative AI can contribute by suggesting checklists and plans based on best practices. For example: “Generate a monthly preventive maintenance plan for a centrifugal water pump.” The model, knowing generic industry standards, can list: check vibration, check sealing, inspect coupling, analyze bearing temperature, etc., with descriptions of how to perform each item. It’s like having instant access to an intelligent template that considers the type of equipment.

Again, it will be up to the expert to adjust the list to the specific reality (operating conditions, criticality, etc.), but starting from an outline is a time saver. In addition, for training new employees, AI can generate teaching materials or even simulate service dialogues. For example, a young technician can practice talking to AI as if it were a “piece of equipment”, to train a problem analysis approach.

4. Support for the use of maintenance systems and software

We don’t always remember how to use all the features of the software. Now imagine asking the system: “How do I register a new preventive OS?” and receiving a step-by-step explanation. This type of support is already available with Engeman®’s virtual assistant.

Tools such as the Engeman® software itself have numerous features and the user will not always remember everything or know how to get the most out of it. AI can act as an interactive tutor. Unlike fixed manuals, this help is dynamic and contextual and can even use information from the user’s current screen to guide them. The result is continuous training and on-demand support, reducing time searching for documentation and technical support calls.

5. SQL or Scripting Suggestion

If you need to query specific information in the database but don’t master SQL, AI can generate the code for you based on a simple request, such as: “I want a list of equipment with more than 3 failures in the last 6 months.” This democratizes access to information since even those who are not SQL experts will be able to obtain the correct query with the help of the model.

The list above brings just a few examples of how Generative Artificial Intelligence can be applied in maintenance management. Increasingly, technology companies are discovering new assets. It is also worth mentioning that the actual implementation of these integrations with AI requires access to correct data, in order to ensure security and confidentiality and to shape the user experience so that it is intuitive.

What are the main advantages of Generative AI in Maintenance?

The integration of Generative AI into Engeman software is being designed to bring real gains to maintenance teams. See the main gains for the team:

1. Increased productivity

With it, tasks such as technical reports and maintenance plans are produced quickly, freeing up the professional to focus on activities with greater impact.

2. 24/7 assistance for the entire team

Even outside of business hours, AI works as an always-available assistant. Whether to consult a procedure at 3 am or ask questions about equipment, support is always accessible directly in the Engeman® software environment.

3. Automation of cognitive tasks

 AI helps in reading and analyzing histories, identifies recurring failure patterns, suggests preventive actions, and even fills out technical forms based on previous data. It transforms the volume of information into more agile decisions.

4. Personalization and continuous learning

Generative models can be adjusted to the specific context of the company. By being fed company-specific data, AI starts to “speak the language” of your team, understanding internal technical terms and processes unique to the plant. The more it is used, the more accurate and relevant it becomes—like a learner by doing.

What are the main challenges of Generative AI in maintenance?

Despite the great potential, Generative AI also has limitations that require constant human attention and supervision. Some key challenges to consider:

• Hallucinations and inaccurate responses:

One of the main precautions when using Generative Artificial Intelligence in maintenance is the risk of so-called “hallucinations”, responses that seem reliable, but are wrong or have no basis in real facts. AI can suggest non-existent procedures, mistaken specifications, or incorrect diagnoses with complete “confidence.” This is because the model tries to predict the most likely answer based on the data it has already seen, and not necessarily on technical truth. In critical contexts, such as maintenance, following wrong information can lead to rework, failures, or operational risks. Therefore, validation by a human professional is mandatory. AI is an important ally, but the one who makes the final decision is the human expert.

• The technological dependence on AI

Artificial Intelligence can speed up processes and make day-to-day maintenance easier, but delegating too many tasks to AI can weaken essential human skills, such as technical writing, critical analysis, and decision-making. For example, if all report writing is automated, new technicians may fail to learn how to write clearly.

In addition, in case of failures or unavailability of the AI, the team needs to be prepared to continue operating normally, without compromising on the quality or safety of the activities.

Therefore, it is essential to maintain the balance between automation and continuous professional training. Technology should be an ally, not a substitute for human competence.

• Lack of critical sense and context:

Despite its efficiency, Generative Artificial Intelligence does not have a critical sense or understand the context in depth. It can generate technically correct answers, but without considering operational, ethical, or situational nuances.

In addition, it can reproduce biases in the data with which it was trained or suggest inappropriate actions if not well-guided. Therefore, all AI-generated content must be reviewed by an expert before being applied.

Think of AI as a brilliant intern: it’s productive and a quick learner, but it still needs constant human supervision to ensure safety and assertiveness.

• Knowledge limitations and importance of secure integration

Models such as GPT-4o mini have a learning time limit, for example, until 2023. Without integration with up-to-date company data, AI can generate generic responses, disregarding recent or plant-specific information.

To avoid this, Engeman personalizes and integrates AI with technical and internal data, ensuring that the virtual assistant acts safely, in context, and adheres to operational reality.

All this with full compliance with LGPD and information security policies, protecting customer data, and ensuring that AI is a reliable and effective resource.

Becoming aware of these challenges helps to use generative AI responsibly and effectively. With proper care, serious review policies, double checks on critical information, and staff training to live with the new technology, it is possible to reap the rewards of this innovation while minimizing risks.

Other practical applications of generative AI at Engeman®

Generative Artificial Intelligence has enormous potential to transform the routine of maintenance teams, and we are just beginning to explore all the possibilities. In addition to the examples already presented, other practical applications can further expand the benefits in everyday life:

• Interpretation of technical images: with the ability to understand images, AI will be able to analyze photos of components, screenshots with error messages, or even equipment diagrams. This makes diagnosis and support easier, especially in situations where the image speaks more than a thousand words.
• Intelligent analysis of histories: by cross-referencing data from work orders, failures, and maintenance performed, AI can identify recurrence patterns that would otherwise go unnoticed. This supports more strategic decisions and can help prevent problems before they happen.
• Creation of content for training: imagine training new technicians with personalized materials, generated by AI according to the profile of the team or the plant’s equipment. AI can simulate scenarios, create checklists, answer questions like a “digital trainer,” and speed up the learning process.
• Predictive diagnostic support: In the future, by integrating sensor data and operational histories, AI will be able to suggest actions based on trends and probabilities of failure. This represents an important step towards increasingly predictive and intelligent maintenance.

With each new application, we reinforce the proposal that AI does not replace the team, it offers support, agility, and more knowledge so that each professional can make faster, safer, and more informed decisions. At Engeman®, the focus is always on evolving together with the customer, making technology a practical and accessible ally in the maintenance routine.

Conclusion:

Generative Artificial Intelligence is no longer a futuristic promise to become a concrete ally in routine maintenance. With it, previously manual and repetitive tasks gain agility, reports are produced faster, decisions are more informed, and technical knowledge becomes more accessible to everyone on the team.

We are facing a paradigm shift, similar to the impact that IoT sensors, PLCs, and Industry 4.0 have caused. Now, AI extends this advancement, enabling more intelligence, personalization, and efficiency in asset management. And those who adopt this technology now will have a real competitive advantage.

But like any powerful tool, AI requires preparation, responsibility, and discernment. As discussed throughout this article, it is essential to ensure human supervision, review the generated content, and integrate data safely and strategically. The quality of the answer still depends on the experience of the professional who asks the question and this reinforces the irreplaceable value of the team and its technical knowledge.

Engeman is investing in models, customizing AI with technical vocabulary, industry data, and internal practices, and ensuring full compliance with the Brazilian General Data Protection Law (LGPD). All this is to deliver a robust, reliable, and really useful solution for those who live maintenance daily.

Maintaining the future is collaborative: between people, machines, and artificial intelligence. And it has already begun. Want to know what Engeman® AI looks like? Schedule a demo with our team!