How to Reduce Downtime with AI

Unplanned machine downtime has remained one of the biggest and most costly challenges in industrial plants for years. Every minute the production line stands still means not only lost output, but also rising costs, schedule disruptions, and pressure on the maintenance team. Over the course of a year, these seemingly short stoppages can add up to figures in the hundreds of thousands of dollars.

Interestingly, the problem rarely stems from a lack of data. Most plants have extensive technical documentation, repair histories, procedures, and, most valuable of all, the experience of employees who „remember how this used to be fixed.” The real difficulty is that this knowledge is scattered and unavailable at exactly the moment it’s needed most, when the machine is already down.

We dedicated our webinar „How to Reduce Downtime and Save $400K Annually with AI” to this topic, hosted by Łukasz Borzęcki, CEO of VM.PL, and Rafał Peno, Senior Customer Success Manager at VM.PL. During the session, we showed live how artificial intelligence shortens the time it takes to diagnose and resolve failures.

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Watch the Webinar Recording

If you want to learn the details and see the tool in action live, we’ve made the full webinar recording available. In it, you’ll find a demonstration of troubleshooting a failure, a discussion of data security issues, and the entire Q&A session.

Where Long Downtimes Come From

The scenario that drew the most nods during the webinar is all too familiar. The machine stops, and with it the entire line. The operator looks at the error message and starts searching for documentation. They flip through binders, open one PDF after another, and log into several different systems. The experienced coworker who „always knows what to do” happens to have the day off. Time passes, and production is still at a standstill.

In this scenario, the core of the problem isn’t a lack of knowledge. The knowledge exists, but it is fragmented and hidden in different places. The bottleneck becomes the time needed to reach the right information. It is this stage, not the repair itself, that generates the longest and most costly downtime.

UR CoPilot – All Your Knowledge in One Place

During the webinar, we presented how the AI assistant for maintenance works. The tool combines technical documentation, failure history, and operational knowledge into a single, coherent source. Instead of searching through multiple systems, an employee asks a question in natural language and receives a concrete answer along with step-by-step instructions.

The key features shown live:

search in natural language, regardless of the language in which the technical documentation was written,
guiding the employee through the successive steps of diagnosis and repair,
generating links that lead directly to the relevant sections of the documentation,
working on any device, from the office computer to a phone held on the shop floor,
integration of data from multiple sources: PDFs, Excel files, scanned documents, emails, WhatsApp messages, and CMMS, ERP, WMS, and OPC UA systems.

To show that this isn’t just theory, we ran several live demonstrations. Among them were resolving a robot arm failure, following safety zone procedures, and providing guidance on replacing parts. In each case, the assistant guided the user through the successive steps in a way that was clear to both the operator and the experienced technician.

Data Security and Compliance

Każdego dnia w działach operacyjnych dużych organizacji zapadają tysiące powtarzalnych decyzji: akceptacja płatności, wybór metody rozliczenia, ustalenie trasy w logistyce, klasyfikacja zgłoszeń klientów. To działania, które pojedynczo wydają się proste, ale w skali całej firmy mają ogromne znaczenie kosztowe.
McKinsey szacuje, że firmy, które wprowadziły machine learning (ML) do procesów operacyjnych, zmniejszyły koszty nawet o 30–40%, a czas podejmowania decyzji skrócił się średnio o 25% (źródło). To pokazuje, że mówimy nie tylko o oszczędnościach, ale też o realnej przewadze konkurencyjnej.

Nowa era w zarządzaniu decyzjami

For production plants, the confidentiality of technical and operational data is critical, which is why we devoted a separate, in-depth section of the webinar to it. The key principle is that the knowledge base remains on the client’s infrastructure. The data doesn’t leave the company and isn’t used to train external models.

Additionally, the model draws only on those sections of documentation that are necessary to provide a specific answer, which further limits the exposure of sensitive information. The entire solution is GDPR and AI Act compliant and operates based on ISO 9001 and 27001 standards. This is a topic that generated significant interest among participants and came up again during the Q&A session.

What Results You Can Achieve

The most common question at meetings like these is: how much will I gain? During the webinar, we referred to a specific example. In one project, we managed to reduce downtime by 70%, cutting it from over an hour to just a few minutes.

The benefits we discussed in more detail are primarily:

shorter response and repair time, meaning a lower MTTR,
fewer downtimes and a reduction in their total duration,
relieving the maintenance team, which can focus on preventive actions instead of putting out fires,
a higher OEE,
real savings visible within just a few weeks of implementation.

It’s worth emphasizing that the savings don’t come from one big improvement, but from consistently shortening dozens of individual events that, over the course of a year, add up to substantial sums.

What Implementation Looks Like in Practice

Participants often worry that implementing an AI solution is a long and complicated project. During the webinar, we showed that you can approach it gradually. It’s best to start with a single line or a selected set of machines, rather than covering the entire plant right away.

A typical implementation path involves several steps. First, you load the existing documentation, including archived documents. Next, you configure user roles, separately for operators and separately for the maintenance team, so that everyone has access to the information relevant to their work. The next stage is fine-tuning the model on the client’s data, and after launch, gradually refining the answers based on user feedback.

It’s important to keep one principle in mind: the effectiveness of the tool depends directly on the completeness of the data entered. The more complete the documentation, including old manuals, scanned documents, and the history of reported issues, the more accurate and reliable the answers.

What’s Next

The webinar confirmed the idea behind the entire solution. Reducing downtime doesn’t require gathering more and more data, but rather making smart use of the data the plant already has. AI doesn’t replace employees’ experience. It makes that experience available to the whole team in just a few seconds.
If you’d like to see how the solution handles your plant’s documentation, we invite you to get in touch.

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How to Reduce Downtime and Save Up to $400K Annually with AI – Webinar Recap