How a 230-Year-Old Train Manufacturer Found a Use for ChatGPT

It's quite telling that one of the world's oldest engineering companies, founded back in the 19th century, is now using ChatGPT as a work tool for hundreds of its employees. STADLER, a Swiss manufacturer of rail vehicles with a history of over 230 years, has launched a large-scale implementation of AI tools into its daily operations and shared the results.

Not About Technology, but About Time

In a nutshell: STADLER didn't build a new AI platform or overhaul its business processes from scratch. The company simply gave 650 employees access to ChatGPT and observed where it genuinely assists with information-related tasks – working with documents, texts, data, and correspondence.

Knowledge work – preparing reports, analyzing documents, drafting emails, translation, and structuring data – takes up a significant portion of the workday in any large company. Simply put, it's everything that doesn't require physical labor but demands time and concentration. And it's precisely here, according to STADLER's results, that an AI assistant proves particularly useful.

What Has Changed in Practice

After implementing ChatGPT, employees began to save a significant amount of time on tasks that previously required considerable effort: finding specific information in internal documents, drafting complex emails, summarizing long materials, and preparing presentation drafts.

This doesn't mean ChatGPT does all the work for people. Rather, it handles the initial draft – what used to take an hour now takes fifteen minutes. The employee still reviews and refines the result, but the starting point is already there.

For a company with 650 participating employees, this acceleration adds up to thousands of saved work hours – and this isn't a metaphor, but a direct result reported by STADLER.

Why It Works Here

STADLER is not a startup or a tech company. It is a train manufacturer with an engineering-focused culture, complex documentation, and a multilingual environment. It's typically these kinds of companies that accumulate vast amounts of internal knowledge that is difficult to find and even harder to apply quickly.

An engineer needing to understand the requirements for a specific system previously spent time searching through documents and corresponding with colleagues. Now, part of this work can be accelerated – not because the AI knows the answer better, but because it helps get to the relevant fragment faster or formulate a query for the knowledge base.

In a multilingual environment (STADLER operates in dozens of countries), ChatGPT has also proven useful as a tool for translating and adapting texts – without losing meaning and without needing to involve a translator every time.

Not Just Ticking a Box

An important detail: STADLER did not approach the implementation as a mere PR project. The company systematically tracked where the tool actually reduced workloads and where it didn't catch on. This allowed them to focus on scenarios where the impact was tangible – and to avoid trying to shoehorn AI into places where it wasn't needed.

This approach, by the way, often proves more effective than large-scale transformation programs. When employees themselves find uses for the tool in their specific jobs, rather than receiving it as a mandatory part of a new process, engagement and real-world benefits turn out to be higher.

What This Says About the Industry as a Whole

The STADLER story is a good example of how AI assistants are no longer the exclusive domain of tech companies. Manufacturing enterprises, financial organizations, and logistics companies all work with large volumes of text-based information, and it is precisely there that ChatGPT and similar tools are finding their application.

This isn't about replacing employees. It's about reallocating effort: less time on routine information processing and more on decision-making and substantive work.

For a 230-year-old company, this is perhaps the essence of the change: not a revolution, but an evolution of work habits. Slow, but steady.