STADLER transforms knowledge work at a company with 230 years of history

STADLER, the rail vehicle manufacturer, shows how a large industrial company can put ChatGPT to practical, everyday use. If you work in operations, HR, marketing, or IT and wonder whether AI chatbots are just hype, this look at STADLER's experience will give you a realistic picture of what changed for their teams, where the time savings came from, and what lessons other companies can take away.

Why STADLER Turned to ChatGPT

STADLER is a Swiss-based manufacturer of trains, trams, and other rail vehicles, employing tens of thousands of people worldwide. Like many industrial firms, its teams spend a significant portion of their day on knowledge work: writing reports, answering emails, preparing presentations, translating documents for international customers, and digging through technical documentation.

Rather than treating generative AI as a distant future technology, STADLER decided to give employees direct access to ChatGPT and encourage them to experiment. The company rolled out access broadly, reaching around 650 employees who could use the tool in their daily routines. The idea was simple: let the people who actually do the work discover where AI helps most.

What Actually Changed in Daily Work

The most noticeable effect reported across the company was time. Tasks that used to eat up hours could often be finished in minutes. Typical examples include:

• Writing and editing. Drafting emails, summaries, and internal communications became faster. Employees used ChatGPT to produce a first draft, then refined it themselves rather than starting from a blank page.
• Translation and multilingual communication. With customers and colleagues across many countries, STADLER staff used the tool to bridge language gaps in everyday correspondence.
• Summarizing documents. Long reports and technical texts could be condensed into key points quickly, making it easier to prepare for meetings or brief colleagues.
• Brainstorming and problem-solving. Teams used the chatbot as a sparring partner for ideas, phrasing questions differently until they found a useful angle.

The savings add up. When hundreds of employees each reclaim even a small amount of time per day, the aggregate gain across 650 people becomes substantial in terms of hours returned to the company.

Making Adoption Work Across a Big Organization

Handing people a new tool is easy; getting them to actually use it well is harder. STADLER's experience highlights a few ingredients that made the rollout stick:

1. Broad access instead of a small pilot. Opening ChatGPT to a large group of employees meant use cases emerged from many departments, not just IT enthusiasts.
2. Encouraging experimentation. Staff were invited to try the tool on their own tasks, which produced creative applications leadership might never have prescribed from the top down.
3. Learning from each other. As employees found prompts and workflows that worked, tips spread organically, raising the overall skill level.

This bottom-up approach contrasts with rigid, centrally mandated AI programs. It also means the company didn't need a large dedicated team to drive adoption — curiosity and practical value did much of the work.

Lessons for Other Companies

STADLER's story offers takeaways for any organization considering a similar move:

• Start with access and trust. You learn more from wide, real-world usage than from a limited proof of concept.
• Expect writing, translation, and summarization to lead. These are consistently the first tasks where employees see clear value.
• Measure time saved, not perfection. AI drafts are not flawless, but a rough first draft plus human editing is often much faster than working alone.
• Set expectations about data care. Companies introducing AI tools should remind staff about what information is appropriate to share, since sensitive company or customer data deserves caution.

Who Should Try This Approach / Who Should Skip It

Try it if your organization has a large share of knowledge work — emails, reports, documentation, multilingual communication — and you can give a broad group of employees safe access to a tool like ChatGPT. STADLER's experience suggests the payoff comes quickly when people apply AI to tasks they already understand deeply.

Skip it if you expect AI to replace expertise rather than support it. The time savings STADLER employees experienced came from people who knew their jobs well and used ChatGPT as an assistant, not a substitute. Without that foundation and without basic attention to data security, results will likely disappoint.