STADLER integrates AI and accelerates knowledge work | Keryc
Imagine a 230-year-old company that builds waste sorting plants and, without fanfare, turns artificial intelligence into a normal part of daily work. How did they do it? STADLER integrated ChatGPT as a productivity layer that is changing how knowledge is created inside the company.
STADLER integrates AI and accelerates knowledge work
STADLER is a family-owned company with more than 230 years and about 650 employees, specialized in automated recycling plants. Since 2023, under the leadership of co-CEO Julia Stadler, they adopted a simple principle: anyone who works in front of a computer should use AI to improve speed, quality, and collaboration.
The results speak clearly:
125+ custom GPTs created.
30-40% time savings on common knowledge tasks.
2.5x faster to get the first draft on average (up to 6x in high-volume cases).
>85% active daily use among employees.
How they implemented it in day-to-day work
They chose ChatGPT for output quality, speed, and the ability to start generating value from day one. Implementation combined bottom-up experimentation with top-down support and guardrails: teams were allowed to explore concrete cases while management facilitated access and training.
Areas and concrete uses:
Engineering and data: analysis, code support, and performance evaluation.
Projects and management: custom GPTs to structure processes and improve documentation.
Marketing: translating technical knowledge into clear communications for global audiences.
All areas: writing, summaries, research, and structured thinking.
ChatGPT is not just a tool for writing; it became a thinking partner that helps structure ideas and speed up work.
A concrete example: what used to take half a day to get a decent first draft now appears in 20 minutes, and in high-volume flows like social media the acceleration can reach 6x.
Impact on culture and workflow
The numbers are only the tip of the iceberg. The real change is in how people work: complex tasks start with less friction, the focus shifts from creating from scratch to refining decisions, and outputs are clearer and more consistent.
Practical consequences they reported:
Faster decisions thanks to access to structured insights.
Higher quality and consistency in documents and communications.
Less repetitive work; more time for high-value tasks.
Voluntary recurring use: employees return to the tool several times a day.
What’s next? From assistant to execution layer
STADLER already envisions the next step: moving from assistants that help to agents that execute. The idea is to integrate systems that collect information, generate outputs validated against standards, and route work for approval. In other words, AI moves from support to being part of the operational flow.
For a company with more than two centuries of history, the change doesn’t erase its legacy; it accelerates it. If a century-old industrial company can make AI an everyday strategic tool, what could you do in your team with a similar approach?
