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AI Engineering
OpenAI Blog·Mar 27·5 min read
STADLER reshapes knowledge work at a 230-year-old company
TL;DR
STADLER, a 230-year-old company, faced productivity bottlenecks in knowledge work—tasks requiring information synthesis and decision-making across its 650-person workforce. The organization integrated ChatGPT, an AI language model, into daily workflows to automate routine information tasks and augment employee capabilities.
✦ Why It Matters
Engineers can learn how to integrate large language models into enterprise workflows to reduce knowledge work overhead and measure productivity gains.
Key Takeaways
- Integrate large language models into existing workflows rather than building separate tools.
- Measure time savings and productivity metrics to quantify AI augmentation impact.
- Focus AI deployment on routine information tasks to free humans for complex decisions.
- Scale AI adoption across entire workforce to maximize organizational productivity gains.
Full Summary
STADLER, an established organization with 650 employees, identified inefficiencies in knowledge work—labor-intensive tasks involving document analysis, information retrieval, and synthesis. The company deployed ChatGPT, a large language model trained to generate human-like text responses, as a productivity tool integrated into employee workflows.
The implementation focused on automating routine information-handling tasks while preserving human judgment for complex decisions. By embedding ChatGPT into standard work processes, STADLER reduced time spent on repetitive knowledge tasks and freed employees to focus on higher-value activities.
Measurable improvements in productivity and time savings were observed across the workforce, demonstrating that AI language models can effectively augment human workers in knowledge-intensive roles without requiring extensive retraining.