Quick Start
- What you will learn: How to structure an AI rollout to accelerate productivity in a traditional business setting.
- What you need: An enterprise AI platform (like ChatGPT) and a clear change management plan.
A 230-year-old company is proving that artificial intelligence adoption is not restricted to agile tech startups. According to OpenAI, STADLER is actively using ChatGPT to fundamentally transform its knowledge work. The deployment spans 650 employees, resulting in notable time savings and accelerated productivity across the organization.
This development is significant because it highlights a clear shift in enterprise AI. Legacy businesses with deeply entrenched processes are successfully integrating generative AI to modernize their operations.
While the OpenAI report focuses on STADLER’s specific operational success, replicating this kind of organizational transformation requires a deliberate approach. Here is a practical guide to reshaping knowledge work in a traditional company.
Step 1: Audit existing knowledge workflows
Before rolling out an AI platform, identify exactly where employees spend the most time retrieving, summarizing, or processing internal information.
Why it matters: Generative AI delivers the highest return on investment when applied to specific administrative bottlenecks rather than vague, generalized tasks.
Best Practice: Survey department heads to isolate the most time-consuming, document-heavy processes in their daily routines.
Step 2: Implement a broad deployment
STADLER chose to roll out ChatGPT to 650 employees, integrating the technology across a wide swath of its workforce.
Why it matters: Widespread access prevents operational silos. It allows different departments to discover unique, localized use cases that leadership might not have anticipated.
Tip: Ensure you are using an enterprise-grade tier of your chosen AI tool to protect proprietary company data when deploying at scale.
Step 3: Establish clear productivity metrics
Measure the time saved on specific tasks before and after the AI integration.
Why it matters: You need concrete data to understand if the AI is genuinely accelerating productivity or simply shifting the workload to prompt engineering.
Warning: Do not measure success purely by adoption rates or the number of prompts sent. Focus strictly on the reduction in hours spent on standard knowledge tasks.
Practical Next Steps
If you are leading operations at a traditional organization, start by running a targeted pilot program in a single, document-heavy department. Document the time saved on routine tasks, establish strict guidelines for data privacy, and use those early metrics to build a comprehensive business case for a wider rollout. You can find more details on STADLER’s specific implementation at the original source.