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OpenAI Manufacturing Europe & UK

STADLER

Enterprise Knowledge Search

STADLER needed to reduce the time employees spent summarizing, translating, drafting, and turning raw knowledge into usable output while scaling modern knowledge work across a 650-person industrial business.

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125+ Custom GPTs created
2.5x faster time to first draft on average
30-40% time savings on common knowledge tasks
650 employees

Reported impact

Business outcome

STADLER created 125+ custom GPTs, achieved 30-40% time savings on common knowledge tasks, improved first-draft speed by 2.5x on average, and reached daily usage above 85% across its AI-enabled workforce.

  • Productivity Improvement
  • Scalability

Solution

STADLER rolled out ChatGPT as a company-wide productivity layer so employees could turn drafting, translation, summarization, and knowledge-reuse work into a faster, repeatable operating model. After evaluating alternatives, the company chose ChatGPT for output quality, speed, and immediate usability, then combined top-down direction with bottom-up experimentation so every employee working on a computer would use AI in daily work. Teams created more than 125 custom GPTs for recurring tasks and used them to standardize how knowledge becomes proposals, social content, and other first drafts. Rather than treat the model as a standalone writing tool, STADLER used it as a structured thought partner embedded into everyday workflows, allowing teams to compress half-day drafting cycles into minutes while preserving human review and decision-making.

Data flow

Employees bring raw notes, documents, and source material into ChatGPT, where custom GPTs turn them into structured drafts, summaries, translations, and reusable outputs. Those outputs flow back into marketing, internal communication, and other team workflows for human review and final publication.

Solution architecture

2 components · 2 layers
  1. Serving
    • ChatGPT Provides the shared conversational workspace where employees summarize, translate, draft, and structure knowledge work.
  2. Orchestration
    • Custom GPTs Package repeatable prompts and workflow logic for department-specific tasks across the company.

Architecture clues

  • Company principle: every employee working on a computer should use AI
  • Custom GPT library packages repeatable workflows
  • Rollout combined bottom-up experimentation with company-wide enablement

Evidence from the source

"125+ Custom GPTs created"
"ChatGPT isn't just a writing tool—it's a thinking partner that helps structure ideas and accelerate how we work."
"In many teams, people were spending too much time turning raw knowledge into usable output—summarizing, translating, drafting."
"We moved from needing half a day to get a decent first version to having a solid draft in 20 minutes"
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    STADLER customer story

    First-party
    https://openai.com/index/stadler/
    Source type
    Vendor-published customer story
    Published
    Mar 27, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Mar 27, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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