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OpenAI Automotive Europe & UK, North America

AutoScout24 Group

Application Refactoring · IT Operations Automation

AutoScout24 Group needed to deliver product changes faster across growing engineering, data, and product complexity without compromising reliability for buyers, dealers, and customer-facing vehicle marketplaces.

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Reduced development timelines from 2–3 weeks to 2–3 days for select projects
~1,000 builder roles using Codex
~10x faster development cycles (weeks → days)
~2,000 employees enabled with AI tools

Reported impact

Business outcome

AutoScout24 Group shortened selected development timelines from weeks to days, raised engineering throughput and code consistency, reduced manual review work, and expanded AI-driven experimentation across both technical and nontechnical teams.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

AutoScout24 Group adopted a two-layer OpenAI rollout that paired broad access with deep workflow integration. ChatGPT was deployed across the company so roughly 2,000 employees could use AI in daily work and build baseline literacy across functions. In parallel, around 1,000 builder roles embedded Codex into engineering, data, and product processes after a three-month evaluation showed gains in usability, workflow fit, code quality, and productivity. Teams used Codex for automated pull-request reviews, large-scale refactoring, technical documentation, and post-incident analysis, while nontechnical staff used AI to prototype ideas and validate concepts faster. A cross-functional AI Champions network connected leadership with individual teams so adoption stayed grounded in practical use cases and existing workflows instead of isolated experimentation.

Data flow

Employee questions and drafts flow through ChatGPT for general assistance, while code, pull requests, incident context, and documentation tasks move into Codex inside engineering workflows. Outputs are reviewed by teams and fed back through the AI Champions network to scale working patterns across the company.

Solution architecture

2 components · 2 layers
  1. Serving
    • ChatGPT Provides organization-wide AI access so employees across functions can use AI in everyday work.
  2. Orchestration
    • Codex Acts as the coding agent embedded into engineering, data, and product workflows for reviews, refactoring, documentation, and incident follow-up.

Architecture clues

  • Adoption focused on embedding AI into existing workflows rather than treating it as a standalone tool
  • Codex was selected after a three-month evaluation across teams
  • Cross-functional AI Champions network translated central guidance into team-level use cases
  • Dual-layer rollout combined company-wide ChatGPT access with deeper Codex integration for builder roles

Evidence from the source

"ChatGPT was rolled out across the organization, giving approximately 2,000 employees access to AI tools"
"Codex quickly proved valuable across several high-impact use cases, including automated pull request reviews, large-scale refactoring, technical documentation, and post-incident analysis"
"Reduced development timelines from 2–3 weeks to 2–3 days for select projects"
"the company embedded Codex into its engineering, data, and product workflows, equipping around 1,000 builder employees"
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
    2 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

    AutoScout24 Group customer story

    First-party
    https://openai.com/index/autoscout24/
    Source type
    Vendor-published customer story
    Published
    May 12, 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 May 12, 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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