AutoScout24 scales engineering with AI-powered workflows
AutoScout24 Group has implemented a dual-layer AI strategy using ChatGPT and Codex to scale engineering and operational workflows. The online car marketplace rolled out ChatGPT to all 2,000 employees and embedded Codex for approximately 1,000 builders. According to the company, these tools have reduced development timelines for select projects from weeks to days while improving code quality and team productivity.
The Core Development
AutoScout24 Group (opens in a new window) is the largest pan-European and Canadian online car marketplace, connecting more than 30 million monthly users with over two million vehicle listings. Operating across multiple brands—including AutoScout24 in Europe and AutoTrader.ca in Canada—the company supports a network of 45,000 dealer partners and employs around 2,000 people globally.
As product expectations increased and system complexity grew, AutoScout24 Group faced mounting pressure to deliver faster innovation without compromising reliability. This is closely tied to the company’s goal of continuously improving how buyers search, evaluate, and purchase vehicles, and how dealers successfully market and sell their inventory. With large-scale migrations, legacy systems, and rising engineering demand, incremental improvements were no longer sufficient. The emergence of large language models presented a timely opportunity to fundamentally rethink how software is built, tested, and scaled—making OpenAI a natural partner in driving this transformation.
—Frederik Kraus, Chief Technology Officer, AutoScout24 Group
Real-World Use Cases & Applications
In parallel, the company embedded Codex into its engineering, data, and product workflows, equipping around 1,000 builder employees with a coding agent integrated directly into their daily processes. Codex was selected following a three-month evaluation across teams, where it demonstrated strong performance in usability, workflow compatibility, and measurable improvements in productivity and code quality.
To ensure adoption at scale, AutoScout24 Group established a cross-functional AI Champions network. This group created a feedback loop between central leadership and individual teams, helping translate AI capabilities into practical, real-world use cases. The approach encouraged organic adoption and ensured AI was embedded into existing workflows rather than treated as a standalone tool.
Codex quickly proved valuable across several high-impact use cases, including automated pull request reviews, large-scale refactoring, technical documentation, and post-incident analysis. Beyond engineering, AI tools enabled non-technical roles to prototype ideas and validate concepts independently, accelerating innovation across the organization. This ultimately supports faster delivery of improvements across the platform, benefiting both buyers and dealer partners.
Strategic Perspective & Ecosystem Impact
AutoScout24 Group implemented a dual-layer AI adoption strategy to balance broad enablement with deep technical impact. ChatGPT was rolled out across the organization, giving approximately 2,000 employees access to AI tools and establishing a strong baseline of AI literacy across functions.
Expanded innovation capacity by enabling non-engineering roles to prototype ideas
AutoScout24 Group continues to expand its use of AI across both internal workflows and customer-facing products. Future efforts will focus on deeper integration of AI into core systems, enabling even greater automation and intelligence across the platform.
Why it matters
This case study demonstrates how large enterprises can successfully combine broad organizational AI access with deep engineering workflow integration. It highlights measurable productivity gains and faster development cycles achieved through tools like ChatGPT and Codex.