AutoScout24 scales engineering with AI-powered workflows

Results

~10x

Faster development cycles (weeks → days).

Results

~2,000

Employees enabled with AI tools.

Results

~1,000

Builder roles using Codex.

AutoScout24 Group 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.

“AI is changing how we build, but more importantly, it’s changing what we can deliver to our users and dealer partners. Faster iterations mean better experiences for buyers and more effective ways for dealers to reach and convert customers.”

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.

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.

“Codex has emerged as a key enabler in our engineering workflows, delivering measurable impact in productivity, quality, and speed.”

• Reduced development timelines from 2–3 weeks to 2–3 days for select projects

• Increased engineering throughput, enabling faster iteration and experimentation

• Improved code quality and consistency through automated reviews

• Reduced manual workload in pull request reviews and documentation

• Expanded innovation capacity by enabling non-engineering roles to prototype ideas

• Combine broad AI access with deep workflow integration to maximize impact

• Prioritize real-world use cases over top-down mandates to drive adoption

• Establish cross-functional champions to scale knowledge organically

• Evaluate AI tools rigorously using measurable engineering metrics

• Focus on augmenting—not replacing—existing team capabilities

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.

As the company scales its AI capabilities, it aims to further enhance user experiences, empower teams across functions, and unlock new efficiencies in how products are built and delivered.