AutoScout24 and AI engineering: how Codex speeds up development

The context: AutoScout24 and the pressure to develop faster

AutoScout24 is one of Europe's leading platforms for buying and selling vehicles. It operates in multiple markets and runs a complex technology infrastructure. That makes development speed a critical competitive factor.

During 2025, the group launched a structured program to adopt AI across its engineering teams. The goal wasn't to experiment. It was to scale. So the choice fell on tools that were already mature: OpenAI's Codex and ChatGPT.

The case study published by OpenAI documents the results of this integration. It also describes how the technical team worked. It's a useful reference for anyone considering a similar path.

The adoption timeline: three distinct phases

AutoScout24's program didn't start with a massive rollout. Instead, it followed an incremental logic. First, the team identified the most repetitive tasks: generating boilerplate, writing unit tests, reviewing technical documentation.

Next, adoption extended to code review flows. Codex was integrated as an assistant in the CI/CD pipeline. Developers started receiving contextual suggestions right in their working environment.

Finally, in the third phase, the focus shifted to code quality. ChatGPT was used to analyze recurring error patterns and suggest targeted refactoring. As a result, the number of bugs in production dropped measurably.

The numbers behind the case

The OpenAI document doesn't publish every metric in detail. Still, a few indicators stand out clearly. Development cycles got shorter. Test coverage increased. And time spent on low-value tasks fell significantly.

These results aren't isolated. Similarly, research by McKinsey on the economic potential of generative AI indicates that developers using AI tools complete tasks up to 50% faster. AutoScout24's data fits into an established trend.

In particular, the case highlights an often underestimated aspect: the impact on onboarding new developers. With Codex available as a contextual assistant, ramp-up times have shrunk. That's a real competitive advantage, especially in markets where technical talent is scarce.

Winners and losers in the engineering ecosystem

Who gains from a well-structured adoption like AutoScout24's? First, senior developers. Freed from repetitive tasks, they can focus on architecture and complex problem solving. QA teams also benefit from broader, automated test coverage.

On the other hand, the ones at risk of losing ground are junior profiles who don't adapt. That's nothing new, though. It's a pattern we've seen with every wave of technological automation. So the right response isn't to resist adoption, but to speed up training.

There's also a third player who gains less visibly: the business. Shorter cycles mean shorter time-to-market. As a result, the ability to respond to market changes improves. For AutoScout24, operating in several European countries makes this advantage even more relevant.

SHM Studio's take: what it means for Italian SMEs

The AutoScout24 case is often read as an example reserved for large organizations. At SHM Studio, we don't share that view. The operating principles applied here scale down.

An SME with a team of 3-5 developers can adopt Codex for test generation and code review. It can also use ChatGPT to speed up technical documentation and debugging. These tools are affordable. So the barrier isn't financial: it's organizational.

The real obstacle for Italian SMEs is the lack of a structured adoption framework. Without a clear methodology, AI gets introduced in an ad hoc way. Results end up uneven and hard to measure. This is exactly the problem AutoScout24 solved with its three-phase approach.

To learn more about how to build a similar path, explore the AI services from SHM Studio, designed specifically for mid-sized companies.

The work still in progress: unresolved challenges

The AutoScout24 case study is positive. Still, it would be wrong to present it as free of issues. Some challenges remain open, even for an organization as well structured as this one.

First, prompt governance. When dozens of developers use ChatGPT independently, consistent output isn't guaranteed. You need internal guidelines on how to use AI tools. That takes time and organizational oversight.

Then there's the issue of dependence on a single vendor. Relying exclusively on the OpenAI ecosystem exposes you to lock-in risk. Gartner research on the AI Hype Cycle stresses that diversifying tools is a best practice for mature organizations.

Finally, the question of measuring ROI remains open. Shorter development cycles are measurable. But quantifying the long-term impact on architectural quality is harder. It's work that AutoScout24, like many other organizations, is still building.

Next moves: what to do now so you don't fall behind

The AutoScout24 case points to a few concrete directions. Here's how to turn them into priority actions for Italian teams.

• Map the repetitive tasks in your current development flow. They're the ideal candidates for a first AI integration. They're also the ones where ROI is fastest to measure.
• Define baseline metrics before introducing the tools. Without a starting point, you can't measure improvement. Adoption stays anecdotal.
• Train your team to use Codex and ChatGPT effectively. It's not just about access to the tools. It's about building prompt engineering skills and the ability to critically review outputs.
• Build your governance with internal guidelines on using AI in code. This includes policies on copyright, security and output quality.

For SMEs that want to start this journey, SHM Studio's digital marketing services and web solutions can support your team in defining a coherent roadmap. And if you operate in B2B, LinkedIn campaigns are an effective channel for communicating your technological evolution to stakeholders.

It's also worth considering how AI adoption affects your SEO strategy and content production. In particular, SEO copywriting benefits from the same tools used in engineering, applied to editorial production.

In short: a model to study, not to copy

AutoScout24 has built a solid case study. But the value isn't in copying it word for word. It's in understanding the principles behind it: incremental adoption, rigorous measurement, structured governance.

These principles hold true whatever the size of your organization. So even an SME with limited resources can draw concrete, practical takeaways from this journey. The difference between companies that adopt AI effectively and those that experiment without results is almost always about method, not technology.

Want to dig deeper, or get an assessment of your own situation? You can contact SHM Studio or explore related articles on the blog. And if you're weighing investments in Google Ads campaigns integrated with AI workflows, now is the time to build a coherent strategy.