AutoScout24 boosts engineering capabilities through AI-driven workflows

Meta description: See how AutoScout24 Group uses Codex and ChatGPT to shorten development cycles, improve code quality, and spread AI adoption across teams.

Tags: Codex, ChatGPT, AI in software development, developer productivity, AutoScout24

Featured image: A friendly abstract illustration of a car marketplace app being built, with glowing puzzle pieces and chat bubbles floating over code blocks, in soft blue and orange tones.

If you're a developer, engineering lead, or team manager wondering how real companies put AI coding tools to work, AutoScout24 Group's experience with Codex and ChatGPT offers a useful, practical example. This article walks through what the company set out to achieve, how it uses these tools day to day, and what changed as a result: faster development cycles, better code quality, and broader AI adoption across the organization. By the end, you'll have a clearer picture of whether a similar approach could fit your own team.

Why AutoScout24 Group Turned to AI Coding Tools

AutoScout24 Group operates one of Europe's best-known online car marketplaces, which means its engineering teams constantly build and maintain web platforms, internal services, and customer-facing features. Like most modern software organizations, it faces familiar pressures: tight release schedules, a growing codebase, and the need to keep quality high while shipping quickly.

That combination is exactly where tools like OpenAI's Codex and ChatGPT come in. Rather than treating AI as an experiment tucked away in a research corner, AutoScout24 Group brought these tools into everyday engineering work. The goal was straightforward: shorten the time between idea and working code, reduce repetitive tasks for developers, and make it easier for more people across the company to benefit from AI, not just a few specialists.

It's worth being clear about what this kind of adoption usually looks like. Codex-style tools help write, review, and refactor code directly in a developer's workflow, while ChatGPT serves as a general-purpose assistant for explaining concepts, drafting documentation, brainstorming solutions, and answering questions about unfamiliar technologies. Used together, they cover a wide range of daily tasks.

Speeding Up Development Cycles

One of the clearest benefits AutoScout24 Group reports from using Codex and ChatGPT is faster development cycles. In practice, speed gains come from several places:

• Faster boilerplate and scaffolding. Setting up new components, tests, or configuration files is repetitive work. AI tools can draft these quickly, letting developers focus on the parts that actually require judgment.
• Quicker debugging. When something breaks, asking ChatGPT about an error message or pasting in a stack trace often points to a likely cause in minutes instead of a long search through forums.
• Smoother context switching. Developers frequently touch code written by others, sometimes years ago. AI assistants can summarize what a module does, making it faster to get oriented before making changes.

The result is not that developers work less—it's that the annoying, low-value parts of the job shrink, and the time between starting a task and shipping it gets shorter. For a marketplace business where product teams want features live quickly, that difference compounds across every sprint.

Improving Code Quality

Speed means little if quality suffers, and this is where AI tools have proven surprisingly valuable at AutoScout24 Group. Used carefully, they act like an always-available second pair of eyes.

Developers can paste a function into ChatGPT and ask for edge cases they might have missed, or have Codex suggest a cleaner structure for tangled logic. AI can also help write unit tests that a busy developer might otherwise skip under deadline pressure, and it can flag inconsistent naming or style across a codebase.

A few habits make this work well in practice:

1. Treat AI suggestions as drafts, not final answers. A human developer always reviews and approves what gets committed.
2. Use AI to ask "what could go wrong here?" during code review, not just to generate code.
3. Encourage developers to ask the tools to explain unfamiliar legacy code, which reduces the risk of introducing bugs through misunderstanding.

The honest caveat: AI-generated code can look convincing while still being wrong. Teams that benefit most treat the tools as accelerators for skilled engineers, not replacements for engineering judgment.

Expanding AI Adoption Across the Company

Perhaps the most interesting part of AutoScout24 Group's story is the third pillar: expanding AI adoption beyond a small group of enthusiasts. Tools like ChatGPT are useful far outside pure engineering—product managers can draft user stories, support teams can summarize tickets, and analysts can get help with queries and scripts.

Broad adoption typically requires a few deliberate steps:

• Easy access. People can't adopt tools they can't reach. Providing approved, company-supported access to ChatGPT and Codex removes the biggest barrier.
• Sharing what works. When one team finds a workflow that saves time, publicizing it helps other teams skip the trial-and-error phase.
• Clear guidelines. Simple rules about what data can and cannot be shared with AI tools keep usage safe and build confidence among cautious colleagues.

As more employees see colleagues saving time, adoption tends to spread on its own. The company moves from "a few people experimenting" to "AI is just part of how we work"—which is arguably the most durable benefit of all.

What Other Teams Can Learn from This

AutoScout24 Group's experience suggests a practical playbook for any organization considering Codex and ChatGPT:

1. Start with everyday engineering tasks where the payoff is immediate, such as boilerplate, tests, and debugging help.
2. Keep humans in the loop for all code that ships.
3. Extend access beyond developers so the whole organization benefits.
4. Set simple, clear usage guidelines early.
5. Share wins internally to build momentum.

None of this requires a large dedicated AI team to begin. It requires willingness, access to the tools, and a culture that treats AI output as helpful input rather than final truth.

Who should use it / who should skip it

Who should use it: Engineering teams and product organizations with repetitive coding work, large or unfamiliar codebases, and pressure to ship faster. It's especially valuable for companies that want company-wide AI adoption, not just isolated developer experiments. Teams with solid code review practices will get the most out of it, since human oversight is what turns AI speed into real quality.

Who should skip it: Very small projects with simple, stable codebases may not see enough benefit to justify the setup. Teams that cannot allow code or data to be processed by external tools—due to strict regulatory or contractual limits—should first confirm what's permitted. And organizations hoping AI will replace experienced engineers entirely will be disappointed; these tools amplify good developers rather than substitute for them.