From Pilot Project to Operations: How Škoda Auto Is Implementing AI in Logistics

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What’s the best way to deal with 3,500 freight vehicles a day, 93 systems in logistics and rapid data growth? Škoda Auto is betting on a combination of digitalisation, standardisation and the targeted use of artificial intelligence. At Trends in Automotive Logistics 2025, the company’s logistics systems coordinator Mojmír Barák explained how it’s moving AI out of laboratory conditions into real operations and how to build people’s trust in new technologies.

Artificial intelligence is changing logistics

At present, Škoda Auto manufactures twelve car models, two of them exclusively electric, and it supplied 927,000 vehicles to the market in 2024. That puts it in fourth place for sales in the European market. 3,500 freight vehicles pass through its gates every day, and nearly 3,900 finished vehicles ride off towards customers.

An extensive network of processes and systems stands behind all this – 93 systems are used in its logistics, and 76 adjustments have been implemented in the last two years alone. The data volume in the last five years has grown by over 600%. “We approach every new technology as a proof of concept, that is, we test its features and suitability on a small project and gradually bring it closer to business – for example through pilot projects all the way to our Digital Delivery Centre. We gain our people’s trust that AI will actually help them. Today, every last warehouse worker knows they can rely on timely delivery of materials,” Barák says.

Big goals, big challenges

Digitalisation at Škoda Auto won’t happen without some obstacles. Logistics is built on dozens of systems and platforms, many of which have been in place for dozens of years. Although they’re fine-tuned and still reliable, there’s often a lack of the people needed to maintain them. They also can’t be shifted to the cloud, limiting their further development. Then add to that outdated processes that slow down innovation. Standardisation is the solution. Operations like these are given unified tools to simplify administration and support efficiency throughout the company.

From EDI to AI agents: specific projects

Škoda Auto took its first big step towards the use of artificial intelligence in 2019, when it began optimising the loading of shipping containers. Back then, this was still time-consuming work. Employees manually calculated combinations, every team had its own Excel macros and in the end they assembled the results in PowerPoint. There were thousands of possibilities, and finding optimal variants took a lot of time. Today, this process is handled by AI; it knows materials’ weight, shape and nature and is able to exclude dead-end branches and only develop the promising ones. The system is learning nonstop, and it delivers transparent results and enables the team to plan exactly what material will be needed later.

Things work similarly with packing – in the course of shipping over 5,100 containers a year, the AI uses historical data, material prices and package sizes to propose an ideal solution. Tests have shown this approach works, and therefore the automaker is developing it further.

Škoda Auto’s current AI projects include for example:

• Optimisation of shipping and supply routes – AI determines the optimum routes and schedule so that materials reach the places where they’re needed, precisely and on time.
• The deployment of conversational LLM AI agents (the Cognigy service) for swift communication with warehouse staff and suppliers.

The long-term process of introducing SAP is an important framework for all of these projects, as is the transition to the cloud, which provides better data processing and data standardisation.

AI with real benefits – not just for show

Škoda Auto’s experience shows that technology is only a part of success. Equally important, if not more so, is managing the expectations for it, and training future users. “Often one challenge is inflated expectations – technologies work perfectly in laboratory conditions, but real-world operations bring far more variables,” says Barák.

That’s why every innovation has its cycle. From proof of concept, it moves on to a pilot project and then is deployed in a Digital Delivery Centre. This approach allows Škoda to quickly evaluate risks and benefits, fine-tune details and only then expand the solution. This approach has proven itself for example during the introduction of the AI for optimisation of container-based shipping – every successful project brings not only savings of time and money, but also new knowledge that is used when testing later innovations.

People: the key factor for success

Mojmír Barák emphasises that no technology in this world can save logistics on its own. “Whenever a new process is introduced, we always find people who are excited by it – I value this enormously. Thanks to this, the novelty flows on into the community, the group of experts; we pass the know-how on,” he explains. Every innovation is born as close as possible to our business side, and gradually experts are trained who accompany it through the company from there.

AI in logistics: the future according to Gartner (and others)

According to estimates by the analysts at Gartner, by 2028, up to 15% of decisions in businesses will be made by artificial intelligence. Škoda Auto is aiming towards this step by step, not just by introducing technologies, but also by connecting them with processes and people.

According to Barák, the goal here is logistics-driven data, which will eliminate current systems’ siloing. This is key, because the number of data integrations at Škoda Auto is rising by about 50% each year. Therefore, for the future it is betting on effective utilisation of SAP, sensible use of artificial intelligence and the development of AI agents and operators that can accelerate and simplify daily decision-making. A pragmatic approach that uses a journey from proof of concept to routine operations thus remains the main strategy for keeping the firm’s logistics competitive and ready for further growth.

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