Make Your Logistics Ready for the AI Revolution: TAL 2025 Speakers Share Their Experience

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The automotive industry stands before a revolution. The rise of artificial intelligence is changing established processes and bringing new possibilities. But what obstacles are companies having to overcome so that they can deploy AI effectively? The experts and industry leaders appearing at Trends in Automotive Logistics 2025 – with the tagline “Digital. Future-Proof?” – are sharing their experience and viewpoints on the key challenges in deploying AI.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

1. Data quality and availability
In automotive, data is often fragmented and inconsistent.
Solution: Introduce reliable data management processes that take care of data collection, cleansing and standardisation throughout all your departments.

2. Integration with your legacy systems
Your legacy systems may not be compatible with modern AI technologies.
Solution: Develop a gradual integration plan that enables modernisation and ensures new solutions can work together with existing systems. Consider the use of middleware to bridge the gaps between technologies.

3. Compliance with regulations and security
Because of the regulations in automotive, it can be challenging to ensure compliance with security standards when you’re deploying AI.
Solution: Study all the regulations right at the start of development so that you can understand all the requirements. Don’t forget strict testing and validation to ensure your artificial intelligence systems meet the security standards.

4. A lack of qualified workers
There’s a lack of experts who understand both AI and the automotive industry.
Solution: Invest in the development of your existing employees. Work with educational institutions and create programmes to prepare future talents for roles in the areas of AI and automotive technologies.

5. Cultural resistance
Your employees may be defensive towards the changes that AI deployment brings, due to fears of a shift in jobs or unfamiliarity with new technologies.
Solution: Support a culture of innovation and constant improvement. Inform people clearly about the benefits of AI and involve employees in the deployment process to gain their support.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

The challenges vary in terms of deployment in production and in end-products. In production, AI can be used for planning or quality control or when designing the project. One thing I can’t overlook here is the use of AI in the automated creation of standardised projects and the subsequent development of software using tools such as Industrial Copilot. One major challenge is the deployment of AI in autonomous driving, where the regulatory and legal framework is yet to be developed. The automotive industry has to overcome this challenge in concert with political leaders and, in particular, reflect ethical considerations and gain the public’s trust.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

The biggest challenge is changing the mindset and culture at your company. Don’t rely on having an external consultant come in and bring a miraculous solution. Use your common sense and natural intelligence even when applying AI. Start from the goals you want to achieve, and then seek suitable means for them.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

I would summarise my answer into five points.

1. No real added value
A lot of companies want to use artificial intelligence just because people are talking about it, not because they’re looking for clear added value, for example in customer satisfaction, the development of cross-selling and upselling, process optimisation and the improvement of quality.

2. A lack of communication
If users aren’t told about the use of artificial intelligence, they can’t even be aware they’re communicating with an AI-supported system. Later on that can lead to a loss of trust and rejection.

3. A low-quality database
Artificial-intelligence systems typically identify repeating patterns in large amounts of data; if the database is insufficient, their results are not relevant.

4. Blind faith
Even though IT systems sometimes display amazing “intelligence”, they’re not always suited for AI, for example if it’s not possible to provide data that can be sufficiently put to use in operations.

5. A lack of transparency
In light of the many influencing factors and methods determining the results from AI, it can be difficult or even impossible to sufficiently understand how a given result has been reached.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

The main challenge when deploying AI in automotive – and not only there – is ensuring it has real value. AI can prepare solutions that people can easily utilise, review and confirm. Someone then has to go on and give them final approval. For now, AI can’t take the place of people, but it can make their work easier.

But if feedback isn’t easy and natural, people won’t provide it, because they won’t see the point. Yet when the whole process is set up well, we get a functional solution as well as a system for permanent change.

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What are the main challenges when deploying AI in industry, and how can you overcome them?

As with every technology, it’s all about the readiness of the systems and people. For the systems, it’s important to know the target architecture and to have thought well about integrations, data and platforms, so that we can keep the whole ecosystem running as easily, quickly, but also sustainably as possible. As for people, there’s a need for education and an active community. If you can build a community of enthusiastic people, that greatly speeds up deployment and development.

 

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What are the main challenges when deploying AI in industry, and how can you overcome them?

Undoubtedly one of the greatest pitfalls is that AI systems have to comply with EU legislation, especially concerning transparency, security and personal data protection. Suppliers will have to inform drivers and other users that they are using AI, and how they are using it. Let’s not forget security and reliability, along with the need for thorough testing and the introduction of security measures to prevent abuse.

Companies will have to collect data on the use of AI and then use it responsibly, that is for example to acquire consent from drivers and other users, ensure this data is protected against unauthorised access etc. Last but not least, companies will have to consider the ethical consequences of using AI systems in their vehicles and deal with liability for accidents caused by autonomous vehicles, risks connected with poor object detection etc.

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