How AI Agents Take Over Delivery Scheduling – Insights from the Webinar with ProLogistics

Did you miss the webinar?Click here for the complete recording.

Tobias Braun (Head of Product Management at proLogistik) observes that the pressure on service teams is massively increasing due to the shortage of skilled workers and rising cost pressures. With the AI agent "Luisa," proLogistik demonstrates how to make the leap from the "monotonous tin can" to an intelligent, autonomous employee. Together, we examined in the webinar how AI is making tomorrow's transport service more efficient.

We've summarized the most important questions and answers from the live talk for you:

What distinguishes a simple chatbot from a true AI agent?

Tobias: For us, an AI agent is a digital employee. While a chatbot often only answers questions so that you can then continue manually, an agent acts autonomously. We train it with instructions, a data base, and specific skills so that it can perform actions completely independently – like a "digital Tobi."

Why does the logistics industry need this technology right now?

Tobias: The well-known problems like the shortage of skilled workers and enormous cost pressure have actually intensified. At the same time, we've now reached a technological maturity level with AI frontier models: the models are very good and have also become affordable in terms of cost. This is now a "match." We can deploy employees where they provide more added value, and let AI agents perform processes autonomously.

How is it ensured that the AI doesn't "hallucinate" when scheduling appointments?

Tobias: The secret lies in deep integration. Our agent Luisa is directly linked to the transport management system. She doesn't make up appointments, but only mentions valid delivery dates that have already been verified by dispatch. Luisa accesses the data source directly and therefore doesn't "hallucinate."

What are the biggest challenges in training such an agent?

Tobias: Modern AI models are extremely powerful. The challenge is to restrict them to the specific use case. We give Luisa clear guardrails: for example, she shouldn't answer questions about the weather or make things up. We had a case at the beginning where she authorized a placement permission that we didn't officially offer yet. You have to instruct the model to strictly adhere to the guidelines.

How do AI agents handle sensitive personal data?

Tobias: We only give the model as much information as it needs for the task. For a delivery appointment, Luisa doesn't need to know the customer's name or exact address. This personal data remains in the protected TMS environment, while the AI model only works with the facts relevant to the appointment.

Can these agents handle complex inquiries in the future?

Tobias: Absolutely. Through the platform concept, we can give the models tools that allow them to access other applications or databases. This way, they can also check the status of picking orders or cancel order items in the future. This enables the automation of very large and complex processes.

Conclusion: Service Automation as a Lever Against the Skilled Worker Shortage

The webinar showed: AI agents like "Luisa" are no longer just "basic text-to-speech," but high-performance tools for relieving service teams. Standardized processes in delivery scheduling can be safely delegated to AI agents. This saves time in dispatch, protects against misplanning, and thus directly reduces transport costs.

Did you miss the webinar?

👉 Click here for the complete recording.

👉 Click here for proLogistik's even profile.