AI for Logistics Companies: Order Management and Customer Contact

In many freight forwarders, the dispatch phone rings more often for information than for orders. AI for logistics companies answers status questions, notifies recipients of deliveries and records transport enquiries in a structured way, so dispatchers can spend their time on route planning. This article shows which tasks are suitable, how an introduction works and where people remain indispensable.

Where dispatch time goes

A typical morning at a mid-sized freight forwarder: a shipper asks whether the pallet to Augsburg is already on its way. A private customer wants to know when his sofa will be delivered, and whether it can be after 17:00. A driver reports that nobody is opening up at the loading bay. In between, an email arrives asking for three pallet spaces to Austria, which needs a quote by lunchtime. Each of these interruptions is short on its own. Together they chop the working day into small pieces, and the actual planning slips into the afternoon.

The enquiries differ a lot, though. Some can be answered from data that has long been in the transport management system. Others need experience, negotiating skill or a decision. Sensible automation starts exactly at this dividing line.

Then there's the timing. Many enquiries come in early in the morning, before the routes are fixed, or late in the afternoon, when recipients get home from work and check where their delivery is. Dispatch is often thinly staffed at these edges of the day, and that's when calls get lost or end up on the answering machine.

What AI can take over for logistics companies

Status information around the clock

An AI assistant on the phone, in the website chat or on WhatsApp asks for the shipment number or order reference, reads the current status from your system and passes it on in plain language. One rule matters here: the assistant only says what the data supports. If a scan is missing or the situation is unclear, it doesn't promise a time but takes a callback request. The article on tickets and customer updates in the service centre describes how such feedback gets into the ticket system in a structured way.

Proactive delivery notifications

Many status calls happen because nobody let the customer know. A message at loading, an announcement of the time window the day before and a note if there's a delay answer most questions before they are asked. For deliveries to private customers, the recipient can reply directly to the message and choose a different time window, as long as your planning allows it. That saves not only calls but also wasted trips. For WhatsApp as a channel, the article on WhatsApp Business automation is worth a look.

Recording transport enquiries in a structured way

For a reliable quote, dispatch always needs the same details: pick-up and destination, date, number of packages, weight, dimensions or loading metres, special requirements such as a tail lift, refrigeration or dangerous goods. An assistant asks for all of these, instead of the dispatcher having to call back three times. For standard routes with fixed price tables, this can produce a draft quote that someone approves. Special transports go straight to the person responsible, with all the details recorded. There is more on the principle in the article on automatic quote creation.

Several languages

Recipients, drivers and partners don't always speak German. An assistant that also takes enquiries in English, Polish or Romanian saves dispatch the translation work. The article on multilingual AI chatbots covers what to watch out for.

Worked example: status calls in dispatch

The following figures are assumptions for illustration, not measurements. Suppose your dispatch team receives 100 status enquiries a day, and each takes about four minutes including looking things up and making a note. That's around 400 minutes, more than six hours of working time a day, spread across several people and countless interruptions.

Suppose further that proactive notifications prevent some of these calls, and the assistant answers every second one of the rest itself because the status is clearly in the system. The difficult cases then stay with people, and dispatch gets back blocks of uninterrupted time. How large this share is in your case depends mainly on data quality: where scans are patchy, even the best AI can't give an answer. Keep a count for a week before you start, so you have a real baseline for comparison.

Introduction in five steps

1. Count and sort enquiries: a week-long tally by type of enquiry, channel and time of day.
2. Clarify data access: does your TMS offer an interface for status queries? If not, are there exports or automatic emails you can use?
3. Set rules: what may the assistant say and what not? When does it hand over to a person, for example with damage, complaints or angry customers?
4. Start with one channel: usually phone status information outside office hours or delivery notifications for private customers.
5. Fine-tune after four weeks: review unanswered questions, add rules, then expand.

Limits and data protection

Damage reports, liability questions, customs and dangerous goods belong in experienced hands. The assistant can record information and request photos, but a person does the assessment. Price negotiations with regular customers aren't a job for a machine either.

On data protection: you may use recipient data you receive from the shipper for delivery, but not for your own advertising. You sign a data processing agreement with the AI provider, and data should be processed in the EU wherever possible. If vehicle positions are used, that also concerns the drivers' employee data, which has to be agreed with the works council if there is one. The assistant should identify itself as an AI on the phone and in the chat, as the EU AI Act requires.

When is it not worth it? If you serve a few large regular customers with a fixed contact person and hardly get any calls from recipients, an assistant brings little. The same applies as long as the status data in the system is unreliable. Then data quality is the first project.

Neurobots provides digital employees that take calls, WhatsApp messages, text messages and emails around the clock and pass requests to your systems in a structured way. The data is held on servers in Frankfurt, and a certified partner handles the setup.

Frequently asked questions

Does this work without a modern TMS?

To a limited extent. Without access to status data, the assistant can record and forward enquiries, but can't give information. For delivery notifications, an export of the route data is sometimes enough.

How do business customers react to an AI assistant?

What matters is that they get correct information quickly and can reach a person at any time. Anyone stuck in a loop with a complicated case gets annoyed, whether it's a person or a machine.

Can the AI accept orders bindingly?

Technically yes, and it mainly makes sense for standard transports with clear prices. Anything with special conditions should be confirmed by a dispatcher before a vehicle is scheduled.

What happens if a delay isn't in the system yet?

Then the assistant can't know about it either. That's why it's important that drivers or dispatchers log disruptions promptly, for example via an app or a short message. The faster the information is in the system, the better the answer the customer gets.

Conclusion

AI for logistics companies takes the load off where data already exists and questions repeat: status, delivery notifications, recording enquiries. Negotiating, planning and difficult cases stay with dispatch. The digital assistant for service centres shows how a digital employee handles recurring enquiries in a structured way.