AI in Transportation Management: Use Cases in a TMS

Eight practical use cases

1. 1Request intake: Reading rate requests that arrive in different formats and turning them into structured records.
2. 2Quoting: Finding the right rate, applying the margin and writing the quote.
3. 3Document processing: Extracting data from bills of lading, AWBs, CMRs, invoices and packing lists and checking it against the file.
4. 4Planning and routing: Recommending consolidation, mode and carrier choices.
5. 5Tracking and prediction: Estimating arrival times and flagging delay risk early.
6. 6Customer communication: Answering status questions and drafting update emails.
7. 7Freight invoice audit: Comparing the carrier invoice with the agreed price and finding differences.
8. 8Analysis: Answering questions such as “which lane lost money last quarter?” in natural language.

Which AI technologies are used?

• Large language models (LLMs): Understanding, writing and summarising free text such as emails and documents.
• Document intelligence (OCR plus interpretation): Extracting fields from scanned or PDF documents.
• Machine learning: Predicting arrival times, demand and prices.
• Optimisation algorithms: Route and load planning.
• AI agents: Automations that carry out several steps in sequence and can use tools.

Example: one request, from quote to invoice

A customer emails about a part load to move by road freight from Istanbul to Hamburg. AI-native TMS software reads the email, extracts the cargo details and matches them to the customer record. It finds suitable options in the current rates and prepares a draft quote; an operations specialist checks it and sends it.

When the quote is accepted, the shipment file opens automatically. When the carrier's CMR is uploaded, its fields are read and compared with the file. During transit the control tower watches for delay risk; if one appears it alerts the right person and drafts the customer update. After delivery the carrier invoice is compared with the agreed price and the customer invoice is presented for approval.

Risks and good practice

• Human approval: Critical steps such as pricing, payments and customer-facing messages should require sign-off.
• Traceability: What the AI did, and on which data, should be logged.
• Data quality: Without current rates and clean customer data, recommendations stay weak.
• Data privacy: Be clear about where customer data is processed and whether it is used to train models.
• Phased rollout: Start with one process, such as document reading, measure the result, then expand.

For companies operating in Europe or serving EU customers, the legal framework is set by the EU AI Act.