5 Concrete AI Use Cases for Logistics Companies

AI in logistics: Practice over hype

Most articles about AI in logistics talk about autonomous warehouses, self-driving trucks, and predictive supply chains. That sounds exciting — but has little to do with the reality of a logistics company with 50-200 employees.

Here are five use cases that work today, with existing technology, for midsize operational teams.

1. Automatically check delivery notes

Problem: Every day, someone manually checks delivery notes against orders. Does the quantity match? The price? The article number?

AI solution: Delivery note via email or scan → AI extracts relevant data → automatic comparison with the order → only deviations are flagged.

Result: The team only checks exceptions. Typically: 60-70% less checking time.

2. Auto-respond to status enquiries

Problem: “Where is my delivery?” These emails consume hours daily.

AI solution: AI scans incoming status enquiries → pulls current status from TMS/ERP → creates a personalised draft response.

3. Classify and assign freight documents

Problem: CMR waybills, customs papers, packing lists — everything arrives by email, everything needs to be assigned to the right case.

AI solution: AI recognises the document type → assigns it to the correct project/order → notifies the responsible person.

4. Build an internal knowledge base

Problem: Knowledge is trapped in heads and scattered documents.

AI solution: Load existing documents into an AI-powered knowledge system. Employees ask questions in natural language, the AI finds the answer.

5. Automatically create weekly reports

Problem: Every Friday someone creates the weekly report by manually gathering data from various sources.

AI solution: AI collects data automatically → creates a structured report → sends it to management.

Your starting point

Which of these use cases fits your company? Our free AI Readiness Check helps you find the best entry point for your situation.