In a logistics company, customer service spends a large part of the day on the same few questions. Where is my package, why hasn't it shipped, when will it arrive. Behind them, the admin team retypes data from delivery notes (surat jalan), vendor invoices and proof-of-delivery slips into the system. When a shipment goes wrong, the details are scattered across driver WhatsApp groups, warehouse emails and supervisor notes.
AI for logistics companies is most useful in exactly this kind of work: repetitive tasks where the data already exists in your systems. This article covers four realistic use cases, the data and systems involved, the risks to manage, and how to start without a large project.
Answering shipment status questions
Status questions are the most sensible place to begin. Retail customers or B2B shippers send a tracking number over WhatsApp, email or a portal. Someone opens the TMS or tracking system, reads the latest status and types a reply. The pattern is the same every time.
An AI assistant can pull the status straight from your system through an API and write a reply in plain language. For example, "The shipment with that tracking number reached the Semarang hub this morning and is scheduled for delivery tomorrow." The data source is what matters. The AI should never guess a status. If the record is missing or looks odd, the assistant should say so and hand the conversation to a person.
For corporate customers, the same assistant can handle broader questions, such as which shipments this month have not been received yet. Access control matters here. Each customer should only see their own shipments.
Extracting data from delivery notes and invoices
Many couriers and distributors still receive documents as photos or scanned PDFs. Signed delivery notes, invoices from trucking vendors, fuel receipts and toll slips. An admin then retypes the document number, date, recipient name, number of packages and invoice amount.
AI can read these documents and turn them into structured data. The result is checked against your system, for example the package count on the delivery note against the package count on the order. If they match, the data goes in. If they don't, or the handwriting is hard to read, the document is flagged for an admin to review.
The admin role stays. The work shifts from typing every document to reviewing the problem cases, which are usually far fewer. For vendor invoices, matching against contract rates also helps finance spot discrepancies before a bill is paid.
Summaries of shipment exceptions
Every day some shipments run late, go to the wrong address, arrive damaged or find nobody home. The information comes from many directions: driver notes, warehouse reports and customer complaints. Supervisors often read dozens of messages just to understand what happened.
AI can condense these events into a single morning report. It lists the problem shipments, the reported cause, who is handling each one and which cases have no follow-up yet. The supervisor can skip the raw messages and go straight to setting priorities.
The summary also helps you see patterns. If delays on a particular route show up every week, that is an operational issue worth discussing, and the data is already organised for the meeting.
Drafting customer updates
When a shipment has a problem, customers should hear about it before they have to ask. Writing a polite, clear message that doesn't promise anything uncertain takes time, especially when there are many of them.
AI can draft these messages from the incident data, covering what happened, what is being done and a new estimated time if one exists. Staff read the draft, adjust it and send it. For sensitive cases such as lost goods or insurance claims, the message should always be written or approved by someone with the authority to do so.
Getting started with AI for logistics companies
All of these use cases rely on data you already have: a TMS or WMS, a tracking system, an ERP or accounting software, and your document archive. If those systems are not connected yet, the first step may be system integration, with AI coming after.
Risks to plan for from the start:
- Recipient names, addresses and phone numbers are personal data protected under UU PDP, Indonesia's Personal Data Protection Law. Limit what you send to the model and choose an AI provider with a clear data policy.
- AI can misread documents. Set matching rules and a clear threshold for when a person must review a document.
- An assistant that answers customers must take the status from the system, never from its own memory.
- Commitments to customers, such as compensation or a new delivery date, are still decided by your team.
- Log every answer and every extraction result so mistakes can be traced and fixed.
The safest way to start is to pick one workflow, for example delivery note extraction for a single branch. Track how many documents are read correctly and how many need correcting over a few weeks. Once the results are stable, extend it to other branches or to the next use case.
Talk it through with Averoa
Averoa helps companies bring AI into the apps and processes they already run, from chat assistants to document extraction, with guardrails and cost control from the start. If you would like to work out which workflow makes sense to start with in your company, you can read more on our AI Integration page.
