Email orders. Straight to your ERP.

What it means to automate email order processing

Many businesses still take most of their orders by email. A customer sends a message, a PDF purchase order or a spreadsheet, and someone on your team types it into the ERP or order system line by line. It works, but it ties skilled people to copying, and every retyped line is a chance for a typo.

Automating that work does not mean handing your order desk to a chatbot. It means a small, dependable flow that does what your team already does, in the same order, every time. AI handles the part that used to need a person: reading a message written in any format and understanding what the customer wants. Plain software handles everything that should follow fixed rules.

In practice, the flow has five steps. Most of the value comes from the last two: checking every order against your own data, and routing anything unclear to a person with the question already spelled out.

• Collect: pick up every new order from the shared inbox, including attachments.
• Read: AI extracts the customer, items, quantities, prices and delivery details.
• Match: link each line to your own customer numbers and article codes.
• Check: apply your rules for prices, minimum quantities, stock and delivery dates.
• Post: send clean orders to your ERP and route the rest to your team for review.

Why manual order entry slows teams down

Manual order entry rarely shows up as a problem on its own. It hides inside the working day: ten minutes here, twenty there, and a backlog every Monday morning or after a holiday. The people doing it are often the ones who know your customers best, and they spend that knowledge on copying instead of on service.

The bigger cost is mistakes. A wrong quantity, a mixed-up article code or a missed delivery date turns into a wrong shipment, a credit note and an awkward phone call. Errors are most likely exactly when volume peaks and people rush, which is also when they are hardest to catch.

To see whether automation is worth it for you, count rather than guess. Take a normal week, count the orders that arrive by email, and time how long a typical one takes to enter and check. Add the time spent fixing mistakes. That figure, multiplied over a year, tells you more than any general estimate could.

What to prepare before you automate order intake

The best starting material is a sample of real orders. Collect a few weeks of them, including the awkward ones: the scanned form, the order buried in a long email thread, the customer who uses their own product names. These examples show what the flow must handle, and later they become the test it must pass.

Next, write down what your team knows without thinking about it. Which customer always means the large bags when they say the usual? Which orders need a second look before they go out? These unwritten rules are what turn a clever demo into a system you can trust, so they deserve an hour with the people who do the work.

• A sample of real order emails and attachments, from many different customers.
• Your customer list and article master data, with codes as your ERP knows them.
• The rules your team applies: price lists, minimum quantities, delivery days.
• How your ERP accepts orders: through an API, an import file or a connector.
• Who reviews the exceptions, and how quickly they need to respond.

Where AI helps and where plain rules work better

AI is good at the messy part. It can read an order written as free text, a table pasted into an email, or a PDF in a layout it has never seen, and turn it into structured data. It copes with spelling mistakes, abbreviations and two languages in one message, which is exactly where older template-based tools tend to break.

It should not be the one making business decisions. Whether a price is correct, a customer is within their credit limit or an item is in stock are questions with a definite answer in your systems, so ordinary code should check them. A model should never invent an article code: it should pick from the codes you have, or say it is not sure.

That split is what makes the flow dependable. Every extracted order is checked against your data before anything is posted, and every check that fails becomes a clear question for a person. When an automation can explain why it stopped, your team will trust it when it does not.

How to roll out order automation safely

Start by running the automation next to your team rather than instead of it. For a couple of weeks it processes every order in the background while people keep entering them as usual. Comparing the two shows where it agrees, where it gets things wrong, and which rules are still missing.

Then let it post the simplest orders on its own: regular customers, standard articles, nothing unusual. Everything else still goes to a person, with the extracted data filled in so they only confirm or correct it. As the comparison stays clean, you widen the scope one order type at a time.

Keep measuring after launch. Track how many orders go through without anyone touching them, how often people correct a field, and how long it takes from email to confirmation. Those numbers tell you where to improve next, and they show the rest of the business what the automation actually does.

How Walhallah builds order processing automations

We start with one order flow and your own examples. A focused prototype that reads real orders and shows what it would post often takes days, so you can judge the results before committing to more. From there, a production version that connects to your ERP usually follows in weeks rather than months.

For Wegter, we built an automation that turns email-based orders into structured SAP workflows, removing a manual bottleneck from the order desk. The same pattern fits any business that receives orders by email: AI does the reading, engineering does the checking, logging and connecting, and people keep the final word on anything unusual.