Operations
AI for turning email orders into system orders
Collect a sample of real order emails and attachments. Use AI to read each one and pull out the customer, product codes, quantities, prices and delivery date into a fixed format. Match products against your own catalog instead of letting AI guess. Create the order as a draft in your ERP or e-shop, and have a person check anything uncertain before confirming it.
Effort:
Medium
Time saved:
Often 50–70% less typing per order; measure it in a pilot
The problem today
Business customers still send orders by email, PDF or a photo of a handwritten list, and someone retypes every line into the system. Mistakes in codes or quantities lead to wrong deliveries and returns. Large distributors and manufacturers already use AI to read incoming orders and enter them into their systems. For a Greek SME the affordable version is a simple flow that reads the email, prepares a draft order and leaves the final check to your team.
How it works, in plain words
Think of a careful data-entry clerk who copies each order line into a standard form and flags anything odd.
What goes in: order emails and attachments, such as PDFs, spreadsheets or photos of handwritten lists, plus your catalog and a list matching customer product names to your codes.
What the AI does: it reads each order, pulls out customer, product codes, quantities, prices and delivery date into a fixed template, and matches every item against your own catalog instead of guessing.
What a person checks: a person reviews every draft order with a low-confidence match, an unknown product or an unusual quantity, and confirms it before it goes live.
What comes out: a draft order in your ERP or e-shop, plus a short list of questions to send back to the customer where something is unclear.
It does not approve credit, change prices or confirm orders on its own.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A 40-person food wholesaler in Thessaloniki
Restaurants and mini markets send orders by email and as phone photos of handwritten lists, and two people retype them every morning. The wholesaler would build a matching list of the names customers use for each product and let AI create draft orders in the ERP. It would aim to cut retyping time, as an estimate to check in a pilot, and track wrong deliveries caused by entry errors.
A B2B cleaning supplies e-shop in Chalkida
Hotels and offices email PDF orders with their own product names and codes. The shop would extract each PDF into a draft order on its e-shop back end and send unmatched lines to a person for review. A realistic first target could be most order lines matched automatically, with the remaining lines flagged rather than guessed.
A promotional merchandise agency in Marousi
Corporate clients email orders for branded mugs, bags and T-shirts with sizes, colours and logo files scattered across long threads. The agency would let AI pull every item, size and quantity into one order sheet and flag missing logo files or delivery addresses. It would measure how often orders need a second round of questions to the client.
How to do it, step by step
Gather 50–100 recent order emails with their attachments. Note the common formats, the usual mistakes and which customers send the most orders.
Define the fields you need: customer, delivery address, product code, description, quantity, unit, price and requested date. This becomes the fixed template the AI fills in.
Build a product matching list with your own codes next to the names and codes your customers use for the same items. The AI matches against this list instead of guessing.
Set up the flow with Make, Zapier or Power Automate: a new email arrives, AI extracts the fields, and the result goes into your ERP or e-shop as a draft order, or into a spreadsheet if there is no connection yet.
Flag uncertain lines for review, such as unknown codes, unusual quantities or missing prices. A person confirms them before the order is released. The same checks apply to invoice data extraction.
Track errors and time per order for 4–6 weeks before you remove any manual check.
Tools to consider
Microsoft Power Automate with AI Builder: reads emails and attachments in Outlook and extracts order fields. Caveat: connecting to a Greek ERP may need a partner or the ERP's own connector.
Make or Zapier: link your mailbox, an AI model and your e-shop or spreadsheet without code. Caveat: orders with many lines and exceptions get hard to maintain in visual flows.
ChatGPT or Claude: test extraction quickly on sample orders before you build anything. Caveat: copy-paste does not scale, and you need a business plan with suitable data terms.
Data protection and GDPR
Order emails are mostly business data, but they also contain names, phone numbers and addresses of contact people, and sometimes of end customers for delivery. Send the AI only what the order needs and do not connect whole mailboxes to AI tools. Use business plans with a data processing agreement and settings that keep your data out of model training. Keep a log of what the AI extracted and what a person changed, so you can trace errors. If you process consumer orders at scale, check with your DPO or legal advisor.
Mistakes to avoid
Letting AI guess product codes instead of matching them to your own catalog.
Sending orders straight to confirmation with no check on unusual quantities or prices.
Building the flow on a few neat sample emails and missing the messy real ones.