Office & managementVerified Real deployment at a named or precisely described company, with a measured result from a primary source.

Customer PO and quote emails → sales orders in the ERP (drafts or automated entry)

Added 2026-10-11 · Last updated 2026-10-14

Role

Sales & customer service · Typical users: customer service rep

The problem

Customer purchase orders arrive by email as PDFs, spreadsheets or scans in every layout and language, and customer service re-keys each one into the ERP by hand.

Step by step

1. Collect a week of incoming customer POs and order emails, including the awkward ones (scans, other languages, long orders).
2. Have the AI extract header and line data into a fixed draft order format.
3. Check every extracted line against master data: customer number, part numbers, price list and the quote referenced.
4. Flag mismatches (unknown part, price difference, missing date) for the rep instead of letting the AI guess.
5. A customer service rep reviews the drafts each morning and enters or approves them in the ERP.
6. Track hours spent on order entry and the share of orders that needed no correction.
7. Before switching to automated entry, measure on 4+ weeks of orders: share of orders with no correction (touchless rate), field errors per order, and orders rejected by the ERP.

Tools

• Workist (automated order entry into the ERP): used at RUKO
• Other tools to evaluate: Esker, Conexiom, Rossum, and order-capture modules of your ERP
• Any LLM (ChatGPT, Claude, Copilot) with the prompt below for level 1 drafts

As reported by the source; most figures are vendor or self-reported and not independently verified.

Practitioner anecdote (not verified)

A practitioner on Reddit (r/manufacturing) describes level 1: Claude drafts sales orders from customer emails and the customer service reps check the drafts each morning. No figure is given; self-reported, not verified.

Human review: cautions

• Start at level 1 (drafts reviewed by a rep) and only move to automated entry once extraction is measured accurate on your own orders.
• Track your own error rate and touchless rate: RUKO's source gives time saved only.
• Price or part-number mismatches must go to a person, never be 'corrected' by the AI.
• Customer orders contain confidential pricing; use an approved enterprise tool.

You are a customer-service assistant preparing DRAFT sales orders.

Inputs: customer emails and attachment text [paste], price list [part, customer part no., price, UoM, currency], customer master [name, number, ship-to list, payment terms, VAT ID], referenced quote [paste].

For each email:
1. Classify: new PO / change / cancellation / quote request / question.
2. Extract to CSV: customer, PO number, line, customer part no., our part no. (only on an exact price-list match, else blank), qty, UoM, unit price, currency, requested date, ship-to, Incoterm, and where in the email or attachment you found it.
3. Validate: unknown part; UoM difference (each vs pack); price differs from list or quote (show both values, do not calculate differences); missing date; ship-to not in master; duplicate PO number; unusually high quantity.
4. Give each line a confidence (high / medium / low); anything not high or with a flag is 'REP DECISION'.
5. Draft a short reply in the email's language; never confirm a price or date that is not in the data.
Never guess a part number; write 'not found' when a value is missing.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.

Sources

• https://www.reddit.com/r/manufacturing/comments/1tdclhd/
• https://www.workist.com/en/success-stories/ruko