AI order entry: turn emailed purchase orders into orders you can fulfil

A customer emails, “Send the usual twelve boxes to the new site, same price as last month.” An experienced inside-sales representative knows that “usual” refers to a particular brand, that the manufacturer recently changed its pack size, that the new site belongs to another branch, and that last month's price was a temporary concession.

Typing the email into an ERP is the smallest part of this job. The commercial work is determining what the customer intends, what you can supply, what you should charge and which promise you can actually keep.

AI transformation for a distributor should make that entire path more reliable: request received → product and unit resolved → customer price checked → stock and delivery confirmed → approved order created → fulfillment completed → appropriate repeat purchase followed up. The objective is profitable orders with less customer effort and fewer avoidable mistakes.

This is a proposed operating design for distribution leaders, not a report of a Looqus deployment. The numerical example is fictional. Product capabilities below come from primary documentation reviewed on 14 September 2026; vendor claims about outcomes are not treated as independent evidence.

Identify the actual bottleneck

Separate the work into three classes before buying anything. First is transcription: the request already contains valid customer references, part numbers and quantities. Second is interpretation: the buyer sends descriptions, photographs, shorthand or a previous order. Third is commercial resolution: the item is unavailable, the price differs, the delivery site is unclear or the customer needs an alternative.

A business dominated by clean purchase orders may obtain most of the value from document automation. A business handling complex material lists needs strong catalog matching and representative review. A business whose orders are already entered quickly but repeatedly stall on backorders needs supply coordination and customer follow-through.

Measure elapsed time and active work separately. An order may take six minutes to enter and two days to wait for a missing detail. Eliminating four minutes of typing will not solve a two-day response delay. The operating design must identify the blocker, assign a person able to resolve it and track the request until it reaches a meaningful finish.

The available products overlap, but start in different places

Conexiom describes a sequence from emailed document capture through validation and normalization to ERP entry; its knowledge base explains its trade-document transformation role. Canals documents rep-reviewed drafts, catalog suggestions, branch availability and customer delivery details. These are meaningful capabilities, not generic text extraction. Conexiom solutions, Conexiom platform documentation, Canals order entry.

Canals' onboarding documentation also provides concrete intake routes, including forwarding emails and, where supported by the organization, Outlook linking. Check the real intake path instead of assuming that every mailbox and attachment format is automatically connected. Canals: Getting started.

Microsoft's current overview documents a quote-first intermediate step and required review of outgoing messages. That design illustrates an important distinction: AI can do extensive preparation while commercial commitments remain controlled. Do not describe every such product as an unattended order-taking bot. Microsoft: Sales Order Agent.

The buyer's task is to run representative transactions through the proposed product and service combination. A logo on an integration page does not prove compatibility with your custom pricing extension, your branch rules or the version you actually run.

Work through the twelve-box order

Consider fictional Meridian Industrial Supply, which sells maintenance consumables. A recognized buyer at customer C-104 sends an order referring to “the usual twelve boxes of M8 bolts,” adds six tubes of threadlocker and asks for delivery to a new site on Friday.

The customer historically bought a 50-piece box of bolt SKU B50. Its replacement SKU B100 contains 100 pieces. The catalog includes both an 8.8-grade and a stainless version. The new-site address exists as an approved ship-to, but it is served by a different warehouse. The previous month's invoice also included a promotion that has expired.

The system should produce a resolution record, not silently choose the most plausible interpretation.

Assume the buyer confirms 600 bolts: six new boxes of 100. The current customer price is $18 per new box. Six threadlocker tubes are $9 each. Product revenue is 6 × $18 + 6 × $9 = $162, before any applicable taxes.

Bolt cost is six boxes at $11, or $66. Threadlocker cost is six tubes at $5, or $30. Product gross profit is therefore $162 − $96 = $66. With $12 variable pick-and-delivery cost, contribution is $54 before office labor and fixed overhead.

If the system interprets twelve boxes using the new pack, it doubles the bolts without authorization. If it carries forward an expired $15 box price, it removes 6 × $3 = $18 from the agreed-order economics. Neither failure is solved by recognizing the text perfectly.

The warehouse now has only four new boxes available. Another branch can transfer two before the cutoff. That transfer costs an additional $8, reducing contribution to $46. If the only way to meet Friday is a $25 emergency shipment instead of the $8 transfer, contribution would fall to $29. If both costs apply, it would fall to $21. The representative needs an explicit choice: approve the expense, agree a delivery charge, split delivery with consent or offer a later date.

The system should return the options and their consequences. It should not invent a promise, downgrade the bolt grade or absorb an unapproved freight charge to make the task appear complete.

Product matches need evidence; units need arithmetic

An AI model can be useful at interpreting a buyer's shorthand and finding candidate products. Final acceptance should combine that interpretation with explicit business checks: manufacturer reference, dimensions, grade, finish, pack quantity and the customer's permitted substitutes.

Keep the original requested quantity beside the normalized quantity. For example: “customer confirmed six BOX100; base quantity 600 EA.” This makes the conversion visible to the reviewer, warehouse and customer. Microsoft documents item-level alternate units with a quantity per base unit, which is the kind of structured record the conversion should use. Microsoft: Item units of measure.

Do not accept a single model confidence score as authorization. A high-confidence match on a common brand name can still have the wrong technical specification. Define which fields must match exactly, which permit a controlled alias and which require a person to decide.

Learned corrections should also have a scope. If customer C-104 uses “blue carton” for a specific item, that alias should not become the meaning for every customer. Store who confirmed it, which catalog version applied and when the mapping should be reviewed.

Price and stock must come from the commercial system

Customer pricing may depend on item, unit, quantity, validity period and agreement. Microsoft documents these dimensions in Business Central's special-price logic. That is why a language model's recollection of the last invoice is insufficient. Let the approved pricing engine calculate the price for the resolved transaction. Microsoft: Sales prices and discounts.

Show the effective price source, agreement date and reason for any difference from the customer's request. A stale agreement becomes an exception for the pricing owner. The model can draft an explanation, but the business decides whether to honor an expired price.

Likewise, on-hand inventory is not automatically stock you can promise. Other orders, warehouse location, scheduled receipts and the requested delivery date matter. Business Central distinguishes available-to-promise from capable-to-promise calculations, which can include replenishment considerations. Confirm how your own ERP calculates these values before exposing them to customers. Microsoft: Order promising dates.

Refresh price and availability immediately before the approved order is committed. If either has changed since review, return the changed fields for a new decision. This is especially important during a busy morning when multiple representatives are selling the same remaining stock.

The implementation architecture

Keep the ERP as the commercial record and add a traceable layer around the unstructured request. The architecture should have an intake record, a proposed order, validated business facts, a resolution queue and a confirmed transaction receipt.

A duplicate-control key can combine the customer, external purchase-order reference and accepted revision. It must accommodate legitimate amendments. A resend of the same attachment should not create another order, while a changed quantity on an existing order must not disappear as “already processed.”

If the ERP request times out, check whether the order exists before retrying. Store the resulting ERP identifier and reconcile the lines. Technical success is not the same as commercial accuracy: a created order with the wrong destination is still a failure.

Protect customer boundaries in retrieval. One customer's price, ship-to address or document should never be used to answer another customer merely because the product matches. Limit access to the records needed for the current task and retain the decision trail for authorized reviewers.

Repeat purchase is part of the outcome

An order desk often has valuable demand signals but no time to act on them. A customer usually replenishes a consumable every five weeks. A quote was accepted for some lines but the rest waited for stock. A new delivery site may need associated items once its initial order arrives.

Use those signals to create relevant follow-up work, not automatic pressure to buy. Check actual shipment, returns, open complaints, stock and the customer's communication preferences. A backordered item arriving is a concrete reason to contact the buyer; a generic “checking in” message has much less value.

Record whether the follow-up produced a reply, a confirmed order, a deferred requirement or an explicit decline. A drafted message is not a recovered sale. Reorder suggestions need customer approval unless a separate standing-order arrangement already authorizes them.

For Meridian, the follow-up might be to confirm the transferred boxes arrived, resolve any remaining requirement and record that the customer now orders in 100-piece boxes. That small update prevents the same ambiguity next month and makes the next transaction easier for both parties.

Build the business case without double-counting

Suppose Meridian handles 1,200 requests a month. In this fictional baseline, active handling takes 12 minutes per request. A pilot might test whether it can reduce the average to seven minutes across the full request mix, including exceptions. The potential capacity released is 1,200 × 5 ÷ 60 = 100 hours per month.

At a modeled loaded cost of $25 per hour, that is $2,500 of capacity value. It is not automatically $2,500 of cash savings. If payroll stays unchanged, the benefit depends on how those hours are redeployed.

Suppose representatives use some of those same hours to win 20 additional orders contributing $45 each after variable fulfillment. That is $900 of additional contribution before service costs and any extra labor. Do not count those working hours simultaneously as eliminated payroll. Present a capacity case and a growth case with their respective assumptions.

Measure errors as well as speed: wrong item, wrong unit, price exception, duplicate order, changed promise and avoidable return. Track both frequency and financial effect. A few expensive mistakes can outweigh many fast clean orders. Compare similar customers and product mixes, and keep difficult requests in the denominator.

A pilot acceptance sheet

Select one branch, one product family and a representative set of customers. Begin by replaying historical requests, then run assisted drafts before permitting tightly defined automatic actions. The following is a proposed acceptance artifact, not a universal deployment standard.

Set explicit acceptance thresholds from the cost of an error in your catalog. A low-value replenishment line and a specification-critical industrial component should not share an automatic-approval policy simply because both arrive by email.

What Looqus would own

AI does the work. We own the outcome. A proposed Looqus engagement would own the operating path around a defined order outcome: connect the intake and business records, implement the chosen tools, build and work the exception queue, coordinate approved customer clarifications, verify the ERP result and measure repeat-purchase follow-through.

Specialists take the 90% job to 100% by resolving the ambiguous pack, finding the right pricing owner, obtaining the delivery decision and confirming that the accepted order is actually created. Your team retains agreed authority over pricing, credit, substitutions and commitments. Integration feasibility and responsibility boundaries should be established before an engagement begins.

If requests are arriving but too much commercial work remains stuck between the inbox and the warehouse, book a call with Looqus about your order desk. Describe one recurring order problem and who currently has to resolve it. No order files are needed for the first conversation. We'll discuss where the handoff breaks and whether Looqus could take responsibility for completing it.

Continue with the next decision

The distributor RFQ guide focuses on quotation controls. See the distribution examples for the wider sales journey.

Sources

Primary sources directly reviewed on 14 September 2026. Product descriptions establish documented scope, not independently verified outcomes or local implementation readiness.

1. Conexiom. Order solutions. Capture, validate, normalize and ERP workflow.
2. Conexiom Knowledge Base. The Conexiom platform. Trade-document transformation.
3. Canals. Distributor order entry. Inputs, catalog suggestions, branch information and representative review.
4. Canals Help. How to get started using Canals. Intake and email setup.
5. Microsoft Learn. Sales Order Agent overview. Updated 23 July 2026; quote-first design and outbound review.
6. Microsoft Learn. Set up item units of measure. Base and alternate units.
7. Microsoft Learn. Record special sales prices and discounts. Customer, item, quantity, unit and effective-date logic.
8. Microsoft Learn. Calculate order promising dates. Supply and delivery calculations.

Work out the economics · Use a practical playbook