Will this replace our credit controller?

No. It removes preparation, matching and drafting. The judgement calls — who to press, what to concede, when to escalate — remain with the person who owns the relationship.

What if our customers all invoice through different portals?

That is common in Europe and it is exactly where pre-send validation pays: getting each customer's required references right first time avoids silent rejections.

Where should we start?

With the interval measurement. Until you know whether the delay is before or after invoice issue, any tool choice is a guess.

AI use case

AI in order-to-cash

Where AI genuinely helps across quote, order, invoice, collection and cash application — with complexity, data requirements, risks, KPIs and ROI considerations.

Miguel Torres, Founder, Merjora · Updated 4 September 2026

In short

In order-to-cash, AI is most useful at the points where the process meets unstructured input: reading customer purchase orders, classifying inbound accounts-payable correspondence, matching aggregated remittances to open items, and drafting collection correspondence for human approval. The decisions — credit limits, escalation, write-offs — stay with people. The measurable effects are faster invoice issue, fewer disputes caused by reference errors, and less manual cash application.

The workflow today

Quotation → order entry → delivery or service completion → invoicing → customer AP intake → dispute handling → collection → cash application → reconciliation.

Where AI helps

Extracting purchase-order references, line items and delivery terms from customer PDFs and emails
Validating that an invoice carries everything the customer's AP system requires before it is sent
Classifying inbound AP mail into remittance, query, dispute and rejection, and routing it the same day
Matching lump-sum remittances to open invoices, including partial payments and deductions
Summarising an account's history so a collector opens the call already informed
Drafting pre-due and escalation messages in the customer's language for a person to approve

Common use cases

Purchase-order intake and validation
Invoice pre-send quality check
Dispute classification and root-cause tagging
Cash application and remittance matching
Collection correspondence drafting

Expected benefits

Shorter interval between delivery and correct invoice issue
Lower dispute rate from reference and pricing errors
Less manual matching in cash application
Collectors spend time on conversations rather than preparation
Root-cause tagging turns disputes into upstream fixes

Implementation complexity

Moderate. The AI steps are well-understood; the effort is in integration with the ERP or accounting system and in agreeing who approves what. Cash application is the most sensitive because it writes to the ledger.

Data requirements

Historical customer purchase orders in their real formats
Open-item ledger with invoice numbers, amounts and due dates
Remittance advices, including the messy aggregated ones
A labelled sample of past disputes with their causes
Verified AP contacts and intake channels per customer

Risks and controls

Writing an incorrect match to the ledger — require confirmation above a value threshold
Automated chasing damaging a strategic relationship; exclude named accounts explicitly
Extraction errors propagating into invoices, which is worse than a delay
Personal data in correspondence being processed without a documented basis

Example workflow

Customer purchase order arrives by email
Fields extracted and checked against the quotation and master data
Discrepancies above tolerance routed to order management; the rest confirmed automatically
Invoice assembled with all customer-required references and validated before sending
Inbound AP replies classified; disputes tagged with a cause and assigned
Remittances matched to open items, with low-confidence matches queued for review

KPIs to track

Days sales outstanding, split into pre-invoice and post-invoice intervals
Share of invoices issued within one day of completion
Dispute rate and average dispute resolution time
Cash application auto-match rate
Collector hours per million euros of receivables

ROI considerations

Value the cash release separately from the recurring efficiency saving
One day of DSO is worth about one day of average credit sales in released cash
Dispute prevention usually outperforms dispute handling on value per euro invested
Count reviewer time in the running cost; it typically exceeds inference cost

Frequently asked questions

Will this replace our credit controller?: No. It removes preparation, matching and drafting. The judgement calls — who to press, what to concede, when to escalate — remain with the person who owns the relationship.
What if our customers all invoice through different portals?: That is common in Europe and it is exactly where pre-send validation pays: getting each customer's required references right first time avoids silent rejections.
Where should we start?: With the interval measurement. Until you know whether the delay is before or after invoice issue, any tool choice is a guess.

Could this apply to your business?

Merjora quantifies what this workflow costs you today and what changing it is realistically worth.

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