Accounts-payable work often spans data entry, validation, routing, exception handling, and payment-system hand-off. Automation can reduce repetitive work, but the business case depends on the organization's invoice mix, exception rate, controls, and existing systems. The architecture below is a starting point for that assessment.
Step 1: Unified invoice intake
The first problem to solve is getting invoices into a controlled processing queue regardless of how they arrive. Inputs may include email, supplier portals, EDI, KSeF, or public-procurement channels. Choose intake paths from the legal scope and supplier landscape, then normalize only the formats that are actually present before pushing approved data into Salesforce.
Poland uses distinct systems and obligations. KSeF is the national VAT e-invoicing system: mandatory issuance began in phases on 1 February and 1 April 2026, while qualifying smallest taxpayers remain deferred until 1 January 2027; receiving through KSeF generally became mandatory from 1 February 2026, subject to statutory exceptions. PEF supports structured documents for Polish public procurement and operates in the Peppol network. Peppol is an interoperability network, not a synonym for KSeF and not, by itself, the source of every Polish B2G obligation. Confirm current scope and exceptions with official guidance before implementation.
Step 2: AI-powered extraction
Once an invoice lands in the intake queue, trigger a Salesforce Flow that calls an external AI extraction service (Scanforce, Azure Document Intelligence, or Google Document AI) via a Named Credential. The service returns a structured JSON payload: invoice number, vendor, amounts, line items, VAT details, payment terms. The Flow maps these fields onto a custom Invoice__c object or into standard Financial Services Cloud records if you're running FSC.
Map confidence scores to a field on the Invoice record. Low-confidence extractions (below your threshold) automatically set the Status to "Needs Review" — everything else proceeds to validation.
Step 3: Three-way matching
Three-way matching compares the invoice against the purchase order and goods receipt. In Salesforce, the workflow can look up the PO, compare agreed fields within organization-approved tolerances, and flag discrepancies. Auto-approval should occur only where finance, audit, and risk owners have approved the controls; exceptions should route to the accountable team.
Step 4: GL coding and cost allocation
Even with a PO match, the workflow may need to assign a general-ledger account, cost center, and project code. A classification model can propose coding from historical postings, but required training volume and suitability depend on the selected product and data quality. Surface predictions as suggestions until measured performance and controls justify further automation.
Step 5: Approval workflow
Build approval rules from the organization's delegation-of-authority policy rather than generic amount bands. Salesforce approval tooling, Flow, or an industry-specific orchestration layer can route the record. Optional Slack or Teams notifications require a separately designed integration, and escalation timing should follow the approved operating policy.
Step 6: ERP write-back and payment
Salesforce is rarely the system of record for payments — SAP, Oracle, or Microsoft Dynamics is. Once an invoice is approved in Salesforce, trigger an outbound integration (Platform Event → MuleSoft flow, or a direct REST/OData call) to create a vendor invoice in the ERP. Pass the Salesforce Invoice ID as an external reference so the two records stay linked. Payment confirmation from the ERP flows back to Salesforce to close the loop: update the Invoice status to "Paid" and record the payment date and bank reference.
Step 7: Analytics and exception monitoring
Build a Salesforce dashboard with invoices by status, processing time, exception rate by vendor, early-payment discount capture, and duplicate-warning outcomes. Define targets from a measured baseline and finance-approved control policy. The dashboard then provides evidence for whether the workflow is improving rather than assuming a universal ROI.
- Measure implementation and operating cost against the current manual baseline
- Set exception-rate and processing-time targets from observed data
- Track early-payment discounts actually captured, not only offered
- Treat duplicate matching as a warning control with an owned review process
- Review false positives and false negatives before expanding automation