Beta Digital
AICase study · Anonymised
NLC
National Logistics & Transport Client
AI Project · Logistics & Transport · National

We used SAP Document AI to turn invoice PDFs into validated S/4HANA postings — starting with a controlled live foundation and a roadmap to scale.

★ Key result
End-to-End
End-to-end Document AI invoice pipeline delivered live
★ Key result
Error Handling
Exception handling route for invoices needing human review
Result
AI-enabled
OCR and structured field extraction from supplier PDFs

The challenge

A national logistics and transport business was processing large volumes of supplier invoices manually through its accounts payable function, with staff keying information from PDF attachments into SAP S/4HANA. The process was time-consuming, exposed the business to data-entry errors, and was difficult to scale across diverse supplier invoice formats.

The opportunity was to introduce intelligent automation without attempting an uncontrolled big-bang rollout. The business needed a solution that could validate extraction accuracy, prove business rules, manage exceptions and build confidence with finance stakeholders before expanding the automation footprint.

What we did

Beta Digital designed and delivered an automated invoice processing solution using SAP Business Technology Platform and SAP Document Information Extraction. The programme was structured around a three-phase roadmap, with Phase 1 focused on proving an end-to-end pipeline using real invoice samples and controlled production scope.

Supplier invoices are received through a monitored mailbox, where SAP Integration Suite picks up attachments and routes them to Document Information Extraction. The AI service performs OCR and extracts structured fields such as supplier details, line items, amounts, tax codes and purchase order references.

The extracted data then passes through a validation layer that checks against purchase orders and vendor master data in S/4HANA. Clean matches can progress automatically, while exceptions are routed to a review queue for human intervention. The architecture separates ingestion, extraction, validation and posting so each stage can be refined as additional suppliers and document types come into scope.

What it delivered

Phase 1 delivered a working live foundation for AI-assisted invoice processing. The business gained a repeatable pattern for taking unstructured invoice PDFs, extracting structured data, validating it against SAP records, and managing exceptions through a controlled process.

The three-phase roadmap gives the organisation a practical route to scale: tune extraction accuracy, extend supplier coverage, add document types and progressively reduce manual processing effort across accounts payable.

By the numbers

The full measure of the engagement

5 metrics · 2 key results
End-to-EndEnd-to-end Document AI invoice pipeline delivered live
Error HandlingException handling route for invoices needing human review
AI-enabledOCR and structured field extraction from supplier PDFs
3 phasesStaged roadmap from controlled production scope to broader automation
Validation against purchase orders and vendor master data in S/4HANA
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