A major issue is that standard VIM setups lack a unified dashboard to track why OCR is failing at a supplier level. While systems like the Information Extraction Service (IES) use machine learning to adapt, administrators struggle to see which specific vendor layouts routinely trigger errors without custom analytics reporting, which is a large task to undertake.
For organizations that have invested heavily in OpenText Vendor Invoice Management (VIM) for SAP Solutions, this visibility gap can be a major pain point in Accounts Payable processes. VIM drives straight-through, touchless processing, but when invoices consistently fall into exception handling, AP teams are called upon to bridge the gap with their experience.
When an invoice fails data extraction, it routes to an Invoice Validator agent SAP Fiori Capture Validation Inbox or Windows Validation Client for manual validation. If a high-volume supplier subtly changes their invoice format, moves the Purchase Order number, or uses a font that confuses the Optical Character Recognition (OCR) engine, the system flags it. The AP clerk corrects it, processes the invoice, and moves on. But what happens when this occurs a hundred times a month for the same supplier?
Without supplier-level analytics, AP managers cannot easily quantify this effort. The overall “touchless rate” drops, cost-per-invoice metrics rise, and AP staff become trapped in repetitive data entry—the exact manual work VIM was implemented to reduce. More manual touches create more risk.
Modern OpenText extraction engines—whether you are using the Information Extraction Service (IES), Core Capture for SAP Solutions, or legacy Business Center Capture (BCC)—are incredibly powerful. They leverage advanced machine learning, knowledge bases, and feedback loops to “learn” how to read complex documents over time.
However, AI is only as good as the input it receives. Sometimes the root cause of an extraction failure isn’t the learning algorithm; it is bad input. A vendor might be sending low-resolution scanned PDFs, using unpredictable dynamic tables, or submitting invoices where information might overlap or be unreadable on paper. Without a dashboard highlighting that Vendor X has a 45% extraction failure rate due to unreadable tax fields, SAP administrators and AP leads remain unaware of the bigger picture. They cannot intervene because they don’t know who the consistent offenders are.
To maximize the value of OpenText VIM and achieve the automation rates that make a difference, organizations must bridge this analytics gap.
While standard VIM Analytics (VIMA) provides excellent visibility into invoice lifecycles and workflow bottlenecks, tracking OCR failures requires a bit more focus. Dashboards which allow you to pull out the top 10 offenders when it comes to OCR failures are huge time and resource savers.
Visibility is only the first step. Ensure your AP team is correctly training the system. When correcting an invoice in the validation screen, users must utilize point-and-click or single-click entry to map the correct fields, feeding that layout data back to IES. If clerks are manually typing the data into SAP instead of using the capture validation tools, the machine learning engine is starved of the feedback it needs to adapt.
Implementing VIM for SAP is not a “set it and forget it” project; it requires a strategy of continuous improvement. By acknowledging the blind spots in standard OCR reporting and taking steps to illuminate supplier-level data, organizations can support their AP departments.
Stop treating the symptoms of extraction failures one invoice at a time. By shining a light on vendor-specific OCR data, you can address the root causes, continuously improve your machine learning models, and improve your touchless processing allowing your teams to focus on more valuable areas.
