When Optical Character Recognition (OCR) made its appearance several years ago, it was considered a transformative technology. While one has to agree that OCR was useful at the time, the volumes of invoices that need to be processed these days are massive and make OCR technology obsolete.
Where OCR shines and where it stumbles
Before OCR entered the scene, invoice processing had been largely manual, with human intervention needed for typing invoice data. The process was slow, error-prone and expensive. OCR technology could read characters from a page and convert them into editable text. It was indeed a revolutionary and transformative technology at the time especially since invoices could be read at scale. However, invoice volumes have surged over the years. As per Billentis, the global market is forecast to encompass 550 billion voices annually and this number is expected to quadruple in size by 2035.
A point to be noted is that the actual reading of the characters in an invoice is just the first step in its processing. OCR does not really understand or infer what it has read. Another drawback is that any kind of format deviation in invoices is a potential point of failure for OCR and requires manual review and correction. Since invoices come from different vendors with varied layouts, standardisation is impossible.
Then comes the issue of unstructured content. Many of the invoices have content that lives outside of tidy fields. This information could be about changed terms or notes about partial deliveries or even amended charges explained in free text. OCR does not understand or read any of it.
Enter AI for invoice processing
The good news is that artificial intelligence (AI) can now read invoices almost like humans, but much faster! The technology has made a significant impact in the areas where OCR has visible drawbacks. AI extracts invoice data through contextual understanding and machine learning (ML), while OCR relies solely on pattern recognition to convert images to text. This means that AI can interpret context, infer the meaning and make decisions.
Traditional OCR systems typically require templates for each unique vendor format. This is because OCR relies on predefined templates with fixed coordinates and patterns. Defined templates tend to assure accurate capture, but this method does not support scale when diverse vendors are involved. AI, on the other hand, is able to generalise across vendor formats.
AI is trained on millions of diverse invoices to understand semantic context. So, it can identify what normal looks like and recognise any kind of deviation from this normal. Hence, duplicate invoices and price discrepancies, among other deviations, can be easily flagged.
The capabilities that AI brings
Processing at scale: Straight-through invoice processing (STP) refers to the fully-automated handling of an invoice right from its receipt to payment approval — all this without human intervention. While OCR laid the groundwork, AI takes it significantly further, making STP not just feasible but scalable across high invoice volumes. The result is faster processing cycles, meaningfully lower operational costs, and stronger supplier relationships built on timely, reliable payments.
Fraud and anomaly detection: Invoice fraud is a growing challenge and it has proven to be costly for organisations. Companies end up losing money due to duplicate invoices, inflated amounts and fictitious vendors. OCR-based systems have no capacity to detect any of it. They read what is on the page and pass it along. With AI’s ability to detect deviations from the normal, it can quickly point out invoices that have anomalous data and flag the same.
Continuous improvement: The most important benefit of AI is its ability to continuously learn and improve over time. Every change or correction made by a reviewer is noted and learnt by the AI system, ensuring that over time its accuracy improves.
Conclusion
According to market.us, the AI For Invoice Management market size is expected to be worth around USD 47.1 Bn by 2034, from USD 2.8 Bn in 2024, growing at a CAGR of 32.6% during the forecast period from 2025 to 2034.
AI in invoice processing frees up team time so that teams focus on higher-order accounting work rather than manual invoice processing. With all the benefits that it brings, the question really is not whether to use AI but how fast organisations can move to using AI for invoice processing.
How IBPM can help
Making informed decisions is essential for maintaining a competitive edge, and leveraging pertinent data is key to smarter choices. Many organisations grapple with siloed data sources, inconsistent reporting, protracted planning cycles misaligned with business operations, and an overemphasis on past data at the expense of forward-looking trends or scenarios. Infosys BPM’s financial reporting services, such as enterprise reporting, analysis, and planning (ERAP) and Tax Support Services, drive operational efficiencies and foster strategic business partnerships by automating mundane tasks, streamlining reporting and planning, and delivering timely, high-quality insights.
