Automate your workflow with Scry AI Solutions
If your Accounts Payable system still leans on OCR, you’re not automating, you’re firefighting with outdated tech that silently bleeds time, money, and trust. Optical Character Recognition (OCR) was first introduced in the late 1920s to digitize printed text, a purpose it still serves today. But much has changed since then, and document formats have become more complex. AI tech is deeply integrated into today’s automation tools, enhancing their precision and accuracy. Nonetheless, many companies continue to rely on outdated financial and accounting systems, thinking their OCR software alone will be enough. They believe it’s an inexpensive means to achieve full automation and eliminate paper-based back office processes.
Well, not quite.
Despite automation, invoice processing remains largely manual, consuming a considerable amount of the finance team’s time due to persistent OCR errors. Invoice errors can be costly to resolve and reduce productivity and profit margins.
OCR technology in Accounts Payable solutions comes with hidden risks and can end up costing more than it saves over time. In this blog, we’ll explore the inefficiencies of traditional OCR systems and how AI-powered solutions are redefining AP automation.
OCR in accounts payable captures invoice data, but its claimed capabilities are often overstated. It’s a form of unsophisticated automation, like a robot that only converts printed text and characters into machine-readable data. To operate, it uses zonal OCR or a rule-based OCR template configured with a sequence of if-then rules. These rules tell the software exactly where to look for specific fields in structured documents.
Simple OCR won’t work if your document lacks proper structure or has a modern invoice format. Handwritten, complex, or varied formats of documents also aren’t handled well.
Many traditional OCR engines struggle with document complexity and variability. Some systems rely on the font of the first character in a line to interpret the rest. This makes them blind to the changes in font size or formatting of the document. They often fail to accurately process both bordered and borderless tables, and cannot effectively handle unstructured or modern invoice layouts.
Additionally, OCR systems do not clean up visual noise, such as black gaps or stray characters, resulting in incomplete and error-prone outputs. They may misclassify key fields like subtotal vs. total, misinterpret currency symbols, and lack the localization awareness needed for global documents. Critically, they offer no semantic understanding or adaptive intelligence, making them inflexible in real-world use.
Template-based OCR systems, without AI enhancements, typically struggle with adaptive duplicate detection or context-aware error handling. It forces your skilled AP team into manual review for error-prone correction work. You must pair the traditional OCR with people to ensure quality, which adds time and cost. Even a single error can spiral into more significant problems, causing delayed approvals, vendor disputes, and accounting headaches.
As per the American Productivity and Quality Center (APQC), 0.8% to 2% of total disbursements processed each year by businesses are duplicate or erroneous. It might sound small, but if an organization makes $100 million in payments, that’s about $800,000 to $2 million in errors. Hence, that error rate in OCR systems isn’t just a technical flaw; it’s a costly financial risk.
OCR can provide invoice digitization, but its output is riddled with limitations and inefficiencies. These hidden gaps are often overlooked during AP planning, leading the business to severe operational and financial strain. Below is the breakdown of the four most critical cost areas caused by outdated, template-based OCR in AP:
To achieve the holy grail of quality, speed, and cost reduction, one must add intelligence to their automation system. This can be achieved via Intelligent Document Processing (IDP) systems that use AI-powered OCR enhanced with machine learning. With IDP, you get an adaptive and self-improving solution that understands the semantic meaning behind the group of characters. It not only reads and performs template guessing but also interprets it, learns from errors, and extracts data ready to match, validate, and route.
Let’s take a look at the key differences between Legacy OCR and AI-powered IDP:
Feature | Legacy OCR | AI-Powered IDP |
---|---|---|
Template Handling | Stuck with fixed templates, breaks if the layout changes. | It adapts to different formats, and no templates are needed. |
Data Capture | Just reads text, struggles with blurry or low-quality images. | Actually understands content using AI, making extraction smarter. |
Line-Item Accuracy | Fails to accurately extract multi-line or nested table data. | Extracts structured data with laser precision. |
Context Awareness | Lacks contextual understanding of the data. | Knows the difference between a date, a due date, and a PO date. |
Handling Errors | Every issue needs manual fixing. | Flags actual errors automatically, saving you time. |
Learning & Improvement | Never gets better; the same mistakes happen over and over. | Learns from corrections and improves continuously. |
Fraud & Duplicate Check | Either weak or completely useless. | Spots duplicates, mismatched values, and fraud risks. |
Scalability | Stuck on-prem, scaling becomes tedious work. | Cloud-based, secure, and scales effortlessly. |
Straight-through processing happens when an invoice from a supplier directly goes into your ERP platform for payment without manual intervention. It requires data capturing, matching, coding, validation, and payment approval, all without a human touch. As the term implies, ‘straight-through processing’ accelerates the invoice and payment cycle. But is straight-through processing even possible?
Yes and no.
With an AI-based IDP that performs enhanced OCR via machine learning, yes. With simple automation technology without occasional human oversight, no.
You see, STP heavily relies on OCR tech and its accuracy. Traditional OCR on the market today, in any case, has an explicit error rate. What’s even more problematic is that there’s no connectivity. If your supplier sends an invoice with an error or that is incomplete, the system won’t detect or interpret it. It won’t automatically send or notify the supplier, “You sent an incorrect invoice.” Your AP team has to manually fix those errors and then request the vendor for the correct ones, extending the invoice life cycle.
IDP leverages AI technology to accurately capture data, perform the matching logic, and flag errors or exceptions. It ensures that defined business rules are consistently applied. Additionally, it maintains the supplier network and keeps vendor master data accurate. So, if there’s an error in the invoice, it will link back to previous data patterns and can rectify them accordingly. The system will also notify the vendor and the accounts payable team automatically. The invoice OCR and advanced reconciliation predict and populate line numbers, even when the supplier hasn’t provided them. This ensures straight-through processing and helps cut down on tedious, time-consuming tasks while driving cost savings and business value.
Automation is only effective when the system’s accuracy is reliable, and accuracy is the pinnacle of straight-through processing. AI-powered IDP does that without forcing human review and provides touchless processing at scale.
A global provider of legal, tax, and regulatory solutions faced growing challenges in reconciling vendor invoices against complex service agreements. With multiple suppliers operating under varied contractual terms, manual OCR-based validation introduced frequent billing discrepancies, approval delays, compliance risks, and vendor friction.
To address this, the organization implemented AI-powered contract intelligence to automate invoice validation. The system utilized advanced OCR and deep learning models to extract key contractual elements. Key data fields such as pricing structures, SLAs, penalties, and billing terms were automatically matched against incoming invoices.
Discrepancies like overbilling, duplicate charges, missed discounts, and non-compliant fees were flagged in real time. Integration with existing financial and contract management platforms enabled a seamless, end-to-end reconciliation workflow.
By replacing rigid OCR workflows with adaptive, AI-driven reconciliation, the organization scaled its invoice processing capabilities while ensuring contractual compliance.
Legacy OCR may have served as a stepping stone, but it’s certainly not built for continuous change, real-time data, or modern compliance needs. As the business scales, so does its AP process and the pressure to digitize. An incomplete or outdated tech stack can stall progress for years, throwing your AP team into the ancient loom of inefficiency. Therefore, to move from fragmented workflows to efficiency, your AP system needs true intelligence, i.e., AI.
Below are what you must prioritize:
Rule-Based Automation + Straight-Through Processing Enablement | Flags exceptions, auto-routes approvals, and clears compliant invoices instantly without human assistance. |
Feedback Loop or Continuous Learning via Machine Learning | The system improves with every correction, reducing future errors. |
Cloud-Native Architecture | Ensures scalability, real-time access, and faster deployment. |
ERP-Ready APIs | It can allow seamless integration with your existing SAP, Workday, Oracle, and other Financial Software. |
Security & Compliance Ready | Built-in controls for audit logs, fraud flags, and policy alignment. |
Invoice Format Agnostic | Handles PDFs, images, XMLs, e-invoices, and multi-language documents. |
If the answer to these questions isn’t a clear yes, you are effectively investing in software that introduces new errors.
The only reliable math in reducing the OCR error is the number seven — in other words, achieving meaningful accuracy improvements requires custom-built solutions that can run into seven figures.
Scry AI provides a more sustainable solution. Our Advanced AP Solution, powered by intelligent document processing, uses AI to handle unstructured, variable invoice formats with greater precision, achieving up to 99% accuracy. It integrates easily into existing systems, learns from data over time, and enables straight-through processing with minimal manual intervention. Most importantly, it delivers this capability without the high cost or complexity traditionally associated with enterprise-grade OCR.
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The parties agree that the United Nations Convention on Contracts for the International Sale of Goods will not govern these Terms of Service or the rights and obligations of the parties under these Terms of Service.
If any provision of these Terms of Service is illegal or unenforceable under applicable law, the remainder of the provision will be amended to achieve as closely as possible the effect of the original term and all other provisions of these Terms of Service will continue in full force and effect.
These Terms of Service constitute the entire and only Terms of Service between the parties in relation to its subject matter and replaces and extinguishes all prior or simultaneous Terms of Services, undertakings, arrangements, understandings or statements of any nature made by the parties or any of them whether oral or written (and, if written, whether or not in draft form) with respect to such subject matter. Each of the parties acknowledges that they are not relying on any statements, warranties or representations given or made by any of them in relation to the subject matter of these Terms of Service, save those expressly set out in these Terms of Service, and that they shall have no rights or remedies with respect to such subject matter otherwise than under these Terms of Service save to the extent that they arise out of the fraud or fraudulent misrepresentation of another party. No variation of these Terms of Service shall be effective unless it is in writing and signed by or on behalf of Company.
No failure to exercise, and no delay in exercising, on the part of either party, any right or any power hereunder shall operate as a waiver thereof, nor shall any single or partial exercise of any right or power hereunder preclude further exercise of that or any other right hereunder.
We may provide any notice to you under these Terms of Service by: (i) sending a message to the email address you provide to us and consent to us using; or (ii) by posting to the Website. Notices sent by email will be effective when we send the email and notices we provide by posting will be effective upon posting. It is your responsibility to keep your email address current.
To give us notice under these Terms of Service, you must contact us as follows: (i) by personal delivery, overnight courier or registered or certified mail to Scry Analytics Inc. 2635 North 1st Street, Suite 200 San Jose, CA 95134, USA. We may update the address for notices to us by posting a notice on this Website. Notices provided by personal delivery will be effective immediately once personally received by an authorized representative of Company. Notices provided by overnight courier or registered or certified mail will be effective once received and where confirmation has been provided to evidence the receipt of the notice.
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