Implementing a more efficient system for handling vendor invoices is crucial for organizations looking to streamline their accounting processes and reduce errors associated with manual data entry.
Accounts payable tasks make up about half of all accounting work, and for real estate firms and property managers, property accounting adds another layer of complexity. Invoice capture—or invoice data entry into a system of record—remains a time-consuming, paper-centric, manual process for many.
With labor bridging the gap between a vendor invoice and more modern AP approval workflow tools, causing payment bottlenecks and staff burnout. AP teams are increasingly exploring technology to automate invoice capture, with AI gaining ground on more commonly used OCR solutions.
While traditional Optical Character Recognition (OCR) solutions have been a popular choice in the past, AI-enabled invoice capture solutions offer significant advantages that make them a more suitable option for modern businesses.
Here are several reasons why AI-enabled invoice capture is a better choice than traditional OCR solutions.
Traditional OCR technology converts printed or handwritten text into machine-encoded text. While OCR can extract text from documents, it often struggles with accuracy, especially when dealing with varied invoice formats, poor-quality scans, or complex table structures. OCR systems can misinterpret characters, leading to errors in data capture.
To make the most of OCR, templates are often required—usually created by the AP team—to ensure the right information is being read on the invoice and captured into the system of record. When an invoice deviates from standard templates, accuracy is severely impacted, resulting in unusable data.
In contrast, AI-enabled invoice capture solutions leverage machine learning algorithms and natural language processing (NLP) to understand and extract data more accurately from invoices, regardless of format or quality.
AI systems can learn from historical data and improve their accuracy over time, adapting to different vendors’ invoice layouts and handling diverse fonts, styles, and document types with greater precision.
OCR systems are typically limited to recognizing and extracting text without understanding the context. This means that OCR might capture the wrong information or miss critical data if the layout changes slightly or if the text is ambiguous.
AI-enabled solutions, however, provide contextual understanding. They can comprehend the relationships between different data fields, such as recognizing that a sequence of numbers corresponds to an invoice number, a date, or an amount based on its context within the document.
This contextual awareness allows AI systems to extract relevant information more accurately and reliably, reducing the need for manual verification and correction.
The critical difference for AP work is not being limited to information displayed on the invoice. Vendors don't always use the correct property codes, relevant remittance address, or purchase order numbers. To ensure payments go to the correct vendor, from the correct GL account, and the right entity, additional invoice coding is required by the AP team.
This coding requires prior knowledge and context that OCR can neither see nor supply—but AI trained on historical data can.
As businesses grow, so does the complexity and volume of invoices they need to process. Traditional OCR systems often require extensive reconfiguration and customization to accommodate new invoice formats or changes in vendor documentation. This can be time-consuming and costly, especially for businesses dealing with numerous vendors and international invoices.
AI-enabled invoice capture solutions are inherently more scalable and adaptable. They can handle a wide range of invoice formats without needing manual intervention, thanks to their ability to learn and adapt from new data.
When growing a portfolio and onboarding new properties, there's a lot of rethinking of processes and transferring of institutional knowledge, which slows down operations while things ramp up. OCR can only help with what has been explicitly mapped out and anticipated. The benefit of AI is the capacity to keep learning beyond what was explicitly programmed, and to develop and apply rules that evolve along with your portfolio.
This makes AI solutions more flexible and cost-effective for businesses with dynamic invoice processing needs.
AI-enabled solutions can seamlessly integrate with existing enterprise resource planning (ERP) and accounting systems, automating the entire invoice processing workflow. They can automatically validate extracted data against existing records, flag discrepancies for review, and update databases in real-time.
This level of integration minimizes manual intervention, reduces processing time, and ensures data consistency across platforms.
In contrast, traditional OCR systems may require additional middleware or manual processes to integrate with existing systems, limiting their efficiency gains and increasing the risk of errors during data transfer.
AI-enabled solutions often come with advanced security features and compliance capabilities that protect sensitive data. They can automatically detect and redact confidential information, ensure data encryption, and maintain audit trails for compliance with industry regulations such as GDPR and HIPAA.
While some OCR systems offer basic security features, they may lack the advanced capabilities needed to ensure compliance with stringent data protection regulations, exposing businesses to potential risks and liabilities.
Initially, AI-enabled solutions may seem more expensive than traditional OCR systems, but they offer significant cost savings in the long run. By reducing errors, automating manual processes, and improving data accuracy, businesses can save on labor costs, minimize financial penalties due to data inaccuracies, and optimize their overall invoice processing operations.
AI-enabled invoice capture solutions provide a superior alternative to traditional OCR systems by offering greater accuracy, contextual understanding, scalability, integration capabilities, security, and cost efficiency.
By leveraging AI technology, businesses can transform their invoice processing workflows, reduce errors, and enhance productivity, ultimately leading to better financial management and business outcomes.