How AI Agents Work in Accounts Receivable Automation — And Why It's Better Than OCR Alone

There was a time when OCR was seen as the best modern workaround for reading mounds of documents and manually entering data into software systems.

In recent years, there has been a major shift from basic automation tools like optical character recognition, or OCR, which actually dates back to the 1970s, to more advanced systems known as AI agents.

While OCR can read, extract, and clean up text from digital documents, AI agents go several steps further by ingesting large amounts of data, interpreting it, making decisions based on the information they’re given, and adapting as they learn.

In this blog post, we’ll explore how AI agents work, how they differ from traditional OCR tools, and why this evolution matters for the future of accounts receivable automation.

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Key Takeaways:

• Beyond automating tasks, AI agents can reason, plan, and adapt based on the data they receive.
• OCR is useful for reading, extracting, and cleaning text, but its capabilities stop there. AI agents can use OCR, but also go further by validating, acting on, and learning from the information they process.
• The quality of data fed into AI systems directly impacts their effectiveness.
• When used in areas like accounts receivable, AI agents can streamline workflows without requiring major system overhauls.‍

What is an AI agent?

When someone talks about AI agents, you may be inclined to imagine robots running around and working side by side among people in the workplace.  

That may sound far-fetched, but it’s actually not far from reality.  

To be sure, AI agents are software systems that can perform specific tasks without the help of a real person and use the information they gather to change the way future work is carried out.

AI agents learn by gathering data from databases or the API connections that allow information to seamlessly flow from one software tool to another, along with other information from their surroundings, such as text, images, and audio files.  

All of this information is then run through all of the algorithms, rules, and programs that govern an AI agent’s actions to reason, plan, and determine the best course of action to take.

More specifically, language learning models — commonly known as LLMs — enable AI agents to understand complex instructions, come up with a game plan, and execute it.

But here’s the catch: AI is only as good as the data it has in context. If an AI model is fed inaccurate, incomplete, or biased data through LLMs, an AI agent may take actions that are undesirable or ineffective.

With that said, it’s important to ensure that any dataset which may be fed to an AI model — such as customer, invoice, contract, and general ledger details in an ERP system — is accurate, free of duplicates, and up to date.  

Perhaps one of the most attractive benefits of AI agents is the fact that they can learn and make better decisions over time by identifying changes in patterns or relationships. By doing this, AI models are designed to make better predictions or classifications in the future.  

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‍As an example, Fazeshift’s AI agents can automatically

• Turn contracts into invoices or subscriptions
• Match incoming payments to outstanding invoices
• Leverage customer details
• Manage payment communications with customers‍
• Handle payment disputes from customers‍
• Compare contract details to billing information
• Leverage submitted information from a self-serve payment portal to update billing information
• Use customer onboarding form data to fill out billing details in an ERP

Since AI agents are used to automate specific tasks and create seamless workflows between software tools, it’s easier to implement AI in accounts receivable without overhauling your current systems.

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‍How’s an AI agent different from OCR technology?

In practice, OCR — an abbreviation for optical character recognition — is used to read text from image or document files and then extract it. Software solutions often leverage this extracted data to fill out information that’s stored within a software system.

For example, you could use OCR to read a PDF copy of a contract that’s attached to an email and use that extracted information to fill out billing details in your ERP.

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‍Here’s a short, step-by-step breakdown of how OCR works:

• Image acquisition: A digital image of a document is converted into binary data — or unique combinations of 0s and 1s — and analyzed. Dark areas are recognized as potential text, whereas lighter areas around it are classified as background.  ‍
• Preprocessing: The digital image is cleaned and errors are removed to facilitate the reading process. This process can involve adjusting the image alignment, removing image spots, smoothing out lines on potential text, and identifying matches for handwritten and typed text.
• ‍Text recognition: Individual characters are identified and assembled into words and sentences. The recognized text is then compiled in a machine-readable format.‍
• Post-processing: The processed text is analyzed and reviewed for spelling mistakes and grammatical errors.      

Unlike AI agents that are able to ingest information, process it, create a plan, execute it, and leverage past experiences to inform future decisions, OCR’s capabilities are static and confined to reading documents, extracting text, and converting that data into a machine-readable format.

OCR’s recognition rate — the metric used to measure accuracy — can also vary when it needs to read copies of handwritten documents. The number 7, for instance, could be read as the letter T.

To be fair, OCR still has value. In truth, an optimal recognition rate is at least 95 percent.

There’s a good chance that AI agents still use OCR to read and extract text from a document, but it’s not the only one they use to carry out a task, review their work, and validate it.  

Case in point: An AI agent may use OCR to read and extract text from an invoice but then lean on additional processes to clean up the data and double check it before producing the final result.

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Lean on AI agents for real accounts receivable automation

As organizations look to streamline operations and make smarter decisions, the move from static tools like OCR to dynamic AI agents represents a major leap forward in what automation can achieve.

OCR has long been a useful tool for digitizing and extracting information from documents, and it still plays a valuable role in modern workflows. But compared to AI agents, OCR is limited in scope.

AI agents can not only extract data — with OCR sometimes as a first step — but also interpret it, act on it, and learn from the outcomes. This makes them far more versatile, intelligent, and capable of driving continuous improvement across your systems and processes.

Need to find the best accounts receivable automation software on the market and improve accounts receivable turnover with AI?

‍Schedule a call with Fazeshift and see how AI agents can work for your team so they can tackle more mission-critical tasks.