AI Fundamentals

Intelligent Document Processing: Beyond OCR and RPA

Kognitos

Generative AI and Intelligent Document Processing

Key Takeaways

Generative AI and document processing centers on a clear limitation: OCR paired with RPA works well for standardized, high-volume documents but breaks down elsewhere. This post explains that OCR struggles with handwriting, non-standard fonts, poor images, and complex tables, while RPA operates “bad data in, bad data out” and turns brittle when documents vary or arrive incomplete. Generative AI overcomes these limits by understanding context, adapting to new inputs, and reasoning about relationships across the wider business process, even handling incomplete or inconsistent data. It illustrates with examples: a hospital processing complex medical charts, an international conglomerate matching payments to invoices across subsidiaries and languages, and insurers handling claims. Critically, the claims processor, not a developer, teaches the system how to handle exceptions. Combining generative AI with OCR streamlines document automation, reduces reliance on developers, and lowers the cost of automation.

Many businesses leverage OCR + RPA to save time and reduce errors associated with manual data entry. For example, a manufacturing company that processes thousands of invoices per month that are highly standardized and do not vary often, can use OCR technology to extract invoice data automatically. Today some forms of OCR are becoming available to business users through no-code templates and pre-trained models, but most require some developer expertise. These technologies are a good first step, but unfortunately only cover a small portion of the total documents used by businesses.

Limitations of Current Approaches

Despite its strengths, OCR and RPA both have limitations that restrict the ability to process complex documents. OCR technology has limited ability to recognize unstructured text like handwriting, non-standard fonts, and poor image quality. Furthermore, OCR technology has limited accuracy rates in understanding the context and extracting information from the wider business process. It is particularly challenging to extract information from complex documents that include tables, graphs, and other visual elements. OCR technology also struggles to process documents with incomplete information, and it is difficult to catch and handle errors using OCR technology alone.

RPA functions much in a “Bad Data In, Bad Data Out” style. When fed bad or incomplete data by OCR, RPA bots frequently break and are described as “Brittle”. Additionally, RPA is best when used for processes that do not have lots of exceptions. Complexity or variations in documents can create exceptions that cause RPA bots to break.

OCR + RPA is a great tool for standardized processes, but struggle with different document types. These limitations of OCR technology and other traditional approaches to document processing are holding back businesses in today’s fast-paced market. Businesses need accurate and efficient document processing to make informed decisions, streamline workflows, and maintain a competitive edge. Incomplete data, inconsistent data, and errors in document processing can cause businesses to lose money and damage their reputation.

How can Generative AI overcome the above limitations?

Generative AI like ChatGPT, or GPT4 is a game-changer for document processing. It uses advanced deep learning algorithms to analyze large volumes of data and identify patterns, making it highly accurate and efficient in document processing. Generative AI can learn from diverse examples and adapt to new data inputs over time, making it highly effective in processing complex documents with tables, graphs, and other visual elements. It can also recognize and understand the context of the wider business process, making it highly effective in handling incomplete or inconsistent data.

For example, a hospital can use Generative AI to process medical charts, which contain complex data structures such as tables, diagrams, and graphs. By using Generative AI, the hospital can extract critical information from the medical charts, such as patient diagnoses, medications, and treatments, with a high degree of accuracy and speed.

In another example, Kognitos worked with an international conglomerate that needed to match payments with invoices from different subsidiaries across the globe. The complexity of the process and the variety of documents and languages necessitated a tool that could understand context, and financial professionals in a shared services center to directly control the automation, not developers. By combining Generative AI with OCR in a Generative AI Automation platform, the conglomerate could greatly reduce the number of people manually processing data with high degrees of accuracy.

Generative AI can also help businesses handle cases when documents have incomplete data or when OCR has extracted incorrect information. It can recognize the context of the wider business process, understand the relationships between different pieces of information, and use this information to extract the correct data. For example, an insurance company can use it to process claims that contain incomplete or inconsistent data. The claims processor rather than a developer, is in control, and can teach Generative AI Automation how to handle situations with incomplete data, and how to find that data in future documents. An example of how a business user can teach Generative AI automation how to handle a document with a simple command can be found here: Generative AI + OCR

In conclusion, traditional approaches to document processing such as manual data entry and OCR technology helped take the first step, but have significant limitations that can hold back businesses. Generative AI is a game-changer for document processing, providing accurate and efficient processing of complex documents. By implementing Generative AI Automation for their document processing needs, businesses can streamline their workflows, decrease errors, and reduce the need for developers, greatly lowering the cost of automation and the ROI of projects. This gives companies a competitive edge in today’s fast-paced market. The time to adopt Generative AI for document processing is now.

How to Apply Generative AI to Document Processing

Define the document types and extraction targets for each process. Different document types require different extraction fields: invoices need vendor, amount, line items, and PO reference; contracts need parties, dates, obligations, and termination clauses; claims need claimant, coverage, and incident details. Define extraction targets by document type before deployment.
Test generative AI extraction accuracy against your specific document population. Generative AI document processing performance varies significantly by document type and format variety. Test on a representative sample of at least 100 real documents from your actual document population before making a deployment decision.
Configure validation rules that catch common extraction errors. Generative AI extraction is not 100% accurate. Configure validation rules for each critical field: amount ranges, date format checks, required field presence, and cross-field consistency. Validation rules catch errors before they reach downstream systems.
Design the exception handling workflow for documents that fail validation. Every document processing deployment has exceptions. Design the exception handling workflow before go-live: who reviews each exception type, what information they need, and what action each resolution triggers. Undefined exceptions create processing backlogs.
Measure generative AI extraction accuracy separately from overall touchless rate. Overall touchless rate combines extraction accuracy with validation and downstream posting. Measure extraction accuracy separately to identify whether exceptions are caused by extraction errors or downstream system mismatches. Separate metrics enable targeted improvement.

Frequently Asked Questions

Intelligent document processing with generative AI refers to the use of advanced deep learning models, such as those powering ChatGPT or GPT-4, to automatically extract, interpret, and act on information from business documents. Unlike traditional OCR, generative AI can understand the context of the wider business process, not just recognize characters on a page. It can handle unstructured data, complex layouts with tables and graphs, and documents that contain incomplete or inconsistent information. This makes it far more capable than earlier approaches for real-world enterprise document workflows.

Generative AI overcomes OCR and RPA limitations by using deep learning algorithms that analyze large volumes of data to identify patterns and understand context. Traditional OCR struggles with non-standard fonts, handwriting, poor image quality, and complex visual elements like tables and graphs, while RPA bots are brittle and break when fed bad or incomplete data. Generative AI can adapt to new data inputs over time and recognize relationships between different pieces of information to extract the correct data even when inputs are imperfect. Business users, not just developers, can teach the system how to handle exceptions by giving it simple natural-language instructions.

The main benefits include significantly higher accuracy when processing complex and variable documents, reduced reliance on manual data entry, and lower dependence on developer resources for automation. Generative AI can streamline workflows and decrease errors caused by incomplete or inconsistent data that would otherwise cause traditional RPA bots to fail. Businesses can lower the overall cost of automation and achieve a stronger ROI on automation projects. These capabilities give companies a competitive edge by enabling faster and more reliable processing at scale.

Traditional OCR combined with RPA works well for highly standardized, repetitive processes but struggles with document variety, exceptions, and unstructured content. OCR cannot reliably interpret handwriting, non-standard layouts, or documents with incomplete information, and when it outputs bad data, RPA bots break in what is sometimes described as a bad data in, bad data out failure mode. Generative AI, by contrast, understands context and can reason about the wider business process to handle exceptions gracefully. This means generative AI can cover a far broader range of real-world document types that OCR and RPA cannot reliably automate.

Kognitos worked with an international conglomerate that needed to match payments with invoices from different subsidiaries across the globe in multiple languages. The complexity and variety of the documents required a system that could understand context and allow financial professionals in a shared services center to control the automation directly, without relying on developers. By combining generative AI with OCR in a single automation platform, the conglomerate greatly reduced the number of people manually processing data while maintaining high accuracy. A separate example involves hospitals using generative AI to extract diagnoses, medications, and treatments from complex medical charts with tables and diagrams.

Businesses should evaluate whether the platform allows non-developer business users to directly control and teach the automation, since this dramatically reduces implementation cost and maintenance burden. It is important to assess how the system handles exceptions and incomplete data, as this is where traditional OCR and RPA fail most often. Organizations should also look for platforms that combine generative AI with OCR so they get the benefits of both character recognition and contextual understanding. Finally, businesses should consider the ROI by estimating how much manual processing labor the system can replace and how quickly it can adapt to new document types or process changes.