Document automation has undergone a dramatic transformation in recent years. As a company working at the intersection of document processing and artificial intelligence, we have witnessed firsthand how the approach to automating document processing has fundamentally changed in the last two years.
In this post, we explore how document automation has evolved from resource-heavy OCR projects to flexible AI solutions. We explain the pitfalls of older methods, show how large language models overcome real-world document variations, and highlight why now is the best time to adopt modern approaches.
The Traditional Approach: Resource-Intensive and Inflexible
In the past, automating document processing was a resource-intensive endeavor that often led to disappointment. Many organizations found that OCR projects were more complex than initially anticipated, especially when dealing with real-world document variations. These projects typically required collecting large amounts of training data for specific document types, carefully labeling them, and training specialized models for each document variation.
What looked promising in controlled test environments quickly unraveled in day-to-day operations. Documents arrived with coffee stains, stamps obscuring text, handwritten annotations, or poor scan quality. When suppliers changed their document layouts or new suppliers joined, the entire process had to restart – collecting new samples, retraining models, and validating results. Traditional OCR solutions struggled with these variations, leading to far more manual verification than planned.
These challenges made automation projects slow, expensive, and frustrating. A single project could take months before showing any value, and even then, the solution remained brittle – working well only for the specific documents it was trained on. Many companies ended up maintaining multiple extraction models, one for each major supplier or document variant, transforming what should have been an efficiency gain into a maintenance burden.
Real-World Document Challenges
Let’s look at a real example of a delivery note from a laboratory supplies company to illustrate the kind of challenges that would stump traditional OCR systems:
- A barcode sticker partially overlapping the text in the header section
- Handwritten notes and stamps in the goods receipt section
- Special handling instructions for temperature-sensitive items (“ship on dry ice”)
- A complex layout mixing tabular data with shipping instructions
Both printed and handwritten checkmarks for verification
In the past, each of these elements would have required separate specialized handling. The barcode overlay would have confused text recognition, handwriting would have been ignored, and special instructions might have been missed entirely.
The Modern Approach: Intelligent and Adaptable
Today’s document automation landscape has fundamentally transformed. Advances in generative AI and large language models have revolutionized how to approach document processing. Rather than developing specialized models for each document type, we can now leverage powerful pre-trained models that can process document context and structure effectively.
Large Language Models handle document variations with remarkable flexibility. They recognize that a “ship on dry ice” instruction is crucial regardless of its location, and process handwritten verification data as effectively as printed text. They are able to connect related information, e.g., treating “ship to” addresses, “delivery addresses” and “consignee” fields as equivalent, regardless of formatting or placement. For laboratory documents, the models automatically capture industry-critical data like lot numbers and expiration dates, streamlining compliance requirements.
The sophistication of large language models eliminates the need for extensive training data collection or lengthy proof-of-concept phases. With built-in processing of document structures and standard field types for logistics documents, production use can often start immediately. At ExB, we consistently achieve field-level accuracies of 85-95% on standard logistics documents from day one, with opportunities to reach even higher accuracy through document-specific modifications that require minimal effort.
endless possibilities.
ExB is an Intelligent Document Processing platform that transforms unstructured data from any type of document into structured results. Our AI-based software can not only extract all relevant information from your documents, but also understand them. This allows you to automate your processes and save both time & money, while improving your customer experience and employee satisfaction. Win-win.
Time to Restart Your Automation Journey
If you have previously attempted document automation projects that didn’t meet expectations, now is the perfect time to revisit this space. Today’s implementation process looks radically different. What used to take months of setup and training can now often be accomplished in a matter of minutes.
Getting started with modern document automation is refreshingly straightforward. The first step is to gather some sample documents that represent what you typically process. Include both the easy ones and those that have given you headaches in the past. For instance, if you are dealing with delivery notes, you might have some pristine PDFs from your major suppliers. However, don’t forget to include those troublesome scans or documents with handwritten notes all over them. Having a mix helps to understand real-world challenges.
Next, think about what information you need to extract from these documents. For a delivery note, you’re probably interested in the basics, like delivery dates and addresses, but there might be other critical information too. Do you need to capture special handling instructions? Are there quality control signatures that matter? What about reference numbers that need to match your ERP system? Taking a few minutes to jot down these requirements helps to focus on what matters to your business.
It’s also worth considering some of the tricky situations you’ve encountered. Perhaps you’ve dealt with delivery notes where someone stamped “URGENT” across important information or cases where crucial details were squeezed into margins. Maybe you receive documents in multiple languages or some with particularly complex line items. Examples of such edge cases help to understand the full scope of your needs.
Conclusions
By shifting from traditional OCR projects to modern, flexible AI solutions, companies can automate document processing in days rather than months, drastically reduce manual verification, and keep pace with shifting document formats. Don’t let past frustrations with OCR-based solutions hold you back—today’s AI approaches are faster, more accurate, and ready to handle your real-world document challenges.
The good news is that you don’t need to figure all of this out on your own. At ExB, we help you set up an initial extraction workflow where you can test your actual documents and see the results firsthand. Within minutes, you can experience what modern AI-powered document processing can achieve with your specific documents.
Ready to see what’s possible? Contact us to set up a practical test with your documents. We will help you configure the extraction to your needs and get you started right away.
