Reduce TCO of paper-based processes with paperless document automation

Ashutosh Saitwal
Ashutosh Saitwal

Founder CEO - KlearStack AI

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Extract Data from Unstructured Invoices with KlearStack

Save 80% cost with 99% data accuracy in invoice processing! 

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In the previous blog, we established a firm foundation for a paperless office in the form of invoice automation. Taking it forward, this post talks about the costs associated with paperless document automation. Since every organization has 20% structured and 80% unstructured data with vendor invoices forming a non-trivial part of the latter, reducing costs related to invoice processing is our primary goal.

The vendor invoice processing method we discussed in the previous article was entirely paperless and it brings its own costs along. In fact, the three other approaches – manual data entry, traditional OCR solutions, and template-based OCR solutions don’t even come close to the AI-based invoice processing technique in terms of TCO (Total Cost of Ownership).

Also, when it comes to managing the accounts payable (AP) processes, organizations look for the cheapest available solutions, at the time of buying decision. So, what do you think is the most affordable yet efficient means to handle AP processes – manual data entry, traditional OCR solutions, template-based OCR solutions, or AI-based intelligent document processing (IDP)?

Well, don’t stress out! We are here to help you with a fact-based comparison of all the four document processing techniques so that you can decide which is the best paperless document automation solution for your organization.

Manual Document Processing

Manual document processing involves a human to receive the document, manually enter the document data into internal systems/Excel sheets, process the document, send it for approval, and complete the desired action.

For instance, if we consider invoice processing, there’s no invoice reader or software that can convert invoice data automatically into Excel sheets for calculation. The AP department staff has to manually perform all the operations. So, we calculated the cost incurred in the entire process and even referred to few studies made by others. The average cost of processing came out to be $12 per invoice.

Not only that, manual invoice processing led to 32% late payments and 36% vendor calls/emails to remind the company of due payments.

Now, if you multiply the cost per invoice and the delay in payments to the number of invoices you process each month (maybe hundreds, thousands, or millions), you can get an estimate of the total monthly expense in invoice processing. Just don’t freak out for the time being.

Traditional OCRs for Document Processing

When OCR was first introduced to kick-start paperless document automation, it was not traditional as it did reduce the manual data entry tasks. OCR is a data extraction software which reads text from any image (scanned or photographed) automatically while reducing human intervention to an extent.

But that was not all. Traditional OCRs were unable to interpret the data they extract and also lacked the capability to store it in an organized manner. They could just scan the image and extract everything on the document only to feed a cluttered information in the internal systems.

In invoice processing, traditional OCRs could convert PDF invoice to Excel but failed to interpret the type of data and where it needs to be stored for easy processing. As a result, the AP staff had to manually interpret the extracted data and make edits to turn the clutter into analyzable data.

Hence, the costs were never reduced. Instead, the cost of setting up OCRs and again manually interpreting the extracted data added to the total expense. Invoice processing was never fully-automated with traditional OCRs.

Template-based OCR for Document Processing

As mentioned, 80% of an organization’s data is unstructured and it’s hectic to structure it manually or using traditional OCRs. Hence, the innovators came up with a template-based data extraction tools. In this, the user needs to define a template for each unique layout of document. These template-based tools are integrated them with traditional OCRs. Consequently, the OCR could now extract the document data based on the template structure and feed into the internal systems for further processing. The image/ PDF invoice data extraction process was going great for a while.

But here’s the rub. There’s no fixed structure for any document – even those of the same type. Let’s take the case of invoices. Every vendor has a different format for invoices. The fields in the invoice also vary with the type of purchase made. That said, it’s impossible to provide one template each for every invoice format.

Due to variability of the documents, template-based OCR could only automate invoice processing up to 40% and save 20% of the costs. The invoices that did not follow the already fed template were processed incorrectly and the AP staff was back on the job again, rectifying the mistakes and validating the extracted data for authenticity.

Template-based OCRs failed to interpret the data and were unable to understand the context of a field’s data. Instead they focused on the positioning of the fields in the invoice and made mistakes when the field positions were different from that of the template. This again required human intervention for correcting the mistakes.

Was affordable invoice automation still a far cry?

AI-enabled Document Processing

AI text recognition/ document interpretation became the first end-to-end automated document processing technique with the ability to interpret document data irrespective of the documents’ layout and text. It can understand the context of the data and eliminate the need for templates.

AI-enabled document processing is actually an integration of template-less OCR solutions and AI-based intelligent data interpretation. It combines machine learning along with natural language processing and computer vision to understand the context of data before and after OCR extraction and store it in internal systems with an added advantage of document decision support. That said, your unstructured documents can be mined into actionable insights with AI-based document processing.

So, if you use AI-enabled IDP SaaS solutions, you are actually boosting your business productivity up to 200% and reducing TCO up to 300%. What’s more? You can expect 90-95% of accuracy within 90 days of implementing it. Moreover, you are free from any template maintenance costs, hardware costs, and up-front license costs.

Given all the four scenarios of document processing, it is clear that manual processing can never be a cheap and efficient approach. Also, paperless document automation is very difficult with manual document processing.

Hence, we are left with OCR-based solutions with AI-enabled intelligence integrated with it. The fourth model, AI-based document processing, is indeed the best way to avail cost efficient yet productive paperless document automation.

Paperless Document Automation : KlearStack Way

KlearStack is an AI enabled data extraction and interpretation software with a perfect fusion of template-less, end-to-end automated, document decision support. It is a comprehensive solution to paperless document automation with an ability to reduce document processing costs while increasing the productivity of the business.

KlearStack uses AI text recognition along with ML and NLP techniques to interpret the document data and provides actionable insights after feeding it to the internal systems. Hence, if you have made up your mind for a paperless document automation solution with low TCO, KlearStack is your go-to solution.

Book a free consultation call to know more about how KlearStack can help you achieve paperless document automation.

Stay tuned for our next blogs on starting a paperless office.


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