Artificial intelligence (AI) and machine learning (ML) bring significant efficiency gains to organizations and should play critical roles in any digital transformation efforts. One major use case for these technologies is AI document processing, also known as intelligent document processing (IDP).
Organizations amass a large number of business documents—from invoices in billing departments to medical coding documents at hospitals. This mountain of paper causes a number of issues: errors, delays in processing, and hours wasted on manual data entry, to name a few. AI document processing can recognize different types of documents and extract important information without manual data entry.
To understand how IDP can help streamline business processes, it’s important to know how it works. With this blog post, we’ll cover each step so you can understand what goes on behind the scenes.
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There are four distinct steps involved in IDP—ingesting the document, classifying the document, extracting data, and verification.
First, you need to get the document into an application that includes document AI features. This could mean importing digital documents received via email attachment, upload, or secure file transfer, or it could involve scanning a physical copy of the document. The system then uses optical character recognition (OCR) to recognize text and images in the document.
Once you’ve imported the document to an AI document reader, the next step in intelligent document processing is for the system to classify the document types. In other words, the software needs to understand the document layout and use document AI to understand what it's looking at, such as an invoice, medical form, or customer feedback form. Once it knows what it’s looking at, it then can start to figure out how to read the document.
At this point, the AI pulls data from the document and transforms it into a usable form. To understand document extraction, it helps to understand two data formats—structured and unstructured.
Structured data refers to data with a built-in structure that machines can read, such as a database table. For example, data could be formatted in a way that clearly outlines names and addresses their own database fields. Structured data is easy for software to use.
Unstructured data doesn’t use a machine-readable format. Information taken straight from a sheet of paper or a PDF would be considered unstructured. For example, an address on an invoice will simply appear as a string of letters and numbers. On its own, this doesn’t tell a computer what that information means.
The goal of this process is to pull information from the unstructured document and convert it to structured data. In other words, to translate the document into a machine-readable format that software can easily work with. For instance, IDP could read payment notifications or checks, update that person’s balance, and then notify the billing department if that person only sent in a partial payment.
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AI isn’t entirely hands-free. Artificial intelligence systems learn over time, using technologies including machine learning. The AI takes a well-educated guess at the type of document and which information to extract. A human must then verify whether the system was correct. This creates a feedback loop that teaches the system how to improve and make more correct decisions in the future. Over time, the amount of human intervention needed is greatly reduced and employees can focus instead on documents that may need special attention.
AI document processing can bring a number of benefits to your organization, including:
Most importantly, IDP makes an organization’s data more readily available for use in other applications or processes. For example, a retail business could use IDP to scan data from an order form, then immediately kick off an automated process of printing a shipping label, searching for inventory, and notifying warehouse staff to start fulfilling the order. That’s precisely why AI document processing is an essential tool for any organization looking to implement a more complete automation strategy.
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