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Document processing: application of AI in companies

Every company processes a huge amount of documents every day. With the rise of artificial intelligence, powerful tools are now emerging to automate these time-consuming and traditionally labor-intensive processes.

Every company processes a huge volume of documents every day. Regardless of the size of the company, this results in high costs and efficiency losses. Thanks to the rapid development of artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA), powerful tools are now emerging to automate these time-consuming and traditionally labor-intensive processes.

Growing amount of data

The IDC estimates that around 175 zettabytes of data will be generated every year in 2025. One reason for this growth is the use of intelligent tools that use ML and AI to analyze data. The source of this amount of data is very often documents that are either newly created or digitized and processed from historical sources.(Source)

As the number of documents to be processed and the data they contain grows exponentially, manual processes for processing these documents lead to far-reaching problems in terms of costs, speed and susceptibility to errors.

Intelligent Document Processing (IDP) can provide a modern solution here.

More than OCR

While Optical Character Recognition (OCR) aims to recognize printed or handwritten text in digital images from scanned documents, IDP goes beyond this. IDP combines several technologies. OCR is part of the IDP toolbox. The information in documents is analyzed, structured data is extracted and automated decisions are made.

The use of AI breaks down the fixed rules according to which OCR recognizes letters and characters. Thanks to machine learning , text recognition capabilities are optimized by using large amounts of data for training. Neural networks or convolutional neural networks (CNNs), i.e. deep neural networks, can be used to capture hierarchical features and improve the accuracy of recognition. Recognition is also improved by the contextual capabilities of AI: compared to traditional systems, not only are the characters recognized, but their meaningfulness is checked by their context and any misrecognitions are corrected. AI also extends the recognition of unstructured data from tables, diagrams or images to other types of documents.

Further processing of the data / RPA

Unlike automated document processing, IDP does not end with reading and digitizing documents. The use of RPA and thus the integration of the information intelligently recognized by IDP in business processes leads to direct added value:

  • Direct cost savings when processing large volumes of data
  • Improved accuracy and fewer false positives
  • Fewer manual processes ensure greater end-to-end data processing
  • Greater process efficiency thanks to the end-to-end automation of processes that is now possible

The use of IDP is therefore an essential component of intelligent automation in companies. As a large proportion of relevant information is still created and distributed in documents, the use of IDP is an important lever for utilizing the benefits of true automation.

Use cases of IDP

IDP can offer added value in all business areas that require extensive document usage. Regardless of industry and sector, there are some horizontal use cases that are already frequently considered today:

In marketing , large quantities of media can be automatically categorized and managed in digital asset management. Context-sensitive metadata is added to the media without the need for time-consuming manual work.

Customer service can use the multitude of available documents and information much more efficiently in the processing of customer inquiries and support cases, thereby improving the customer experience and reducing costs at the same time.

The Human Resources department in particular processes applicant and employee data in a wide variety of formats and variants. IDP reduces the need for manual processing and recording of documents or types.

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