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Accelerate your automation with process intelligence
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Accelerate your automation with process intelligence

Introduction:

Document AI: Enterprises today deal with plenty of AI document processing across various business units. They can be ranging from accounting to finance to human resources to sales and marketing. One of the significant issues with these documents is they are either Word documents, images, PDF’s or Excel documents and mostly, they contain data that is read manually by processing data entered by humans. Extracting and processing documents that have relevant information remains a problem with significant businesses. Today, organizations spend millions of dollars each year manually processing the data, which further increases costs, resulting in unwanted errors; it is both time-consuming and not scalable. What’s required now is no-touch or soft-touch handling.

As per reports by Gartner, by 2025, 50% of business-to-business invoices worldwide will be processed and paid without any manual intervention. Today, Robotic process automation (RPA) is introduced to automate document processing workflows across various business processes, but as many know, RPA is not sufficient. This is because of one particular step involving reading all data manually and entering it into records systems.

Intelligent Document Processing:

Any document AI containing critical information related to business and data for an enterprise from multiple channels and in various forms of PDF, images, Word and Excel documents can directly be part of attachments to emails. Traditional document processing solutions are known to have tried automating the extraction of required data for operators to build templates. This approach is more of a patchwork, as it can handle documents of a similar format. The possibility of system failure is always there when a document of a different design is always there when a new or same vendor entered the system. Modern-day document processing solutions are powered by the confluence of multiple A.I. technologies, including deep learning, natural language processing (NLP), machine learning, and computer vision. The combination of optical character recognition and workflows can transform the business processes in the enterprise.

Combined A.I. Technologies Bring Transformation:

A.I. technologies like deep learning, natural language processing (NLP), machine learning, and computer vision can now power more and more document-processing solutions. Combined with workflows and optical character recognition, they can eventually transform an enterprise’s business processes. A.I.-based IDP solutions generally use multiple A.I. technologies to extract relevant information from images and documents.

A.I. implementation at enterprise-scale requires:

  1. Classification of incoming documents and data extraction from documents with high accuracy without the help of any specialized A.I. experts works to an extent.
  2. It is all about the help of people in the loop when the A.I. model predictions don’t sound confident enough and using all the inputs from people to further improve the accuracy of extraction and classification.
  3. An all-time high-productivity user interface to validate and reconcile the extracted information using the A.I. model for operations.
  4. The ability to incorporate more document extraction into downstream or upstream of business processes is the ultimate.

Rigid automation to intelligent autonomous:

Instead of simply automating business processes, A.I. systems are poised to advance in the state of enterprise processes significantly. Today A.I. facilitates organizations to move beyond automation to build knowledgeable systems that respond quickly and autonomously to continuous change. This evolving field of AI-enabled process management is called autonomous business process, (ABP).

Centrepiece Of This Show is A.I.:

An intelligent document processing solution uses an amalgamation of A.I. technologies to extract relevant information from images and documents.

  1. Computer vision enables recognizing blocks of interest and entities after the OCR has successfully read the document. This further eliminates the need for working on pre-specified formats.
  2. Natural language processing (NLP) technology is perfect for comprehension and document processing. NLP helps to understand the semantics of an extracted text by further validating it against a dictionary by supporting multiple languages.
  3. Fuzzy Logic is known to mimic operators making real-time decisions much faster. When NLP and fuzzy logic support decision-making, this improves system performance and contributes to enhancements in the efficiency across the business processes operations.
  4. Machine learning diligently looks at all the extracted data and identifies anomalies from the outliers in the data to the flag for further human intervention.

Conclusion:

To conclude, Intelligent Document Processing (IDP) software transform semi-structured and unstructured information into more usable data. As known to all, business data is at the heart of digital transformation. Unfortunately, 80% of all business data is embedded in unstructured formats like emails, business documents, PDF documents and images. This implies, intelligent document processing can be referred to as the next generation of automation, which can extract, capture and process data from various other sources of document formats. It is known for using A.I. technologies like computer vision, natural language processing, machine learning, and deep learning to categorize, classify, validate the extracted data, and extract relevant information.

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