Impact of AI on Business Document Processing Workflows

0
4


Digital transformation tools like AI are becoming more essential than ever for business leaders. According to a recent poll From Deloitte and Northwestern University’s Kellogg School of Management, 90% of strategy directors say advanced technologies are important strategic enablers.

As the interest and awareness of these tools grows exponentially, companies are still in the early stages of implementing AI in integral operations such as document processing. Although documents from all departments are generally in digital format (such as PDF, Excel, or Word), the data contained in these documents must be read, processed and entered by humans. Large companies continue to struggle to process documents and extract relevant information. Many still spend millions of dollars a year on manual processes, which is time consuming and error prone. In addition, it is not scalable.

The same Deloitte poll admits that only 34 percent of CSOs respondents believe their companies are mature in integrating AI and other advanced automation technologies such as RPA. To take their business to the next level, organizations must trade these manual processes for low or non-contact manipulation. Robotic Process Automation (RPA) has been used to automate piecemeal document processing workflows in business processes, but RPA by itself is not enough. This is because an essential part of the process is to read all the data manually and enter it into record systems.

AI has proven to be extremely useful in this situation. It extracts data from documents with great precision and converts this unstructured or semi-structured data into structured data which can then be validated and archived automatically via workflows. AI also makes it possible to manage different document formats without the need for pre-specified templates, and it learns to improve continuously. AI has become a force multiplier to meet the need for end-to-end automation. Businesses now benefit from very high precision in data extraction, which was historically impossible with optical character recognition technology alone.

What is intelligent document processing?

Traditional document processing solutions have attempted to automate data extraction, but have forced operators to create models. This approach worked like a patchwork, as it could handle documents of a similar format. The system failed when a document of a different format from the same vendor or a new vendor entered the system.

But intelligent document processing (AI-powered IDP solutions can seamlessly extract and process data from a variety of documents in multiple formats. These IDP products can do this by supplementing optical character recognition (OCR) with AI, which eliminates the painful process of creating and managing models. The AI ​​makes the extraction transparent and guarantees high precision. The AI-powered IDP automates the entire document processing cycle, from data extraction to publication in archiving systems.

Combined AI technologies bring transformation

Multiple AI technologies, including natural language processing (NLP), deep learning, computer vision and machine learning (ML) – can now power document processing solutions. Together with optical character recognition and workflows, they can transform a company’s business processes.

Consider the following situation as an example: The finance and accounting team of a large company automates the processing of AP invoices using an intelligent AI-based document processing solution. Such a solution can read invoices arriving at a supplier email alias, extract the data with high accuracy (up to 100%), validate the extracted data against business rules and automatically classify it in the ERP (Enterprise Resource Planning). It can also detect and report anomalies in invoices and prevent fraud.

An AI-based The IDP solution uses multiple AI technologies to extract relevant information from documents and images. Here’s how:

  • Once the OCR has read the document, computer vision recognizes the entities and blocks of interest. This eliminates the need to work on predefined formats.
  • Machine learning examines all extracted data and identifies anomalies in the data to signal human intervention.
  • For processing and understanding documents, Natural Language Processing (NLP) technology is an ideal option. NLP helps understand the semantics of the extracted text, validates it against a dictionary, and supports multiple languages.
  • Fuzzy logic (a computational approach based on degrees of truth as opposed to the usual ‘true or false’ Boolean logic on which the modern computer is based) can mimic the way human operators make decisions – but much faster . Supplementing NLP with fuzzy logic facilitates decision making, improves system performance, and helps improve business process efficiency.

These different aspects of AI help digitize the ingestion, self-categorization and extraction of data, in combination with business rules to validate all data extracted before it can enter a data system. recording. AI-powered IDPs must also have a workflow engine to ensure that this data can be automatically entered into systems through a sequence of steps. AI, while helping to digitize end-to-end document processing workflows across the enterprise, will play a key role in the digital transformation of businesses.

A welcome change

The future of digital business transformation is bright. A recent KPMG survey found that 48% of respondents plan to integrate AI-enhanced RPA into their organization within the next two years. When looking at IDP solutions, make sure they contain real AI, as there is a lot of “AI wash” is happening today as vendors try to capitalize on the trend without really having an AI-based product. Don’t assume anything; Thoroughly examine all options to find one that works to improve existing business processes but doesn’t disrupt the entire operational Apple Cart. This aspect of digital transformation, done right, will free up untold hours of work for greater productivity in the pursuit of business goals rather than business processes.

Akhil Sahai is Director of Products at Kanverse.ai.



Source link