Digital transformation tools like A.I. are becoming more critical to business leaders than ever. According to a recent survey from Deloitte and Northwestern University’s Kellogg School of Management, 90 percent of chief strategy officers say that advanced technologies are important strategic enablers.
While the interest and awareness of these tools is raising exponentially, companies are still at the beginning stages of implementing A.I. into integral operations like document processing. While documents across all departments are usually in digital formats (such as PDF, Excel or Word), the data within these docs must be read, processed and entered by humans. Large enterprises continue to have trouble processing documents and extracting the relevant information. Many still spend millions of dollars per year on manual processes, which is time-consuming and error-prone. What’s more, it isn’t scalable.
The same Deloitte survey concedes that only 34 percent of CSOs surveyed believe their companies are mature in onboarding AI and other advanced automation technologies like RPA. To take their business to the next level, organizations need to trade in such manual processes for low-touch or no-touch handling. Robotic process automation (RPA) has been used to automate document processing workflows piecemeal across business processes, but RPA in itself is not sufficient. That’s because a critical part of the process is reading all the data manually and entering it into systems of records.
A.I. has proven extremely useful in this situation. It helps extract data from documents with very high accuracy and convert this unstructured or semi-structured data into structured data that can then be validated and filed automatically through workflows. A.I. also enables the handling of different document formats without the need to have pre-specified templates, and it learns to improve continuously. A.I. has become a force multiplier in addressing the need for end-to-end automation. Enterprises are now witnessing very high data extraction accuracy, which was historically impossible with optical character recognition technology alone.
What is Intelligent Document Processing?
Traditional document processing solutions have tried to automate the extraction of data but have required operators to build templates. This approach worked like a patchwork, as it could handle documents of a similar format. The system would fail when a document of a different format from the same or a new vendor entered the system.
But intelligent document processing (IDP) solutions powered by A.I. can seamlessly extract and process data from a variety of documents in multiple formats. Such IDP products can do so by complimenting Optical Character Recognition (OCR) with A.I., which eliminates the painful template creation and management process. A.I. makes extraction seamless and guarantees high accuracy. A.I.-powered IDP automates the entire document processing cycle right from extraction to publishing the data into the record-keeping systems.
Combined A.I. Technologies Bring Transformation
Multiple A.I. technologies—including natural language processing (NLP), deep learning, computer vision and machine learning (ML)—can now power document-processing solutions. When combined with optical character recognition and workflows, they can transform an enterprise’s business processes.
Consider the following situation as an example: A large enterprise’s finance and accounting team automates AP invoice processing using an A.I.-powered intelligent document processing solution. Such a solution can read invoices arriving at an email alias from vendors, extract the data with high accuracy (up to 100 percent), validate the extracted data against business rules and file it automatically in the Enterprise Resource Planning (ERP) system. It can also detect and flag anomalies in the invoices and prevent fraud.
An A.I.-based IDP solution uses multiple A.I. technologies to extract relevant information from documents and images. Here’s how:
- After the OCR has read the document, computer vision recognizes entities and blocks of interest. This eliminates the need for working pre-specified formats.
- Machine learning examines all extracted data and identifies anomalies in the data to flag for human intervention.
- For document processing and comprehension, 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 computing approach that’s 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 – only much faster. Complimenting NLP with fuzzy logic supports decision making, improves system performance and contributes to enhancements in efficiency across the business processes.
These various aspects of AI help digitize the ingestion, auto-categorization and extraction of data, in combination with business rules for validating all the extracted data before it can enter a system of record. A.I.-powered IDP also needs to have a workflow engine to ensure that this data can be automatically entered into the systems through a sequence of steps. A.I., while helping digitize document processing workflows end to end across the enterprise, will play a key role in the digital transformation of enterprises.
A Welcome Change
The future for business digital transformation is bright. A recent KPMG survey found that 48 percent of respondents plan to onboard A.I.-enhanced RPA in their organizations in the next two years. When reviewing IDP solutions, make sure that they contain actual A.I., as there is a lot of “A.I. washing” going on today as vendors try to capitalize on the trend without truly having an A.I.-driven product. Don’t assume anything; fully vet all options to find the one that works to improve existing business processes but that doesn’t upset the entire operational apple cart. This aspect of digital transformation, done well, will release untold employee hours for greater productivity in pursuit of business goals rather than business processes.
Akhil Sahai is Chief Product Officer of Kanverse.ai.