As the volume of electronic information continues to rise, many organizations are looking for ways to reduce their spend on document management.
Widespread access of internet has led to an explosion in information production. The rate at which information is created is high compared to archiving. Majority of the time, this information that occurs in the form of documents is deleted due to lack of efficient solutions that can handle its volume in the most cost-effective manner. This imbalance in processing the volume of documents can be reduced with Intelligent Document Automation.
This is where Artificial Intelligence (AI) and Machine Learning (ML) come into play. This blog highlights how deploying AI-enabled solutions can automate high volume document archiving and turn archives into insightful data with reduced cost.
Obstacles in the way for Intelligent Document Archiving For many industries, specifically the Public Sector and Financial Services require heaps of documents to be processed as part of their operations and data management. These documents can be bills, insurance claims, legal and financial documents, personal and business documents, surveys, reports, etc. depending on the sector or industry that makes use of it. Despite the pledge to go digital, documents in the form of paper are here to stay for a long time. So, businesses have opted the middle way of turning physical documents to digital sources.
However, at this stage, where the documents are available digitally, the clear challenge is to process this massive pool of unstructured and uncategorized digital documents manually. Manual processing of documents is slow, error-prone, and causes operational inefficiencies. Adopting AI-powered document processing can help you achieve great results with minimum efforts and in a cost-effective way.
One of the major challenges to onboard an AI-driven solution for document archiving, which is common for every end-user is the integration to their operating IT ecosystem. In this case businesses should have a clear understanding about their IT infrastructure and opt for an expert AI vendor with proven use-cases.
Also, to learn about the AI in document archiving, we need to understand how it practically functions.
What does AI-powered automation in document archiving mean? In the field of document management or archiving, automation through AI has promising prospects. It is a step for businesses to improve their process capabilities and service offerings while controlling cost and efforts.
Documents used in business operations exist in the form of unstructured data or free text. Traditional document processing techniques are often not equipped to process free-text data. However, AI-powered Intelligent Document Processing uses Natural Language Processing to read, extract, and process data from structured as well as unstructured documents.
Both the technologies of NLP and OCR make it possible to extract relevant information by identifying the intentions behind the free-text, and key words, phrases, and other important data points in the form of entities. This information can be used further to gain insights based on advanced analytics.
Benefits of automation in document archiving For upgrading to automated document archiving process from the traditional process, one requires organizational dedication, multi-functional efforts, and flexible approach towards progressive change. However, implementing the right technologies and processes can bring notable perks for the businesses.
Some of the immediate takeaways of AI-powered document processing are,
Swift classification of each document type Easy to validate and access extracted data Infer valuable data insights Reduced data retrieval complexity Reduced average cost per document processed Scale up operational efficiencies Looking for AI document archiving solution? Simplifai automates your document archiving, minimizes filing errors, data loss, and improves document security with comprehensive Archiving solution
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What to look for in an AI automated document archiving solution ? With the help of Artificial Intelligence, companies can process archiving of documents, emails, letters, and reports in large volumes with minimal human assistance or interpretation.
An ideal AI-enabled document archiving solution when integrated in your existing processing workflow should,
process structured and unstructured (free text) be easy to use with no coding or technical skill have short implementation period handle various document types achieve highest accuracy and quality in data extraction be easy to integrate with the third-party systems using API/RPA Conventional document processing particularly document archival is one of the critical back-office functions. It is tedious and consumes a big chunk of revenue expenditure in the form of operating costs. To gain positive outcomes, businesses must consider automation of document processing that covers end-to-end workflow. This can be achieved by onboarding the right technologies and building the required foundation for automated document archival.
Adopting intelligent document automation will enable businesses to innovate their workflow processes and make their way towards ambitious digital goals.
References https://dl.acm.org/doi/full/10.1145/3479010#d1e480 https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/best-in-class-digital-document-processing-a-payer-perspective https://aws.amazon.com/machine-learning/ml-use-cases/document-processing/
About Simplifai Simplifai is an AI solutions provider that enables businesses to process and archive large volumes of documents in the form of Intelligent Document Automation through our Documentbot.
Our Documentbot is a powerful AI-powered document archiving solution that can process various types of documents from different sources such as email attachments, email uploads, cloud storage, and system folders. It can streamline document handling for businesses and reduce the processing time from hours to minutes, thus helping them save time, effort, and resources spent on manual document processing.
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