3 Ways AI Can Optimize Document Processing Workflows4 min readReading Time: 3 minutes
Most organizations today aggregate data from multiple sources and channels. What’s more, a vast majority of this data presents itself in different formats such as contracts, records, or paper applications. Data that manifests in these formats are typically unstructured and inaccessible for business operations.
Intelligent Document Processing leverages AI, along with programmable automation (such as repetitive tasks), to convert data from documents into usable, structured formats, and for consumption by downstream systems.
By intercepting AI into their processes, organizations can extract critical insights from unstructured documents and achieve efficiency and effectiveness gains.
Here are three ways businesses can implement Intelligent Document Processing:
1) Increase Throughput & Accuracy With AI-Powered Document Extraction
accuracy in data extracted with AI-powered Document Extraction
For most companies, documents are the foundation of how business gets done. Extracting and processing information from these documents involves operationally intensive processes. These processes rely on sizeable task forces to manually digitize, process, and match documents with specific requirements. With AI, organizations can automate the flow and integration of necessary information from digitized documents into existing flows/applications for downstream processes.
Blox AI uses OCR to extract key data from documents, configure them against a structured format, and seamlessly integrate them into in-house systems. This significantly increases the speed, accuracy, and throughput of document processing while also reducing costs.
2) Improve Efficiency Of Accounts Payable With AI-Powered Invoice Automation
Invoices and receipts are vital to the functioning of every organization. However, they come in different layouts that vary from vendor to vendor. This inconsistency in format makes it difficult for organizations to streamline their invoice processing pipeline, especially at scale. With Blox AI, businesses can automate the extraction of key values from invoices, define attributes against a structured format, validate availability and adherence of the extraction output against overall document inventory, and match the required PO-invoice-GRNs. Despite differences in structure across various invoices, the Blox AI platform is able to pick up the required fields using NLP and facilitate faster invoice processing. This results in higher accuracy, lesser dependency on manual resources, and the ability to scale.
reduction in resource costs with AI-powered Invoice Processing
3) Match Candidates To The Right Roles With Automated Profiling & Recruitment
faster processes with AI-powered Workflow Automation
77% of recruiters say they are more efficient in their recruiting efforts when they have a solid understanding of the market and talent pool they’re recruiting. With AI-powered candidate profiling and recruitment, recruiters can drastically reduce the effort and time required to build high-quality talent pools. Given unstructured data that includes qualification-specific data points such as education, skills, experience, region-specific information, and more, the Blox AI platform is able to extract key data points, validate them across fields, and reconcile them in a structured format that teams can use for evaluation and, consequently, correct funneling of the candidate pool.
The Blox AI Intervention:
Given a business context, Blox AI identifies the best-fit AI model from thousands of models in the library, applies it in real-time, and delivers the required solution.
Using Natural Language Processing (NLP), Computer Vision (CV), Optical Character Recognition (OCR) and a variety of machine learning tools, Blox.ai identifies, labels and extracts relevant data from any type of document.
The AI then maps this extracted information into a structured format while configuring a model which can be applied to all similar document types. The Blox.ai stack is set up to reconcile the data based on business requirements and to push the output to downstream systems automatically.
Thus, enabling businesses to enhance and augment processes and create hyper-efficient workflows that improve the accuracy of their outcomes.