How AI Boosts RPA (Robotic Process Automation)?

Every day all types of organizations manufacture millions of documents in different formats and layouts. These records contain information that must be retrieved, stored, analyzed, and acted upon. Managing such vast quantities of data is time-consuming, boring, and routine. Before the knowledge can become “useful" as practical observations, it also requires tedious manual data entry and validation.

Put simply, RPA is a robot that mimics your keyboard and mouse actions. It replaces the human behind the keyboard and mimics the data entry and clicks that a normal user would perform in the graphical user interface. Of course, current RPA are even smarter and are already capable of running decisions and workflows while connecting with a wealth of other tools.

According to the Institute For Robotic Process Automation & AI, “it is the use of technologies to configure computer software or a ‘robot’ to capture and analyze current programs for performing transactions, controlling data, activating responses, and interacting with other digital systems."

RPA, in other words, automates the rule-based behavior of human employees. Easy, repeatable, standardized, high-volume, and digitally executed tasks are perfectly suitable for RPA technology. RPA is built on rules that can simplify a procedure by automating simple tasks or a sequence of simple tasks. An HR bot that automatically changes employee data through several HR tools or bots that generate monthly reports by extracting data and figures from various applications are examples of RPA processes. However, due to a lack of information traditionally provided by an AI engine, businesses fail to scale RPA properly.

RPA technology can be given a set of instructions based on a specific rule or flow, such as “if this, then that,” so it can automate rule-based processes. It will continue to run until all of the data that the rule applies to has been processed. RPA can process data from documents with a defined structure, such as forms or passports, by using rules or models to work through millions of records of similar designs. However, if a task isn't rule-based, RPA may not be enough to extract the necessary insights without the help of AI.

• Process emails and attachments
• Access web/enterprise applications
• File and directory transfers
• Copy and paste data
• Fill in forms
• Read and write databases
• Scrape data from the web
• Connect to a system via API
• Collect data from social media
• Follow “if this, then that" logic

Using machine learning (ML), Intelligent Document Processing allows software robots to read, extract, analyze, and make decisions based on data from documents. IDP combines document processing, artificial intelligence (AI), and robotic process automation (RPA). In the end, this means that robots can process unstructured documents with no defined layouts, such as resumes, and collect, interpret, and act on relevant data. As they incorporate the data they encounter, machine learning models improve the robots' skills and accuracy at document processing over time.

Intelligent automation is now being used by businesses to improve productivity and reduce human errors in document processing. This allows them to devote more time to higher-value activities and focus on organizational objectives. Machine learning advancements would lead to crucial breakthroughs in intelligent automation. Ultimately, this form of automation has been restricted in the past because reading a document requires logic and logical thought. As humans, we can easily recognize each document type, comprehend the context of the information contained within the document, and make decisions based on that information. Intelligent document processing is now in use, allowing machines to interpret, understand, and process documents as quickly as humans can in just a few seconds.

DocDigitizer is an IDP engine that uses an innovative purpose-built Cognitive Data Capture technology based on AI learning systems with Human-in-the-Loop to transform unstructured documents into machine-readable representations with (near) 100% accuracy. In other words, with DocDigitizer, you can turn any unstructured information into structured business-valuable data in just a few clicks. There’s no need to deploy, manage, and pay for data curation teams.

This automation is made possible by a machine learning engine that can interpret and comprehend documents in the same way that humans can. You can easily scan, evaluate, and process previously secret data once records have been turned into machine-readable content. Innovative companies use IDP to make data that is normally concealed in documents such as forms, invoices, or emails accessible in a simple and streamlined process.

DocDigitizer works with existing programs, such as RPA, to assist with document classification, data retrieval, and information processing for any type of document in any format or language. Because DocDigitizer extracts data with (near) 100% accuracy due to Human-in-the-Loop, the RPA can immediately plug into that data and start to automate your processes.

1. (near) 100% accurate due to AI + Human-in-the-Loop
2. No Code Data Capture
3. Data production at a high rate
4. Simple set up and activation
5. Full document process automation
6. Contextually understands data
7. Self-learning system with humans in the loop
8. Seamless integration with existing frameworks and systems

DocDigitizer works in conjunction with RPA processes to provide highly precise interpretation based on data stored in documents. It integrates with your favorite RPA method, such as UiPath, and provides document processes that go beyond simple RPA tasks.

DocDigitizer provides data extraction with (near)  100% accuracy to meet your unique enterprise-grade needs, with a document-definable schema and immersive AI/ML model training.
RPA can be used to automate document processing, but enhancing this process with intelligent document processing has significant benefits, including:

1. Time and expense savings associated with paper processing;
2. Human error in data processing is eliminated and enforcement is ensured;
3. Retraining workers from manual and routine data processing activities to more engaging jobs;
4. Streamlining company processes and cutting costs to boost productivity and efficiency.

RPA systems were once used by businesses to automate complex tasks. They now can simplify whole use-cases by integrating intelligent document processing and RPA. As a result, high-precision automation is achieved, eliminating errors and offering a fast return on investment.