The various organizations from different verticals are benefited significantly after implementing RPA. They multiplied the productivity of resources where RPA does repetitive tasks faster with accuracy. The resource can perform more innovative tasks allowing more profit to businesses.
By automating well-defined tasks like creating a user in SAP or restart a server and operating with structured data like processing excel or pdf in a certain format, RPA has benefitted to the organizations with speed and cost-effectiveness. But most of the times there can be unstructured data like invoices, excel, email requests in different formats. Traditional RPA has difficulty processing natural language, text, web content and images.
Artificial Intelligence is a technology where the machine can understand the natural language and act like a human. AI + RPA allow automating task and process the unstructured data where the traditional RPA can’t. AI expands the RPA capabilities to process unstructured data, image content and understand natural language, etc.
How Cognitive RPA is Smarter than Traditional RPA
Cognitive automation is an additional feature added to the RPA, allowing solutions to leverage AI technology to automate tasks by understanding the natural language. Earlier, this could only be performed by human workers. One of the most important abilities of cognitive automation is processing unstructured data, images, text. AutomationEdge RPA tool is the fastest data processing RPA with the ability to process structured and unstructured data 10x faster than its peers in the industry.
[Also Read: How Automation, AI, and Machine Learning Are Changing Business Operations]
Document Processing
Document processing is a critical task for the organizations analyzing the unstructured data like the image or documents is time consuming task as it has to do manually. Automating this is an innovative task and can have a big impact on the organization’s efficiency. One of the largest banks required 24 resources to process 200 requests for Deduplication Process. After implementing RPA, TAT is reduced from 15 min to 5 min with no resource.
[Also Read: Artificial Intelligence for Banking, Insurance and Financial Services]
Key Capabilities for Cognitive Automation
Natural Language Processing (NLP): Basic language understanding makes it much easier to automate most customer service processes. Resolve queries without human intervention.
Optical Character Recognition (OCR): OCR enables automating the document formats like images, handwritten forms and scanned copies. This can significantly impact the business processes in document oriented industries like banking, insurance, legal, retail, manufacturing, law by automating processing documents like invoices, handwritten applications, forms, cheques, etc. If an organization receives 1000s of invoices daily, processing it with RPA + OCR will reduce the huge number of man-hours to process the documents with increased TAT and reduced cost.
Machine learning: Decision making is done by machine learning algorithms by understanding the natural language of the process. Machine learning algorithms generate data patterns and are capable of learning from past data to understand the meaning. With machine learning, automate processes by replacing human judgement with machine judgement. With past data, bot can understand the email requests and create a ticket in service desk systems.
Unstructured vs. Structured Data
Structured data is ordered and labelled properly where the machine can understand it easily. This data is fit into a relational SQL database and can work well with basic algorithms. The structured data is very easy to automate and has a better success rate. Many organizations are using structured data for automation.
Unstructured data is difficult to interpret by algorithms. Unstructured data includes text, images, PDFs, natural language input, scanned documents or web content. This data is very difficult for automated systems to analyze and parse. For traditional RPA solutions, unstructured data has to be converted to structured data manually by a human to process further.
Most companies are finding difficulties in extracting information from unstructured data. Automating the unstructured data becomes a major problem for many RPA solutions. The important documents that most RPA solutions cannot parse are invoices, images, scanned applications, customer emails and voice messages. This makes it difficult for automation to be used in all front office and back office business processes. In order to overcome these issues, organizations have to adopt cognitive automation.
Conclusion
AI-Powered RPA becomes a powerful automation tool to perform successful RPA implementations. It can process the unstructured data types such as text, natural language, images and web content etc. In the rapidly evolving world of automation, companies will gain a significant competitive advantage with cognitive automation where they achieve increased efficiency and productivity.
To learn more about the Cognitive capabilities of the Fastest RPA tool, AutomationEdge and where can you use it in your organization, request for a free live demo.