How companies can overcome the content processing drawbacks of RPA
This article examines how organizations can address deficiencies in the automation of robotic processes (RPA) in content processing
How can companies address these RPA challenges?
Robotic process automation was designed to resemble digital assistants for employees. It is known to be useful for streamlining business operations without increasing costs while reducing human error. However, RPA software alone has its pitfalls in processing content due to incompatible intelligence.
However, there are ways to overcome these disadvantages, as five experts in the field have shown.
One way to overcome the disadvantages of content processing is to combine other intelligent technologies and integrate them into the system.
“RPA technology is mainly used to automate rule-based processes and to mimic human actions, such as processing an invoice and entering data into SAP or Oracle systems from a Microsoft Excel spreadsheet,” explained Gopal Ramasubramanian, Senior Director, Intelligent Automation and Technology at Cognizant.
“However, when it comes to processing content in documents, additional intelligent recording technologies are required that combine optical character recognition (OCR), natural language processing (NLP) and machine learning (ML) to extract metadata from documents and documents automate the processing.
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“There can be different types of content, such as: B. structured / printed, structured / handwritten, unstructured / printed and unstructured / handwritten. It’s pretty easy to extract structured content using standard OCR technologies. Extracting unstructured content is challenging, however, and we are increasingly seeing the advent of NLP and machine learning technologies to address this. “
Arpit Oberoi, RPA specialist at delaware added: “The biggest challenge for RPA technology today is that it often still has difficulties handling unstructured content and data. To solve this persistent problem, companies can try to harmonize their data into more structured datasets and, if possible, also combine AI and RPA to optimize or automate content processing. “
Third party involvement
Andrew Rayner, Vice President, Professional Services EMEA at UiPath, continued on the topic of additional integration and explained the need for third-party applications in combination with RPA.
“In the past, RPA technology could be integrated with third-party applications to aid in content processing,” said Rayner. “For example, many OCR providers (Abbyy, IBM, etc.) have direct integration so that semi-structured or structured documents can be classified and recognized.
“At UiPath, we’ve invested heavily in document understanding to provide customers with a turnkey solution with the flexibility to use various techniques such as pattern matching, templates, and machine learning to deal with unstructured and semi-structured document types.
“As we dig deeper into how content is processed, it plays an important role in hyper-automation. We now have long running workflows with people in the loop so that both robots and humans can work seamlessly on a transaction.
“Tremendous advances have been made in terms of application connectivity to process content through the user interface or APIs. With the advent of RPA and ML, robots can now classify, understand sentiment and suggest the next best action on unstructured content.”
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Invest carefully in tools
While there is a need to seek additional software assistance, organizations need to be careful about overspending and ensure that the tools they invest in are for a clear, specific purpose.
“Organizations have a lot of unstructured data across their organization in a wide variety of formats, whether it be documents, emails, or even system-based data that is not structured, such as reconciliation payment data,” said Chris Porter, CEO of NexBotix. “This creates a problem for RPA, which can only handle structured, rule-based digital processes.
“There are several options for customers to address these deficiencies. One is to buy a bespoke point solution like an OCR tool that can extract data from documents, or invest in a workflow tool that you can use to orchestrate robots and humans, or buy machine learning from Google, to gain knowledge from your complex documents. These tools are designed to solve a very narrow set of problems within narrow parameters.
“However, each of them has its own technical challenges. There are significant costs involved in embarking on any of these projects. You also need the right skills and technology to support each initiative. Each use case needs to be treated as an individual project because you are effectively shopping for that specific need and if you have many different types of data in your company, many different processes that have this level of unstructured data, you will have to start over each time buy right solution to fix every single problem.
“The key is to use the right technology to solve the right problems, but in a scalable way, with business value at the center. For example, we have standard invoice processing that we can implement in any company by using reusable components and automating the end-to-end business process in accounts payable. We’ve already done the hard work building this up and making it work for the client. “
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A final way to overcome the disadvantages of RPA in processing content is to implement additional functionality.
Neil Murphy, ABBYY’s global vice president, said, “The biggest challenge with RPA is the inability to process unstructured content such as invoices, emails, forms, receipts or correspondence. However, companies can and must overcome this.
“All it takes is content intelligence skills that make RPA bots smarter by adding cognitive skills like analyzing, understanding and processing unstructured content. Organizations can deliver this content intelligence knowledge with easy-to-use, no-code or low-code solutions so that their employees can create RPA bots that can process a wide variety of documents.
“We are already seeing acceptance in companies of all sizes in which the technological entry barrier is removed by such an approach. This, in turn, drives innovation – some companies are now combining these skills to provide an expanded cognitive understanding of complex use cases. Customer onboarding is a good example that involves processing a wide variety of documents, from identification documents and onboarding forms to bank statements and proof of address. “