Intelligent document processing (IDP) provides structured data RPA bots need for automating finance and B2B data integration workflows.
With all the options for RPA and data automation, navigating the technological landscape is tricky. If you’re looking for a software solution to automate manual processes, pairing IDP with RPA is a powerful combination.
What's the Difference between RPA and IDP?
The difference between RPA and IDP is that IDP automates the process of structuring unstructured content and RPA automates "point and click" operations typically performed by a human worker. They both work to achieve a common goal: decreasing manual data processing.
Manual processes are highly inefficient, error-prone, and expensive. While solving the reason for so much manual effort seems like a logical thing to do, it may not be realistic for many reasons.
The use of multiple accounting systems is often the result of mergers and acquisitions, and attempting to streamline operations with a single (or upgraded) ERP solution might just be too disruptive. Whatever the reason for inefficient back-end processes, automation is certainly an answer.
The most important thing to understand about RPA vs. IDP is that RPA is dumb. There is zero intelligence built into RPA. It cannot read, understand, or interpret data. Whether you use an attended (human initiated) or unattended (rules-based) bot, it requires highly structured data - either onscreen or in text-based files.
The best scenario for using RPA in finance and B2B data integration is when you have people manually entering data from one system into another.
If you have a lot of rekeying going on, prioritize the most error-prone tasks first. But automation isn’t just about accuracy. Data quality and maintaining compliance are hallmarks of successful RPA implementations.
Why do RPA Projects Fail?
One of the number one causes of failure for RPA projects is a misunderstanding of its ability to scale within the organization. Because RPA is not an intelligent solution, the use-cases are extremely narrow. While it’s perfect for the scenario of rekeying already-digital data, it cannot be used in other processes which deal with with unstructured or analog data.
Another factor hurting the success of RPA is the assumption that its value is in labor reduction alone. While RPA can automate certain manual tasks, the bigger savings really comes from higher quality business processes.
If you can’t tie a financial win to achieving better business outcomes, RPA is not going to be an automation silver bullet. RPA is great at automating bad processes that are too costly to fix.
According to research firm Gartner, Inc., “40% of enterprises will have robotic process automation (RPA) “buyer’s remorse,” due to misaligned, siloed usage and inability to scale.”
Pairing Intelligent Document Processing with RPA Transforms Automation
The biggest challenge in finance and B2B data automation is getting content from business documents like invoices, patient records, or data files into software applications. Combining RPA with an intelligent document processing tool (IDP) is a highly effective strategy to ensure success.
Intelligent document processing provides the heavy lifting needed to deliver structured data to RPA bots.
Intelligent document processing tools combine OCR, machine learning, and data sciences tools like computer vision to extract relevant information from unstructured and analog data sources. IDP is a human-in-the-loop data integration solution that empowers subject matter experts to work with difficult data.
Because successful automation is all about improving business outcomes with better processes, all RPA implementations should include a solution for intelligent document processing.