In our final blog post of the series, we investigate the process of rolling out an RPA implementation. With RPA, your outputs are only as good as your inputs and that applies to the installation as well. Without proper planning and foresight you could seriously hamper your implementation’s efficiency. In this post we share some important considerations to make before moving forward with RPA. If you’d like to download the full version of our 3-part series on the myths around RPA, click here.
Myth #3 – Implementing RPA Is the Same as Any Other New System
Although RPA is remarkably effective, its implementation requires investments in time for both implementation as well as learning curves. Small adjustments that are easily made and intuitive compensations from experience to accomplish a normal process could shut down a robot. The slightest deviation or unexpected glitch that drives the RPA out of bounds of the rules requires immediate attention.
RPA needs to be integrated with other related technologies to create an automation solution. Many believe there is no requirement to change back-end legacy systems. Changing back-end legacy systems may significantly speed up your deployment and training process. However, legacy applications could require workarounds that will ultimately delay development.
Another important consideration when implementing RPA is developing an exception- handling process. While RPA can process high-quality data very efficiently, it struggles with suboptimal data as we saw in Myth #1. Since RPA implementations can often be implemented as a black box process, it’s important to set up exception handling processes and train a human team to operate them. When your RPA implementation struggles with inaccurate data you need to have simple, accessible processes for identifying the erroneous data and fixing it without interrupting your RPA.
How Vidado Improves RPA
Vidado helps by providing consistently-formatted data to your RPA implementation, ensuring that the maximum amount of data is routed for automation and only the necessary amount is sent to human reviewers.
Step 1 – Vision: Vision automatically identifies and sorts the documents you upload, ensuring that the right data is sent to the right RPA workflow. Vision then rotates, de-skews and prepares your documents for optimal data extraction.
Step 2 – Read: Read is where your data becomes digitized. Thanks to the preparation in the Vision step, Read digitizes data from forms more accurately than humans.
Step 3 – Review: Review is an intelligent exception handling interface. By automatically identifying which pieces of information require additional human review, Vidado prevents erroneous data from being sent to RPA.
Step 4: Transform: Transform makes your digitized data automation-ready. By enriching data from 1st and 3rd party sources and then ensuring consistent formatting, Transform provides the best data to your RPA – improving your current workflows and enabling you to automate new ones.
Want to Learn More?
Download the full white paper here to learn about the other myths around RPA and how you can get the most out of your implementation.