You’re going on vacation. You own a multimillion smart home with many extremely complex, interwoven systems. Luckily, your house-sitter comes highly recommended as dependable and fully capable of following instructions on standard operating procedures for your home.
But what about the customizations you’ve made? And what if something deviates from standard while you’re away? Would you just hope for the best, close the door behind you, and not look back?
Probably not. So why do the same with your organization’s robotic process automation (RPA) initiative?
Too often companies consider the launch of an automation initiative to be the end of their effort. But there are many reasons why an RPA endeavor might not be considered successful, and some are tied directly to monitoring to ensure things are truly running as expected.
In a previous post we talked about making the most of your digital workforce capabilities using process mining. It’s the same technology that gives insights to bolster your RPA initiative before you even start. But it can also help you evaluate your process after rollout.
Let’s look at a few common RPA post-implementation issues and how process mining can help:
Despite the operational ease it brings, automation is seldom a single, simple effort. One measure of success should be the peace of mind that comes from knowing what’s really happening rather than operating on the assumption that everything’s going according to plan.
Trust is good. Confidence is better. Confirmation is best.
Eager to learn more? Check out our webinar “Making RPA and Automation Successful with Intelligent Process Mining Technology”.
Southard Jones is Celonis’ VP, Product Marketing. Prior to Celonis, Southard held various executive product and marketing roles at enterprise software companies in the Business Intelligence, Analytics, and Data Science market, including Domino Data Lab, Birst, Right 90, and Siebel Analytics.