Robotic process automation (RPA) has come of age in the past decade, having gained considerable traction among enterprises looking to take the next step in their journey toward intelligent automation. RPA offers substantial benefits, such as taking tedious, repetitive, low-value tasks off knowledge workers’ plate and placing them into the domain of software robots.
That promise has spurred aggressive growth in RPA deployments, with global RPA revenues eclipsing $2 billion in 2021 and ballooning to more than $13 billion by 2030.1 But, as is often the case with emerging technologies, it’s easy to get caught up in the hype and fall victim to myths, misconceptions and mistakes in planning and deploying RPA. Here are three to avoid:
Myth 1: RPA is an end-to-end solution
Many CIOs, CTOs and other executives have become very excited about the promise of RPA at a conceptual level. But the truth is that RPA solutions often fail to live up to the hype: An Ernst & Young analysis of RPA deployments indicates that between 30% and 50% of RPA deployments fail.2
It’s important to keep in mind what RPA can and can’t do, and not try to stretch its capabilities beyond what is feasible. Instead, organizations need to complement RPA’s strength in automating manual tasks. In some cases, RPA may be successful in other areas across the automation spectrum, but it’s best to stay focused—especially in early RPA deployments.
Myth 2: RPA is a ‘set it and forget it’ proposition
No IT executive or C-suite leader would expect that their organization could use the same software, hardware infrastructure or IT services forever. RPA is no different; it needs updates, upgrades and new functionality to optimize its return on investment and ease the journey to intelligent automation.
These updates and upgrades don’t deploy themselves, no matter how much intelligence is built into their framework. Employees still must manage the bots to ensure the right value is being delivered and to spot new opportunities. This kind of watchful eye of human partners is essential, to not only evaluate the solution’s performance but, to be frank, ensure that organizational leadership has up-to-date information on the business benefit of the RPA deployment. Proper automation maintenance is a must, including having the right team and technologies in place to monitor, assess and report progress—or snags.
Deloitte RPA Case Study: How Deloitte Reduced Preparation Time From Days to minutes with RPA software
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Download NowMyth 3: Your human workers are excited about automation
Over the years, there have been many ill-placed concerns that automation, per se, would render traditional workforces worthless and organizations would be little more than a kingdom of bots. Of course, this is far from the truth, but it doesn’t mean your people aren’t worried about their jobs when RPA initiatives are discussed and developed.
The truth is that RPA can, in fact, replace human workers. When RPA—and ultimately, intelligent automation—takes root, there will be displacement of some small portion of the traditional workforce.
At the heart of addressing this myth is to honestly assess RPA’s impact on human workers as part of a comprehensive approach to change management. RPA initiatives will have the greatest likelihood of success when organizations embrace change management, which is the most complex part of automation. Unfortunately, many organizations tend to underestimate and under-resource the process—or ignore it completely.
Organizations that fall victim to any—or all—of these three myths are in danger of not realizing the full potential of their RPA investments. Instead, organizations need to have a sober, reality-based and honest assessment of what their RPA goals and challenges will be, identify key performance indicators that have been discussed among all stakeholders, and enact a best-practice methodology that tests and validates assumptions at each step.
1 “Robotic Process Automation (RPA) Market Size Worldwide From 2020 to 2030,” Statista, August 2021
2 “Get Ready for Robots,” Ernst & Young, 2016
