In 2022, robotic process automation (RPA) will be about the progression and maturation of existing trends, rather than what’s new and shiny.
RPA is a goal-oriented technology that crosses paths with those major IT pillars in a variety of ways. It’s been overhyped, and now it’s moving into a more mature stage, with IT leaders and their teams focusing on use cases that truly function and produce results.
One of the enticements of automation is that it can be done in small steps. In many cases, this is automation as seen through the eyes of traditional system administrators and even site reliability engineers. Organizations should do something manually multiple times and then automate it so that they never have to do it again.
In 2022, there are a few big-picture trends that will influence and connect with the RPA strategy.
The “robots are coming for your job” story is starting to lose traction
Fear of automation is real, and managers should not dismiss or ignore it. In fact, if the organization is in the midst of a major automation push, silence from leadership will likely be interpreted as bad news in terms of job security.
Employment will be impacted by automation over time, but machines-run-amok scenarios are unlikely, especially in corporate settings and other locations where critical thinking is a key component of many jobs. In 2022, the widely held belief that robotic process automation will supplant human workers will be disproved.
Indeed, at a time when many firms are increasing their automation plans, hiring and retention issues as well as the hype surrounding “The Great Resignation” appears to contradict the broad idea that automation removes employment en masse.
Intelligent automation relies on collaboration rather than competition
In the RPA world, “intelligent automation” has been an on-again, off-again buzz term. It’s back on, possibly indefinitely.
RPA, low-code and no-code development tools, and AI/ML are all examples of technologies that fall under this umbrella. It also implies that RPA isn’t “smart” on its own – it can’t learn on its own (as some machine learning models do) or respond to changes in the user interface without human intervention. Intelligent automation is frequently portrayed as an idealized image of how more basic kinds of process automation might work in tandem with more complex cognitive technology, and vice versa.
A vendor may offer “intelligent automation,” which includes both an RPA tool and a machine learning or AI solution. But that could only scrape the surface of what a collaborative environment could offer.
Process technologies like process mining or task mining, workflow tools, business intelligence, low-code platforms, and other services are essential to a comprehensive intelligent automation capability. Firms should look for additional outcome-based relationships in the areas of compliance and customer service, in addition to the obvious links.
RPA strategies are complemented by prominent AI use cases (and vice versa)
More intersection points between RPA and AI/ML will be seen in 2022, whether they fall under the intelligent automation banner or not.
Today’s enterprise AI/ML use cases are divided into two categories, according to industry experts: optimizing data-driven decisions at scale (such as pricing or product recommendations) and assisting humans in exploring options and/or making decisions as part of a complex initiative, such as assisting an executive team in developing a carbon-neutral plan. Those trends will naturally drive future strategic decisions regarding how to combine RPA and AI/ML.
In the context of RPA, organizations should be wary and aware of AI misconceptions. Assuming that RPA will run easily and handle previously difficult problems because “it now has AI” will likely result in a poor result.