In 2022, robotic process automation (RPA) will be about the progression and maturation of existing trends, rather than what's new and flashy.
This is good news for IT and business leaders who regard RPA as only one component of a larger automation plan. "New and shiny" does not always imply success. However, 2022 is likely to be a year in which boards, investors, consumers, and other stakeholders all ask the same question: Where are the results?
To put it another way, where are the results of your large-scale expenditures in digital transformation, AI/ML, cloud, and other areas?
RPA is a goal-oriented technology that crosses paths with those major IT pillars in a variety of ways. It's been heavily promoted, and it's now entering a more mature stage.
IT leaders and their teams will emphasise on use cases that truly work and create results during this phase.
"One of the benefits of automation is that it can be incremental," says Gordon Haff, a Red Hat technology evangelist. "In many cases, this is automation through the eyes of traditional system administrators and even site reliability engineers." Do something manually multiple times and then automate it so you don't have to."
In 2022, there will be four important RPA trends.
Fear of automation is real, and managers should not dismiss or disregard it. In fact, if your company is in the process of automating, silence from the top will likely be interpreted as negative news in terms of job security. in the midst of a major automation push Employment will be impacted by automation over time, but machines-run-amok scenarios seem unlikely, especially in corporate settings and other locations where critical thinking is a key component of many jobs.
"In 2022, the prevalent misunderstanding that robotic process automation will replace human workers will be proven false," says Adam Field, Kofax's SVP of technology strategy and experience. "While automation adoption is at an all-time high, the United States continues to add jobs." 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" – appear to contradict the broad premise that automation removes employment en masse.
If you want the robot overlords to take power, you'd be correct conversing and communicating."
By delegating monotonous computer labour to bots, businesses can alleviate monotony in certain tasks and appeal to people's creative, intellectual nature, according to Field. "Businesses will begin to see increased retention in response to the recent Great Resignation wave by employing technology to handle repetitive and mundane tasks that does not excite the human labour," Field adds.
In the RPA field, "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 "clever" on its own — it can't.
Learn on its own (as some machine learning models do) or adapt to changes in the user interface without the need for human participation. Intelligent automation is frequently portrayed as an idealised image of how more basic kinds of process automation might work in tandem with more complex cognitive technology, and vice versa.
The potential has so far exceeded the reality. The RPA sector, like the broader automation and AI industry, has been very competitive rather than community-oriented. According to Jon Knisley, chief consultant for process and automation excellence at Fortress IQ, genuine intelligent automation will by definition necessitate a collaborative approach. In 2022, Knisley expects a greater emphasis on the need for a cooperative intelligent environment.
"No one can do it alone, and providers who believe they can are mistaken."
"It's feasible that they'll meet the same fate as Icarus and return to Earth," Knisley says. "To create a solution for the enterprise, intelligent automation has too many moving parts for any one company to deliver."
A provider may offer "intelligent automation," which includes an RPA tool as well as an AI or machine learning solution. However, Knisley believes that this is merely the beginning of what a collaborative ecosystem may offer.
"A comprehensive intelligent automation capability requires process technologies [such as process mining or task mining], workflow tools, business intelligence, low-code platforms, and other services." "Look for more outcome-based partnerships around compliance and customer service, beyond the obvious ties," Knisley says.
If for no other reason, that ecology will expand.
that it will be demanded by the market: Many of the lofty promises of automation are unattainable without a diverse set of instruments.
"Companies are increasingly seeking more efficiency and cost savings from operations, which can only be accomplished with a robust toolset," Knisley explains. "Each product has value, but when the solutions are applied together, the entire value is greater. It's the new math, in which 1 + 1 = 3 is a viable solution."
According to Mike Mason, worldwide head of technology at Thoughtworks, additional junction points between RPA and AI/ML will be seen in 2022, whether they fall under the intelligent automation label or not.
Today, Mason sees two types of enterprise AI/ML use cases: optimising 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. Mason believes that those patterns will automatically inform strategic decisions on how to combine RPA with AI/ML in the future.
"We anticipate seeing these two unique styles mirrored in how AI is used."
"RPA can be supplemented," Mason explains. "If bulk data processing is automated, optimization methods can be integrated." If AI is utilised for human-in-the-loop operations like customer service, it may provide a list of viable solutions from which a customer service representative (CSR) can choose and subsequently carry out on their behalf."
According to Mason, this will fuel a less desirable pattern: AI-assisted RPA will suffer from the same forms of hype as AI itself.
"There's a presumption with machine learning that prior data will drive future judgments, which isn't necessarily the case," Mason explains. "Does the use-case necessitate anything more advanced, such as machine learning?" Perhaps not; in some cases, an older, simpler statistical technique would suffice."
The major stumbling block to avoid Assuming that AI/ML-assisted RPA is always better. That is simply not going to happen. Be wary of sales pitches that present automation of any kind – particularly AI-enabled automation – as a magical solution to broken processes and bad culture.
"In the context of RPA, organisations must be wary and cognizant of AI fallacies," Mason says. "Assuming that RPA would run smoothly and solve previously difficult problems because 'it now has AI' may result in a poor result."
RPA and other forms of automation should follow the DevOps playbook's first page: It all comes down to people, processes, and technology. Too many automation programmes are solely focused on technology, and when they do address people, it's usually with a narrow focus.
Knisley underlines the significance of this in the context of complicated change: Companies are investing billions in rigorous digital transformation programmes, yet the results are generally mixed at best.
This is frequently due to a lack of visibility and a focus on people and processes: far too many businesses continue to automate and "transform" operations they don't fully comprehend.
"The absence of understanding about current-state activities is the largest hurdle to any major transformation," Knisley explains. "Unfortunately, most businesses are unaware of how they work on a daily basis. Without a clear picture of your existing processes, you can't get to the desired future state efficiently."
Another incentive to support a robust RPA and intelligent automation ecosystem is this: It might be useful for filling in gaps.
Using technologies like process mining in "process intelligence," which is described as the automatic, always-on collecting of information about an organization's processes and workflows.
"Process intelligence offers you that operational insight into 'what are we doing today' so you can enhance and produce value for the organisation," Knisley explains.
Finally, mistakes in automation and transformation are frequently the result of undervaluing people and misunderstanding (or neglecting) processes. No technology will be able to solve those issues. In 2022, more businesses will accept this and assess why they aren't achieving their digital transformation objectives.
"Too much emphasis on technology as the solution has been placed on the people and process dimensions," Knisley argues. "Transformation success rates can be increased by using a more balanced people-process-technology strategy."