4 Robotic Process Automation (RPA) trends to watch in 2022

IT leaders should see more Robotic Process Automation (RPA) efforts mature and deliver business results. Here are four things to look for
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RPA Robotic Process Automation lessons

Robotic Process Automation (RPA) in 2022 won’t be about what’s new and shiny, but rather the evolution and maturation of trends already underway.

This should be welcome news for IT and business leaders who see RPA as a single tine in a multi-prong automation strategy. “New and shiny” does not necessarily produce results. But 2022 in general is likely to be a year where boards, investors, customers, and other stakeholders ask: Where are the results?

To put it more specifically: Where are the results from those outsized investments you’ve been making in digital transformation, AI/ML, cloud, and elsewhere?

RPA is a results-oriented technology that intersects with those big IT pillars in various ways. It has been hyped plenty, and now it’s entering a more mature phase in which IT leaders and their teams will be refocusing on use cases that actually work and produce outcomes.

“One of the attractions of automation is that it can be incremental,” says Red Hat technology evangelist Gordon Haff. “This is automation through the lens of traditional system admins and even site reliability engineers in many cases. Do something manually more than once and automate it so that you don’t have to ever again.”

That incremental approach – enabled by the likes of Bash and other automation-friendly programming languages, RPA tools, and other technologies – is great. On its own, however, it doesn’t constitute a plan. IT leaders can build a strategic framework that enables and encourages incremental automation while also ensuring the team is spending its time and resources in areas that align with broader goals.

“Automation can also be more strategic,” Haff says. “This means asking, in the words of Dinesh Nirmal, general manager of IBM Automation (speaking at The Economist, Innovation@Work US virtual event in October), where can you ‘free up employees to focus on strategic priorities?’”

[ How can automation free up more staff time for innovation? Get the free eBook: Managing IT with Automation. ] 

4 notable RPA trends in 2022

Let’s examine four big-picture trends that will influence and intersect with RPA strategy in 2022.

1. The "robots are coming for your job" narrative loses steam

Automation fear is real and managers shouldn’t dismiss or ignore it – in fact, silence from leadership will likely be understood as bad news in terms of job security if your organization is in the midst of a significant automation push.

[ Be proactive about job-loss anxiety: Automation vs. IT jobs: 3 ways leaders can address layoff fears. ]

Automation will impact jobs over time, but the machines-run-amok scenarios are far-fetched, particularly in corporate settings and other workplaces where critical thinking is a fundamental part of many jobs.

“The common misperception that robotic process automation will replace human workers will be proven wrong in 2022,” says Adam Field, SVP of technology strategy and experience, Kofax. “The adoption of automation is at an all-time high, but the U.S. continues to add jobs.”

Indeed, hiring and retention challenges – and the hype around “The Great Resignation” – at a time when many organizations are accelerating their automation strategies seems to contradict the sweeping claim that automation eliminates jobs en masse.

If you’re rooting for the robot overlords to take power, you’d be better served hitching your wagon to more advanced forms of AI that aren’t necessarily in widespread use today. RPA may impact job duties but it is not likely to replace people altogether. Its rule-based nature makes it suitable for things like repetitive data processing tasks, but it’s not able to make decisions or handle nuance or changing conditions on the fly.

Even more cognitive forms of automation require lots of human oversight and intervention.

“Algorithms and machines will be primarily focused on the tasks of information and data processing and retrieval, administrative tasks, and some aspects of traditional manual labor,” notes the World Economic Forum’s Future of Jobs report. “The tasks where humans are expected to retain their comparative advantage include managing, advising, decision-making, reasoning, communicating, and interacting.”

Field sees RPA as a possible retention tool: By offloading mindless computer work to bots, organizations can reduce monotony in certain jobs and appeal to people’s creative, intellectual nature.

“By leveraging automation to handle tedious and mundane work that doesn’t excite the human workforce, businesses will begin to see increased retention in response to the recent Great Resignation wave,” Field says.

2. Intelligent automation depends on cooperation, not competition

“Intelligent automation” has been an on-again, off-again buzz phrase in the RPA world. It’s on again, perhaps permanently.

The term usually refers to a mix of technologies, including RPA, low-code and no-code development tools, and AI/ML. It also implicitly refers to the reality that RPA on its own is not “smart” – it can’t learn on its own (as with some ML models, for example) or adjust to things like UI changes without human intervention. Intelligent automation is often an aspirational vision of how more basic forms of process automation can complement more advanced cognitive technologies, and the other way around.

To this point, the potential has outpaced the reality. The RPA market has been quite competitive rather than community-oriented, as has the broader automation and AI industry. But true intelligent automation will by definition require a collaborative approach, according to Jon Knisley, principal consultant for process and automation excellence at Fortress IQ. Knisley sees an increased focus on the need for a cooperative intelligent ecosystem in 2022.

“No one can do it alone, and providers that think it’s possible will suffer the same fate of Icarus and fall back to Earth,” Knisley says. “Intelligent automation done right requires too many moving pieces for any one company to deliver a solution for the enterprise.”

A vendor might offer an “intelligent automation” that encompasses an RPA tool and an AI or machine learning solution. But that may only be scratching the surface of what would be possible in a collaborative ecosystem, Knisley says.

“Process technologies [such as process mining or task mining], workflow tools, business intelligence, low-code platforms, and other services are critical to a comprehensive intelligent automation competency. Beyond the obvious connections, look for more outcome-based partnerships around compliance and customer service,” Knisley says.

That ecosystem will grow if for no other reason than that the market will demand it: Many of the pie-in-the-sky promises of automation aren’t feasible without a comprehensive mix of tools.

“Companies are increasingly looking to drive greater efficiency and cost-savings out of operations that are only possible with a robust toolset,” Knisley says. “Each product can deliver value, but more total value is achieved when the solutions are used together. It’s the new math where 1 + 1 = 3 is a feasible answer.”

[ How can public data sets help your AI work? Read also: 6 misconceptions about AIOps, explained. ]

3. Prominent AI use cases complement RPA strategies (and vice versa)

Whether it resides under the intelligent automation banner or not, 2022 will see more intersection points between RPA and AI/ML, according to Mike Mason, global head of technology for Thoughtworks.

Beware the pitches that portray automation as sorcery that solves broken processes and problematic culture.

Mason sees enterprise AI/ML use cases today falling into one of two categories: optimizing data-driven decisions at scale (such as with pricing or product recommendations) and helping humans to explore options and/or make choices as part of a complex initiative such as assisting an executive team as it develops a carbon-neutral plan. Mason thinks those patterns will naturally guide strategic choices about how to pair RPA and AI/ML together going forward.

“We expect to see these two distinct styles reflected in the way AI can augment RPA,” Mason says. “If automation is being used for bulk data processing, optimization algorithms can be built in. If automation is being used for human-in-the-loop activities such as customer service, AI may provide a set of possible solutions that a customer service representative (CSR) can choose from, and then carry those out on their behalf.”

That will also feed a less desirable pattern: AI-assisted RPA will suffer from the same types of hype as AI itself, according to Mason.

“​​With machine learning, there’s an assumption that past data will inform future decisions, which isn’t always the case,” Mason says. “Does the use-case require something sophisticated like ML? Maybe not – sometimes a simpler, older statistical technique would suffice.”

The big pitfall to watch for here: Assuming AI/ML-enabled RPA is always better. That’s simply not going to be the case. Beware the pitches that portray automation of any kind – and perhaps especially AI-enabled automation – as sorcery that solves broken processes and problematic culture.

“Organizations need to be skeptical and aware of the fallacies of AI in the context of RPA,” Mason says. “Assuming that RPA will work smoothly and solve previously difficult problems because ‘now it has AI’ might lead to a poor outcome.”

4. RPA gets the people-process-tools makeover

RPA and other forms of automation need to copy page one of the DevOps playbook: It’s about people, process, and technology. Too many automation initiatives focus on technology alone, and when they do mention people it’s often a limited focus on negative job impacts. (See also #1.)

Knisley emphasizes the importance of this in the context of complex change: Organizations are collectively pouring billions into intensive digital transformation initiatives, but often with mixed results at best.

That’s often because of a lack of visibility and focus on people and processes: Too many companies continue to automate and “transform” processes they don’t really understand.

“The biggest challenge to any complex change is the lack of knowledge on current-state activities,” Knisley says. “Unfortunately, most companies do not understand how they truly operate on a day-to-day basis. You cannot get to the targeted future state efficiently without a clear view of your current operations.”

This is another reason to root for a thriving ecosystem around RPA and intelligent automation: It may help fill in gaps in “process intelligence” – generally defined as the automatic, always-on collection of information about an organization’s processes and workflows – with technologies like process mining.

“Process intelligence gives you that ‘what are we doing today’ operational insight to then improve and drive value for the organization,” Knisley says.

Ultimately, automation and transformation failures are often the result of undervaluing people and misunderstanding (or ignoring) processes. No technology will solve those problems. More organizations will come to terms with that in 2022 and they evaluate why they’re not reaching their digital transformation goals.

“Too much of the focus on transformation has been directed towards technology as the answer without considering the people and process dimensions,” Knisley says. “Through a more balanced people-process-technology approach, transformation success rates can be improved dramatically.”

[ Where is your team's digital transformation work stalling? Get the eBook: What's slowing down your Digital Transformation? 8 questions to ask. ]

Kevin Casey writes about technology and business for a variety of publications. He won an Azbee Award, given by the American Society of Business Publication Editors, for his InformationWeek.com story, "Are You Too Old For IT?" He's a former community choice honoree in the Small Business Influencer Awards.