Robotic process automation (RPA) doesn’t sneak up on many people anymore. It’s already a hot topic in IT and business circles: Gartner pegged RPA as the fastest-growing enterprise software category last year, and there are plenty of other numbers that speak to widespread interest in the technology already.
Don’t bet on that interest fading much in 2020.
“The RPA market will continue its rapid growth in 2020 as more enterprises come to understand both the power of process automation overall and the number of legacy processes for which RPA is an effective answer,” says Aaron Bultman, director of product at Nintex.
That said, Bultman and other RPA experts note there are some reality checks in store as organizations build out a broader automation strategy. Some might learn the hard way, for example, that not every process is an ideal fit for RPA. Let’s take a peek at some of the key RPA trends to watch in the year ahead.
[ See our related post: How to explain Robotic Process Automation (RPA) in plain English. ]
1. Yes, RPA adoption will grow
“My prediction is pretty simple: More RPA bots will be deployed in all industries where manual repetitive actions are being performed by human resources,” says Vishnu KC, senior software analyst lead at ClaySys Technologies.
[ Building an automation strategy? Read Robotic Process Automation (RPA) strategy: How to create a successful plan. ]
In other words, while RPA interest has been high for a while, actual adoption is now catching up. That’s reflected in forecasted spending on RPA software, which is en route to becoming a billion-dollar category in 2020. Gartner projects spending on RPA software to hit $1.3 billion this year. Forrester, meanwhile, has predicted the RPA software market to total $2.9 billion in 2021.
2. Reality checks about fit will follow
“The RPA market will also undergo a reality check in 2020 as enterprises pursuing digital transformation become more aware of the differences among processes, the range of automation technologies available, and the importance of using the right tool for the job,” Bultman says.
For example, Bultman notes, because RPA can mimic human keystrokes and mouse movements, it’s often a fit for computer-based processes that can’t be accessed via API. But for those processes that do support APIs, there may be better forms of automation.
As adoption grows, so will the reality checks, especially in organizations that have overly idealized expectations of the technology’s capabilities, or those that don’t have a solid grasp of their own processes.
“RPA is not 100 percent perfect. It is only as efficient as the person configuring the automation flow,” KC says. “If there are unexpected scenarios for which the bot has not been configured to take necessary action, it’ll affect the flow and could break the whole chain of automated tasks. The anticipation of all possible combinations of scenarios the bot could encounter is the key to configuring a successful automation flow using RPA.”
KC also expects that teams deploying RPA solutions in the year ahead will begin to better understand the difference between RPA and AI. While RPA fits under the AI umbrella by some definitions, it’s not on its own “smart” or capable of making complex decisions.
“Those who have seen an RPA bot in reality know the difference. RPA is for mimicking human interaction [with a machine],” KC says. “A bot simply follows instructions based on a series of [rules-based] decisions, whereas AI has a mind of its own and is capable of making decisions on its own based on the training set given to it. The decisions an RPA bot has to take must be configured beforehand.”
[ See our related read: How to identify Robotic Process Automation (RPA) opportunities. ]
3. Intelligent automation will get another turn in the spotlight
The phrase “intelligent automation” isn’t new; it has even experienced an earlier “vogue” phase. Definitions vary, but it usually refers to the pairing of process automation with more cognitive AI disciplines like machine learning.
Expect intelligent automation – as well as variations like “cognitive automation” – to be popular anew in the year ahead, particularly as teams gain a more practical understanding of the limits of standalone, rules-based RPA.
“In 2020, tech leaders will augment their initial RPA efforts with a convergence of rules-based and AI-powered automation via a fully integrated intelligent automation approach, enabling them to find new value in the way of workforce capacity and operational efficiency,” says Chris Huff, chief strategy officer at Kofax.
“As efficient collaboration between digital workers and human talent becomes more vital, digital workforce management and governance will become increasingly prevalent. All in all, intelligent automation’s poised to take a significant leap in 2020 and deliver tangible results to organizations that make automation a key component of their digital transformation.”
Two more trends deserve attention: A changing IT/HR relationship and a growing emphasis on RPA for security tasks. Let’s explore:
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Your first 3 items mirror what has been going on (and continues to be so) with Software Test automation (or Automation in Testing, AiT). Adoption of automation tools for software testing has been growing for years, with the last 10 plus showing the biggest increase due to the acceptance and implementation of Opensource tools such as Selenium.
And this leads into the second item you list, and its inherent issues. The problem with over-inflated expectations of the tools and process, what I call "Automagic" (I'm know for the saying "It's Automation, Not Automagic!"), has always been around. Companies jump into implementing the automation tools without understanding the process and effort involved. Thus they set themselves up for failure due to ignorance. I've already heard about this happening with RPA implementation. As you mention they try to do too much too soon without understanding the real problem they are trying to solve. One of your other articles covers this in more detail.
Finally, the promise of Artificial Intelligence (AI) and Machine Learning (ML) is becoming important for both AiT and RPA tools. How this will pan out is to be seen, but it is promising in that AI in the tools will help to improve their capabilities to increase coverage of usage scenarios of the system/process being automated. This can lead to more robust and reliable automation.
Otherwise, I like this article. I'm starting to switch from AiT to RPA, and as part of that I'm seeing some things repeated with RPA implementations that I've dealt with for over 25 years of AiT work. Hopefully the business side will be a bit more cognizant and strive to do a better job of implementing RPA tools/process.
Excellent article really reflects what is in my book Business @ the speed of bots (on amazon)
Would love to get your feedback on this
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