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One of the fastest growing segments of the software market is robotic process automation (RPA), and the sector shows no signs of slowing down. Organizations are increasingly attracted to RPA as a good source for automating routine human tasks that involve data.
Because of that, the RPA segment saw revenue growth of 31% in 2021, reaching a total value of $2.4 billion. Compare that to the overall worldwide software market, which saw 16% growth rate during the same time, explained Gartner vice president, analyst Saikat Ray.
Helping to drive RPA growth is the fact that is has become “industry agnostic, and truly horizontal across many verticals,” Ray told VentureBeat. “Although we have seen huge adoptions around finance, banking, insurance, contact centers, manufacturing and healthcare.”
There are three major trends we can expect to see in the RPA sector over the next two or three years, Ray said:
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1. APIs complement screen scraping
“While RPA vendors traditionally focused on automating tasks via screen scraping, newer entrants to the RPA market — including integration vendors and large software vendors — are successfully promoting an API-first approach for process automation,” Ray explained. “In response to this increased competition, many pure-play RPA vendors are continuously enhancing their API integration capabilities so that they can offer customers both UI scraping and API-first automation approaches. We have observed the emergence of broader automation platforms that offer a wide range of integration capabilities.”
To confirm how competitive the RPA market is, Ray said the vendor landscape is constantly changing. The market includes more than 60 vendors as of mid-2022, and has experienced frequent mergers and acquisitions during the past year.
2. Fluid vendor landscape — frequent M&As
“The RPA market is growing rapidly and is constantly in flux,” Ray stressed. “Incumbent RPA vendors are jockeying for position by evolving their products, while large vendors in adjacent markets are beginning to offer RPA capabilities.”
Gartner research finds that the 10 largest RPA vendors account for more than 75% of the market (up from 70% in 2021), and that the three largest vendors still account for 52% (same as last year). The entry of large vendors such as IBM, Microsoft, Salesforce (MuleSoft) and SAP is rapidly transforming the market landscape, Ray explained. These mega-vendors are targeting their existing customer bases with aggressive offers and low entry price points.
3. Beyond RPA to hyperautomation
“Software engineering leaders and business technologists are scaling beyond tactical automation to deliver greater efficiency, efficacy and business agility,” Ray said. “RPA technologies play an essential role in hyperautomation — a discipline that helps to combine several technologies in an orchestrated manner to deliver end-to-end, intelligent, event-driven automation.”
What is RPA good at, and bad at?
Taking its rapid growth into account, there are some things that RPA is good at doing, and some that it is not, Ray said.
Good at doing
“RPA is a good tactical alternative to automate a routine human task that involves data transcription between applications when APIs or other forms of integration are not available. RPA emulates human actions (key strokes, mouse clicks) and follows the same sequence of activities to automate a task,” Ray said.
RPA at the core is an orchestrated screen-scraping technology. Any business processes that have high volume and frequency, smaller scope, low complexity, and are routine and predictable in nature may qualify for RPA, Ray says.
Moreover, RPA is most useful when it is used as the last mile of integration. That is, where an API or other integration methods are not typically available for data transcriptions involving legacy apps, spreadsheets, homegrown applications and/or complex ERPS/CRMs. According to Ray, popular applications of RPA are in finance, accounting, HR, administration, operations, contact center, supply chain, procurement and sales.
Not good at doing
“RPA is not-so-good at automating applications and/or processes that change frequently,” Ray said. “It cannot apply judgments and/or dynamic decision-making. It is also brittle to the effect of screen changes within an application. For example, typically a SaaS-based application is not better automated via RPA. API-first strategies are recommended.”
Finally, an organization is most likely to be successful with implementing RPA by good data governance with a well-defined organizational structure, roles and responsibilities.
“Technical resources should have skillsets in developing RPA scripts (typically .Net or Java/Angular JS and Python). Also skillsets to manage RPA orchestration tools, monitoring and administering dashboards, infrastructural skills to deploy, troubleshoot and monitor the runtimes,” Ray said.
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