How do businesses handle high-volume, repeatable tasks that requires hundreds of human hours to perform, like customer service? This is where robotic process automation (RPA) comes along in order to mitigate issues of customer volume and task repetition.
RPA is therefore the use of software with artificial intelligence (AI) and machine learning capabilities to handle such tasks. According to TechTarget, these tasks may include inquiries, calculations and maintenance of records and transactions.
A report from Global Market Insights sees RPA even becoming a $5-billion market by 2024. Henceforth, what will mostly drive RPA market growth by that time will be greater organizational adoption of robotics technologies. In particular, RPA will help organizations enhance their capabilities and performance, as well as boost savings on cost.
Research and Markets data meanwhile reveal that the industrial robotics market alone will reach $40.8 billion by 2024 from $18 billion in 2018, with a 14.1% compound annual growth rate (CAGR) between 2019 and 2024. According to the market research firm, the major contributors to this growth will be the rising penetration of the Internet of things (IoT) and investments in robotics across regions.
RPA technology usually consists of software robots (or bots) that can in fact mimic a human worker. In particular, RPA bots can log into applications, enter data, calculate and complete tasks, and then log out.
At present, practitioners divide RPA tech into three (3) broad categories: probots, knowbots and chatbots.
However, RPA software is not part of any organization’s IT infrastructure. Instead, according to TechTarget, RPA sits on top of IT. Therefore, it enables a company to implement the technology quickly and efficiently — all without changing the existing infrastructure and systems.
So what distinguishes RPA from traditional IT automation? “It is (therefore) the ability of the RPA software to be aware and adapt to changing circumstances, exceptions and new situations. Once RPA software has been trained to capture and interpret the actions of specific processes in existing software applications, it can then manipulate data, trigger responses, initiate new actions and communicate with other systems autonomously,” TechTarget explained.
“Basic RPA was useful for automating repetitive tasks, (however) the rules-based approach worked best with structured data,” TechTarget added. “The cognitive variety, however, would add optical character recognition (OCR), natural language processing (NLP) and machine learning to handle semi-structured and unstructured data — expanding the efficiencies of RPA to a wider range of enterprise activities.”
Earlier in June 2019, analyst Gartner reported that the RPA software market grew by 63.1% in 2018 to $846 million. Therefore, this growth making it the fastest-growing segment of the global enterprise software market. Gartner hence forecasts that RPA software revenue will reach $1.3 billion in 2019.
For their digital transformation journeys, companies can benefit from this tech for the following:
Top applications therefore include:
Generally, it evolved from three (3) key technologies: screen scraping, workflow automation and artificial intelligence.
Screen scraping involves collecting screen display data from legacy applications, wherein a more modern user interface can display that data. Workflow automation eliminates the need for manual data entry, thereby increasing order fulfilment rates, plus speed, efficiency and accuracy.
AI, meanwhile, involves the ability of computer systems to perform tasks that normally require human intervention and intelligence. Today, its software is particularly useful for organizations having many different and complicated systems that need to interact together fluidly.
For instance, if an HR electronic form misses a zip code, traditional automation software would flag the form. Then an employee would handle this issue by looking up the correct zip code and entering it on the form.
But with RPA tech, such software with the ability to adapt, self-learn and auto-correct could handle this issue. Then it would interact with the HR payroll system even without human assistance.
So what do enterprises need to consider and look for in RPA tech, according to TechTarget?
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