According to a report by McKinsey and Company on emerging and disruptive technologies, it is predicted that automation technologies, such as robotic process automation (RPA), will have a potential economic impact of nearly $6.7 trillion by 2025. The automation market is expected to have the second largest economic impact of the technologies considered (e.g. 3D printing, cloud technology, autonomous vehicles) behind the rise of the mobile Internet for smartphones and tablets. Given these statistics, it’s obvious the growth of RPA is happening quickly, and RPA is poised to grow into one of the leading technological platforms and is expected to become a standard for positive business outcomes and performance.
While RPA technology has evolved quickly during the last few years and essentially taken the automation technology industry by storm, it begs the question: just how new is RPA? Where did it come from? What are its origins?
Traditional automation has been around for quite some time, but it’s not always clear how RPA compares to these other technologies. Does RPA share some similarities with its predecessors? If so, how are they similar and where do they diverge? How has RPA grown and matured in such a short time, and what are the essential markers or signposts in this evolution? To answer these questions and trace the history of RPA, we’ll examination the evolution of RPA, its origins and development, the proliferation of this technology, and what can be expected of RPA in the future.
RPA is seen by many proponents as a game-changing technology, yet a common debate among the automation community is whether RPA is a new development or if it should instead be seen as simply an extension of the technologies that came preceded it. To understand where you are now, you have to examine where you’d been. So it stands to reason: in order to appreciate the current state of RPA, we need to first understand what came before it, starting primarily with developments after the 1990s.
Three key predecessors of robotic process automation include:
Screen scraping technology saw its first days before the development of the Internet where it was was the first technology that created a bridge between current systems and incompatible legacy systems, and it has more recently been used to extract data from the web on the presentation layer. While there certainly are benefits of screen scraping over manual labor, screen scraping is also limited in that, for example, the software’s compatibility with existing systems and applications varies and its reliance on the underlying HTML code of websites is makes it difficult for the average business user to understand. For this reason, many organizations sought more adaptable, versatile technologies.
While the origins of the term “workflow automation” dates back to the 1920s during the industrial era and emergence of manufacturing, the term has become more frequently used since the 1990s. Workflow automation software can, for example, aid in order processing by capturing certain fields of interest, such as customer contact information, invoice total, and item ordered, translating them into your company’s database, and notifying the corresponding employee. This kind of software eliminates the need for manual data entry and increases order fulfillment rates, so advantages include increased speed, efficiency, and accuracy.
Despite earlier advances in robotics, the term “artificial intelligence” was not coined until 1956 at conference at Dartmouth College. Artificial intelligence (AI) refers to the capability of computer systems to perform tasks that normally require human intervention and intelligence. The tasks that can be completed by AI machines are those that were previously highly dependent on humans for their judgement and decision making ability and include, for example, financial planning and fraud detection. While AI can be expensive, the benefits of AI include increased accuracy and precision in tasks and replacement of tedious, time-consuming manual labor.
As saying the goes, the whole is greater than the sum of its parts. While each of these advancements and breakthroughs in automation technology was somewhat seismic in its own right, the evolution and deployment of RPA and its ability to combine, refine, and reimagine certain aspects of each of these technologies is what truly makes RPA such an impactful technological platform.
While the technology was developing for some time before, the emergence of term “robotic process automation” can be dated to the early 2000. RPA is a developing technology, but, as we just briefly discussed, it still relies on the technologies artificial intelligence, screen scraping, and workflow automation and elevates these technologies to a new level, advancing their capabilities in a significantly improved way.
RPA is highly dependent on both screen scraping and workflow automation, but in ways that provide more benefits for the business users. Rather than being dependent on code as is required for screen scraping, RPA software allows users to establish automation and manage workflows using drag and drop features in a visual way that can be entirely independent of coding knowledge. Also unlike many web scraping tools, some RPA software makes use of optical character recognition (OCR) technology to adapt to changing websites without requiring intervention from a human employee.
RPA also builds on artificial intelligence. In fact, Deloitte suggests that RPA is the combination of artificial intelligence and automation:
“Robotic Process Automation (RPA), a synonym to AI, is the application of technology allowing employees in a company to configure computer software or a ‘robot’ to reason, collect and extract knowledge, recognize patterns, learn and adapt to new situations or environments.”
In addition, automation and AI are competent technologies on their own, but the collaboration between RPA and AI allows for complex capabilities to emerge. While automation is able to streamline repetitive, rules-based business processes, the software is largely unable to deal with exceptions on its own or make decisions outside of how it has been programmed. On the other hand, AI can be used, even in addition to automation software, to approach tasks that require more complex decision-making and analysis, such as natural language processing, recommendation services, and online customer support. The coordination between the aforementioned technologies also points to what is possible in the future of RPA.
The big question is: Where is RPA headed? What does the future of RPA look like?
The development of RPA technologies has already come a long way from the days of simple screen scraping, and RPA continues to transform how many companies approach their business activities, especially when it comes to scaling and streamlining processes. It’s a superior technology that has made its way to the forefront for the benefits it provides and the ease at which these benefits can be obtained. Yet, the market is expected to continue to evolve even further and more innovative RPA solutions are predicted to emerge.
Industry analysts expect the combination of RPA solutions with even more intelligent technologies has great potential for widespread adoption across all industries. Machine learning and cognitive computing , for example, are technologies that involve learning on the part of the computer or software beyond their initial programming, much like a human would respond in similar scenarios. These platforms are able to deal with unforeseen errors and exceptions in a business process, learning from and adapting based on previous actions and experiences. Unlike traditional automation, they are able to apply judgement and creativity to their work, which will essentially allow companies automate enhanced visibility, transparency, communication, and collaboration across their value chain.
With the addition of RPA to increase speed and provide process automation support, the journey of machine learning and the development of even more intelligent technology will only be rapidly accelerated. The days of cognitive automation are on the horizon. Software robots are already able to automate simple, repetitive processes, and through the combination of RPA with these intelligent platforms, they will soon be able to improve their own performance and make complex decisions with little intervention or programming. This has the potential to make companies more agile and responsive, which is crucial in today’s increasingly global and complex marketplaces.
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