Robotic Process Automation (RPA) has gained high attraction in recent research and in industry. However, it is often not clear how RPA differs from earlier attempts to automatize work. This will now be clarified.
Why RPA exists
Robotic Process Automation (RPA) are software agents that imitate human actions by rule-based processes. RPA tools aim to automate less complex, but frequent human work. RPA robots utilize the user interface to capture data, work solely on the Presentation Layer, and manipulate applications just like humans do. They interpret, they trigger responses, and RPA tools communicate with other systems in order to perform on a vast variety of repetitive tasks. Only substantially better: an RPA software robot never sleeps, and makes zero mistakes – if implemented correctly.
According to a recent report published by the consulting house McKinsey and Company on emerging and disruptive technologies, it is predicted that automation technologies, such as 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 technology platforms. RPA is expected to become a standard for positive business outcomes and performance.
How RPA has arisen
RPA was seen by many proponents as a game-changing technology. However, it is claimed in the literature that traditional automation has been around for quite some time, and thus it’s not always clear how RPA compares to other technologies .
By proponents, RPA was seen as a game-changing technology. But a common debate among the automation community was, whether RPA was a new development, or if it should instead be seen as simply an extension of its predecessors, which are: Screen Scraping, Workflow Automation, and Artificial Intelligence (AI).
Screen scraping emerged before the internet existed, and was intended to be used to analyze text from computer screens. Nowadays, it is predominantly used to extract data from the web.
Workflow automation exists 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.
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 judgment, decision-making ability. The use-cases of AI 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.
How RPA works nowadays
The emergence of the term RPA can be dated to early 2000. RPA is developing technology and still relies on the technologies mentioned above. It just elevates these technologies to a new level, advancing their capabilities in a significantly improved way.
Combination of techniques
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 is a combination of AI and workflow automation. RPA allowing employees in a company to configure computer software or a robot to collect and extract knowledge, recognize patterns, learn and adapt to new situations or environments. And, automation and AI are competent technologies on their own, but the collaboration between RPA and AI allows for complex capabilities to emerge.
Pros & Cons of recent RPA
Like any other technology, RPA has its advantages and limitations. The advantages are as follows:
- Reduces human efforts and errors.
- Improves productivity by saving cost and time.
- It offers real-time visibility into bug discovery.
- Suitable for the usage by non-technical persons too, as no programming skills are required to use it.
- Easy to automate a large number of processes.
- Feature of tracking the defects for each test case.
The disadvantages instead:
- Need to reconfigure the robots for small changes in the application.
- Dependency of bots on the speed of application.
What can we expect from RPA
It is expected that the combination of RPA solutions with even more intelligent technologies has great potential for widespread adoption across all industries. Such new RPA attempts are named Smart RPA and are the main concern in recent research on RPA .
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. The new RPA platforms will be 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 judgment and creativity to their work, which will essentially allow companies to 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. 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 will have the potential to make companies more agile and responsive, which is crucial in today’s increasingly global and complex marketplaces.
The integration features such as natural language processing, text analytics based on semantic analysis and machine learning into the RPA platform will help companies become more efficient by outperforming repetitive business processes based on the treatment of large volumes of unstructured information: text email and attachments, forms, record data, customer’s interactions, and call notes, purchase orders, etc. Organizations in any vertical market, especially in heavily regulated industries such as financial services, insurance, healthcare, telecom, and retail, will have the opportunity to automate more rapidly their business processes and extend automation to new areas, increasing the scalability of operations and minimizing error rates, thanks to the accuracy in managing data that only superior cognitive capabilities can offer.
McKinsey & Company (2019). Driving impact at scale from automation and AI.
 Nick Ostdick, TOPICS: Robotic Process Automation, RPA, RPA Impact, RPA history, RPA Journey
 Van der Aalst et al. (2018). Robotic Process Automation, Bus Inf Syst Eng, Springer Wiesbaden.