By Yuriy Shubin
The article was originally published on ITProPortal.
The idea of implementing robotic process automation (RPA) on our projects hit us several years ago. Those days I was working on geoinformation projects. Together with my team, we worked on the delivery of high-quality data for a large-scale mapping company.
Working with such a client always implies, continuous improvement. Researching and searching for ways to boost efficiency was an integral part of our job. Browsing the studies, reviews, approach descriptions and all other writings, I stumbled upon the article about the evolution of robots. The author was talking about jobs that will disappear when robots come. There was a long list of reasons explaining his every point in details. Though things looked quite reasonable, I could not get rid of Terminator pictures in my mind. Just for fun I googled the topic and came across the RPA term.
I guess that was the moment when RPA stepped in my life. I got really interested and wanted to cheer up the neurons. It was a great chance to get out of the comfort zone and try something new. Moreover, AI (Artificial Intelligence) and ML (Machine Learning) services in GIS industry were actively adopted. I thought it worth learning.
What is RPA?
Robotic process automation (RPA) is the practice of automating routine business processes with software robots. Automation can include data processing, the interaction between different digital systems and legacy silos (like scanned docs or software with API capabilities – RPA works on GUI level as well as API level), and many other things. By building algorithms on a special platform, the developer gives the robot clear instructions and configures it to perform the tasks. If developers add machine learning functionality, the instructions may become less clear. The robot acquires a certain freedom of action.
My first steps in RPA began with attempts to change the approach to the current front office tasks of the GIS project and internal back-office processes of the company.
We trained a lot on simple routine tasks. Recruiting bot was our first case of RPA implementation. The bot searched the CVs of candidates in the database per certain parameters, like tech skills, soft skills and so on. It collected the information and put it to the company template. As a result, the recruiter received an analyzed candidate’s CV. The recruiters didn’t need to do the manual search. The bot did it for them.
After that, we did a few more RPA projects. Our experimental bots reduced the number of employee manual operations. Eventually, we became the RPA department.
The department began with two enthusiasts. A year later 5 more people joined us.
Today we work on several client projects and the team continues growing. Therefore, we run workshops for the newcomers to help them get on our RPA board. Sometimes they also have to study on the project go. A good example of a project where newcomers learned to create robots was a website with a collection of music events.
When the client came to us, they created the music event collections manually. They had a big team who googled events around the world, translated the descriptions and posted on the website. When we developed a robot for them, the need for such team disappeared. They did not need their people to spend time on routine search and posting. Now the robot with integrated artificial intelligence looks for music events itself and translates the descriptions.
Developing a bot for an automotive company was also a great experience for us. The bot started interaction with the car owner after the purchase. It contacted the person SMS, messenger or email and listed its services. The bot provided assistance in a real-time mode. For example, if the car owner didn’t know what kind of light was on the dashboard, the bot explained. It could remember to change the oil or refuel and even book a slot for car maintenance in the service center.
During these and other projects, I noticed some think that RPA is the same as Artificial Intelligence and Machine Learning. It is a mistake. If we use analogy, then RPA is like hands, while AI and ML is brain. The robot cannot deviate from the given rules. It performs them precisely. This is the essence of RPA. The idea of AI and ML technologies is to teach the machine make decisions autonomously, moving away from the established instructions. However, robotics may include elements of AI and ML.
Why is RPA trendy?
So, why do the world largest companies implement RPA?
Robotization eliminates the possibility of error during task performance. The program follows the defined rules. It cannot fail them, unlike a person, who may skip small things that impact the whole process in the future.
Robotization helps reduce costs. What will the company get when investing in the development of a high-quality robot? The robot is not a human. It is a machine that does not need a reward for the completed task.
What is the difference between RPA and the development of special software? RPA is faster in its implementation since it refers to the low-code approach to development. It means the developers use ready-made modules to create solutions. A minimal amount of code is written manually while most of the monotonous tasks get automated.
Therefore, it has a shorter term of return on investment. RPA implementation does not require restructuring of current company processes.
Why is RPA a revolution in the automation of business processes?
Despite the fact that the RPA market is still small, it is growing steadily. According to the HfS research the global market of RPA will reach $1,2 billion by 2021. By that time, 40% of large organizations would be using the RPA solutions with AI and ML features.
In a nutshell, the competitive advantages of RPA are apparent. Implementing RPA, companies get the opportunity to increase the process efficiency and reduce operating costs. Business owners can be sure that tasks completion is under control since bots are not created to deviate from rules. And the last but not the least, RPA implementation allows focusing on mission-critical things and investing in qualified employees who cover business-critical things.