Robotic Process Automation (RPA) – What Is It Actually?

December 18, 2023

In today’s highly competitive business environment, optimization is one of the most important keywords. –There seems to be an endless list of business components that require constant adjustment to become more efficient, productive, and powerful. These components include (but are not limited to):

• Improved cost efficiency
• Process optimization
• Procedure optimization
• Increased customer satisfaction
• Marketing optimization

In this complex area of business, one technology has emerged in the last few years that is now used as a standard method in many companies. Robotic Process Automation (RPA) can provide new answers to the questions of thousands of entrepreneurs across a wide range of industries. For example, consider the question “How can I achieve more in the same amount of time with the existing number of employees?” RPA is a core technology that provides a variety of solutions for a wide range of issues.

In this article, we first create a basic understanding of Robotic Process Automation. Once the first step is taken with RPA, exciting new possibilities follow with Machine Learning, Pattern Recognition and cognitive AI components.But for now, let us start with the main component on the journey to a digitized and fully automated business: Robotic Process Automation (RPA).

What is RPA?

Robotic Process Automation (RPA) is a technology or collection of techniques that makes it possible to imitate human input and actions on computer applications. With the help of an RPA tool, we can have a digital assistant perform all the inputs on the computer that you or your employees would otherwise do with a mouse and keyboard. Thus, this bot, which runs as a software on the computer, can execute entire process chains.

Unlike simple macros, an RPA bot is not limited to a single application. It can switch back and forth between any program and application. It can enter and read data almost anywhere and perform calculations and make simple decisions. For complex decisions (but not limited to those), an RPA bot can interact with humans to, for example, make a request or take one last look at a processed operation before it is completed.

The possibilities are endless. It can be said that an RPA bot can operate a computer just like a human. For monotonous, repetitive tasks, an RPA bot shows its strengths. For creative tasks and complex decisions, the bot prefers to leave it to your employees.

What are the benefits of RPA?

In addition to some obvious benefits – such as faster, error-free, and high-quality work results – RPA bots also bring advantages that may not be immediately apparent. For example, as a company, you can continuously improve your Data Quality and achieve higher customer satisfaction through faster response times. But, your employees can also benefit from the RPA bot. The bot can perform onerous,  monotonous tasks for you. This allows your employees have more time for complex, strategic or creative topics. The bots can also function as personal assistants to your employees and support them in their work.

Our clients are already benefiting from these advantages. With RPA, we have raised their Data Management to a new level of quality and efficiency. Security is also improving due to the accuracy and reliability of RPA bots. Data no longer leaves its clusters and is automatically encrypted, which render data leaks a thing of the past.

Automate inefficient processes efficiently?

Automating a process does not mean a simple hand-over to an RPA bot. Even if this is still what clients think from time to time, experience recommends more planning effort into one’s process optimization. A lean and efficient process significantly enhances the aforementioned benefits of automation.

Furthermore, it is not uncommon for people to include process steps to make their work easier or to use assistance programs for orientation. A bot does not need these either to achieve the same result. In addition, an RPA bot has different possibilities than a human. It can request and process an entire dataset via a single API (application programming interface) call or search for an intermediate result from an extensive Excel spreadsheet buried in the vastness of SharePoint.

Such circumstances – and many more – should be considered for each process during optimization of a prior process and, if necessary, shortened or changed before sending your digital RPA assistant to work.

The future of RPA

The future of RPA is the future of software automation. This is because, as explained earlier, RPA is the core of a technological development that is currently summarized under the term “hyperautomation”. The goal of this development is to expand automation into more and more application areas to scale faster and to make automation methods even more intelligent.

Today, we can assume that there are 4 maturity levels. The first two are technologically already fully developed, but level 3 – and in particular, level 4 – are still in the researched phases. They are not yet very differentiated or in some cases not even standardized. Most companies are at a level 1 or 2, where RPA bots control processes in collaboration with human employees or do their work completely autonomously – free of human intervention, review or care – 24 hours a day, seven days a week.

Some companies have already gone one step further and handed over processes entirely to their digital assistants – so-called end-to-end automation. This means that the bots can “estimate” the nature and scope of work to be done and scale their “manpower” accordingly. This way, tasks are completed independently and fully in perfect quality, even in a dynamic environment. These robot setups are usually managed in the Cloud.

Level 4 also includes cognitive capabilities often referred to as Artificial Intelligence (AI). Here, for example, the bot is supposed to grow beyond its abilities in simple text recognition (optical character recognition [OCR]) by improving its pattern recognition via advanced Machine Learning algorithms. It learns from past results to perform even better. This way, software robots can already interpret voices or structure data to perform contextual analysis or make decisions.