The future of RPA

Robotic Process Automation (RPA) is a proven way to automate processes without making significant changes to your application landscape. With the rapid development of AI, RPA is gaining new momentum, promising to transform the future of RPA into intelligent process automation. This makes RPA increasingly relevant as an accessible automation solution.

What is RPA?

Robotic Process Automation (RPA) is a technology specifically designed to perform standardized and repetitive tasks that would typically be handled by human employees. These repetitive tasks are executed within existing applications. Examples include checking websites for updates, responding to emails, manually copying data from one application to another, and more.

One key feature of RPA is that it does not require programming to implement the technology. Additionally, an RPA application does not need access to the underlying software code or databases it interacts with, meaning no integration with existing software is necessary. These attributes make RPA highly appealing. However, it is worth noting that RPA itself is built using code-based software.

So, how does RPA work? Deloitte offers a definition that sheds light on this: “Robotic Process Automation (RPA) is defined as the automation of rule-based processes with software that utilizes the user interface and can run on any software, including web-based applications, ERP systems, and mainframe systems.”

RPA is a rule-based solution that mimics human actions in software. These rules must be predefined, which highlights the types of tasks or actions for which RPA is particularly suited: opening emails, moving files, copying and pasting data between locations or applications, scraping screens or websites, performing calculations, extracting structured data from documents (such as customer or invoice numbers), or following ‘if this, then that’ rules.

Background

The origins of Robotic Process Automation (RPA) can be traced back to screen scraping technologies that emerged in the 1990s. The concept of RPA was likely first coined in 2003 when Blue Prism launched the first software specifically designed to automate repetitive tasks in business processes. This software employed a virtual, software-based “robot.” The term RPA was introduced to clarify that it was not referring to actual robots or traditional forms of automation.

RPA vs. Software Development

RPA’s role in the software world is well-defined. RPA is not intended to replace existing software but to enhance its capabilities. Often, RPA is a suitable solution to facilitate limited integration between existing applications. A good example of this is the order processing workflow. When an order is received in Application A, a set of products needs to be ordered through System B. Once the products are ordered, transport must be arranged via Application C, and System D is needed to add metadata to the invoice. As long as these processes follow a standard procedure, RPA can ensure that all the systems work together without human intervention.

This is known as system integration, but there are multiple ways to achieve it:

• Modify the various systems (change the code in all systems);
• Create new software that connects the different systems (such as APIs);
• Implement an RPA solution that links these systems by using data across the different systems.

For the first two solutions, you need to code; for the third option, RPA providers claim that coding is not necessary. Instead, as a user of an RPA solution, you can build software robots that can be configured to perform routine manual tasks automatically.

Building workflows

Building RPA applications yourself involves creating workflows that contain decision-based actions. This is usually done using drag-and-drop interfaces, meaning no coding is required. As an alternative to manually creating workflows, some RPA tools also offer the ability to record actions performed by a human within an application. The RPA solution can then convert this information into workflows that can automate tasks. Sometimes, advanced RPA tools also allow you to develop plug-ins or integrate with other solutions, enabling you to access APIs or invoke functions from external DLLs (Dynamic Link Libraries), which are files containing specific code and data designed to perform particular functions or procedures.

As mentioned, RPA often provides a faster solution to automation challenges—but you need the right competencies to implement RPA solutions effectively. This typically requires process and domain knowledge, as well as the ability to think logically and analytically.

Using RPA for automated software testing

RPA and Test Automation have some similarities, particularly in UI test automation, as both tools enable the automation of UI interactions. To do this, these tools replicate the actions instructed by the user (with the tester essentially acting as the user). However, RPA and automated testing are not the same. RPA serves a broader purpose that extends beyond automated testing. Moreover, automated testing usually requires testers to write scripts using a programming language, while RPA is specifically designed to automate tasks without the need for coding.

That said, there are instances where automated testing and RPA intersect. One such example is Robot Framework, a generic open-source automation framework. It can be used for test automation as well as generic robot process automation, such as RPA. Another example is the automated testing of RPA solutions themselves, which is of course possible. One of the most advanced tools for this purpose is Test Suite from UiPath. Test Suite can also be used for testing web applications. The tool provides capabilities for automated test management, including test planning, monitoring requirements, and generating issue reports. You can integrate this application with issue ticketing systems such as Atlassian Jira, and it offers functionalities for deploying, securing, and managing test robots.

RPA will become smarter with AI in the future

As mentioned, RPA is particularly suited for handling repetitive, rule-based tasks that previously required human effort. However, the ability to learn is not part of RPA’s functionality. If something changes in the automated task (for example, if a field in a web form shifts or if the UI changes), a software robot will not automatically detect or resolve the issue. This makes RPA vulnerable to changes in processes, potentially causing the solution to fail.

However, there is a connection between RPA and AI. AI, which is currently developing rapidly, will certainly complement RPA solutions in the future. If an RPA bot is programmed to perform a specific task but can also “learn” from the context and adapt its behavior, this leads to Intelligent Process Automation (IPA). These types of robots are referred to as AI agents, capable of operating as “digital employees.”

Finally: The future of RPA

In the future, RPA will undergo a new development with the addition of AI. AI-based RPA solutions will not only be better at handling unstructured information and context but will also be able to build workflows on their own and handle deviations or changes more intelligently. This makes them less vulnerable to the dynamics of business processes. Nevertheless, there will still be plenty to do for “traditional” RPA, especially when robust output is required.

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