What the Future Holds for Robotic Process Automation: The Next Chapter

As we’ve discussed in earlier posts, robotic process automation (RPA) has made significant strides in recent years. By rapidly progressing beyond its initial focus on automating simple user interface (UI) tasks, RPA today has “come of age” to become a multifaceted and highly scalable automation platform.

But what about Gartner’s talk of moving beyond RPA to achieve “hyperautomation?” In practice, it turns out, RPA itself is rapidly becoming the universal automation platform that organizations require. It is doing this by encompassing and integrating the two main categories of automation: UI automation and application programming interface (API) automation.

It makes sense to have a common platform that spans both UI and API automation given that thousands of workplace processes have elements of each. And RPA, coming from the UI side of the automation spectrum, has a critical characteristic that positions it as the best platform for delivering “universal” automation: RPA emulates how people perform tasks.

Emulating human activities is more challenging – and ultimately more powerful – than simply automating computer-to-computer processes by connecting different systems via the APIs they expose. Put another way, it’s relatively simple to expand a UI-focused RPA platform by adding API capabilities to it, but not so simple to move in the other direction.

As RPA platforms evolve, they will continue to expand their scope and their reach, with the addition of API automations being just one of many RPA evolutionary tracks. One core area of change, for example, will be to keep bolstering the enterprise-grade credentials of RPA platforms. As automation becomes a distinct and pervasive layer in the IT stack, RPA solutions must deliver all of the reliability, availability, security, scalability, and manageability traits required of such mission-critical platforms.

Of course, given its human-emulation mandate, RPA will also continue to exploit different artificial intelligence disciplines. In addition to machine learning, some RPA solutions are already leveraging everything from document understanding to computer vision in order to more easily and more perfectly automate existing tasks and processes.

Pioneering RPA provider UiPath is corralling many of these advanced technologies to deliver a capability it calls “continuous discovery.” In essence, UiPath has positioned its triad of discovery capabilities – UiPath Task Capture, UiPath Process Mining, and UiPath Task Mining – to constantly monitor human and IT system activities to identify automation opportunities. Once identified, IT developers, as well as citizen developers, can make use of the extensive suite of tools UiPath provides to easily and rapidly create automated solutions.

More fundamentally, as it plots RPA’s future, UiPath is aiming to extend its ability to deliver “semantic automation.” Meeting this objective involves giving the RPA platform itself some of the same type of understanding and knowledge that comes naturally to the person doing any given task.

For example, much like a person, a document understanding technology may be able to identify a form as an invoice or as a receipt, and may also recognize the different types of data within such forms. With this type of semantic knowledge in hand, an RPA platform can significantly reduce much of the up-front busy work developers otherwise face when building automations.  Semantic automation, in short, will both speed the creation of automations and improve their quality and accuracy.

UiPath has devoted itself to continually advancing every aspect of RPA and has already created a platform capable of delivering the hyperautomation and mission-critical capabilities organizations require.

To view a discussion between UiPath CEO and its Executive Vice President of Product and Engineering about the future of RPA – including the company’s pursuit of semantic automation.