Hyperautomation Trends 2022: RPA, Low-Code, AI

The goal for many businesses is to scale automation while continuing to provide value. But it’s important not just any technology will do- they need the right tools in order to streamline their processes and give customers what’s expected from them. That means implementing a hyper-automation framework, which selects appropriate technologies that perform functions Optimally

One way of achieving this would be by doubling down on already existing strategies like scaling machine learning platforms or opting into cloud services such as Microsoft 365 Business suite but there are other ways too

Hyperautomation, according to Gartner, is “a business-driven, disciplined approach that organizations use to rapidly identify, vet, and automate as many business and IT processes as possible.”

It involves the choreographed use of several tools, technologies, or platforms, including:

Many organizations have already adopted hyperautomation tools, which are changing the way work gets done—56% of organizations are using between four and 10 initiatives at once.

RPA software first gained popularity in the early 2000s, notably in the banking and financial sectors, with proponents heralding it as the best solution for implementing automation at scale. However, RPA only performed well when being fed high-quality, structured data—something that many enterprises unfortunately lacked.

That’s because paper-based processes or legacy systems don’t provide usable data for RPA solutions to function properly. With the increased desire for end-to-end automation, the value of RPA has really been put into perspective, and it’s only one component of a larger story. Still, RPA is a critical component and worth investing in as long as proper integrations, solid data, and defined processes are in place.

Artificial intelligence has almost taken on a life of its own these days, with practically every organization touting their tremendous capabilities as being “enhanced by AI.” However, this overused buzzword can actually be another indispensable building block of scaling automation, if implemented properly.

Just as with RPA, AI can only achieve peak optimization when supplied with a huge amount of good data. Most enterprises have this data, although much of it exists in unstructured form or is of poor quality, since it was gathered via error-prone, paper-based forms.

If an enterprise has some of the initial hyperautomation building blocks in place, low-code process automation platforms can tie everything together by unlocking and capturing structured data.

Sitting on top of an existing tech stack, some of these low-code platforms can streamline data collection workflows. For example, paper-based processes can be rapidly transformed into digital experiences, often with citizen developers (with no formal IT training) at the helm.

Through the digital experience, organizations can ensure that error-free data is captured and passed on to systems down the line while minimizing the time and cost investment of executing a hyper-automation framework at scale.