Businesses across the globe, even as they continue to grapple with pandemic-related changes, are doubling down on scaling automation. The goals is to streamline existing business processes while continuing to deliver value to their customers.

But IT leaders need to ensure they select the right technologies that will perform the functions they wish to optimize, and that’s where implementing a hyperautomation framework comes in. 

Also see: DevOps, Low-Code and RPA:  Pros and Cons 

Building Blocks of Hyperautomation

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 is changing the way work gets done—56% of organizations are using between four and 10 initiatives at once.

An increased adoption of the hyperautomation framework will be an important part of the technology landscape in the coming years. The following building blocks that enable hyperautomation are the foundational trends to watch out for in 2022.

Also see: What Does 2022 Hold for Intelligent Automation

Robotic Process Automation

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

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.

But in order to effectively leverage AI technologies, access to structured data is a critical requirement. For example, with structured and machine-readable data, an insurance company can test AI platforms in a handful of areas across its organization to see if it’s valuable first, and then implement it and roll it out in more venues.

Also see: Top AI Software 

Synthetic Data

Given that both RPA and AI technologies are only as good as the data they are supplied with, how can enterprises tap into machine-readable, structured data when it’s often locked away in unusable formats or shielded by customer privacy laws? The answer lies with a trend that is relatively new on the scene—synthetic data.

Synthetic data is information that’s artificially manufactured rather than generated by real-world events. It isn’t traceable to real customer information, and it can be used to train AI and machine learning models.

For example, in the healthcare industry, synthetic data can be used to overcome the challenges associated with protecting patient data and the privacy laws, like HIPAA. By feeding synthetic data into AI algorithms, the healthcare industry can better improve drug discovery, diagnosis and medical imaging, and accelerate its overall digital transformation.

However, in order to easily access data that synthetic data can be created upon, enterprises need to ensure they’re digitizing their data collection in the first place.

Also see: Best Data Analytics Tools 

Low-Code Process Automation Platforms

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 hyperautomation framework at scale.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Fusion Teams

Finally, a system and its processes are only as good as the people overseeing them; this is where fusion teams come in. Fusion teams are multidisciplinary teams that blend technology or analytics and business domain expertise and share accountability for business and technology outcomes. They’re another building block for enterprises to ensure the success of project deployment.

Similar to agile teams, fusion teams are made up of employees from a variety of departments with a wide breadth of knowledge. This allows the team to be more flexible and autonomous, and to drive faster digital delivery.

For this reason, having a fusion team in place can be the difference between the success and failure of scaling automation within an organization—and why it’s a trend to watch out for this year and years to come.

No Signs of Slowing Down

Hyperautomation is driving significant business changes in 2022, with no signs of slowing down. With 80% of CEOs increasing spend on digital initiatives this year and 72% expecting to shorten the implementation timelines, adopting some of these building blocks is a critical step in making better, fast-paced business decisions backed by optimal data.

Also see: Tech Predictions for 2022: Cloud, Data, Cybersecurity, AI and More

About the Author: 

Art Harrison, Co-Founder, Daylight