It’s safe to say that 2021 was an exhausting year for organizations and their employees.

We’re coming up on two full years of the pandemic, which has highlighted the need for agile, cloud-based, and employee-centric business processes that enable organizations to rapidly respond to changing market conditions and customer demands. 

Enter hyperautomation, an augmented approach to automation that leverages technologies such as process discovery, process mining, low-code application development, machine learning, artificial intelligence, document intelligence,  robotic process automation (RPA), and more to streamline operations. In fact, Gartner named hyperautomation one of the top strategic technology trends for 2022, citing its ability to accelerate business growth and resilience.

As the technologies behind hyperautomation continue to mature, they’re unlocking new possibilities for new and legacy firms. For incumbents, hyperautomation is a valuable addition to existing digital transformation projects, allowing them to move quickly while undergoing other ambitious transformations. For smaller firms, hyperautomation allows them to do more with their existing resources, increasing both cost effectiveness and productivity. 

Here’s why hyperautomation is worth prioritizing in 2022.  

Hyperautomation accelerates digital transformation 

One thing technology leaders need to regularly remind themselves of is that most companies were not born in the cloud. Many Fortune 500 firms are in varying stages of their digital transformation journeys, especially if they’ve been around for a while. Oftentimes these companies aren’t dealing with one legacy system, but several – some new, some old, some highly customized, etc.

For these companies, digital transformation spans decades of system baggage that is hard to move, change, or replace. The speed in which these companies can transform is then negatively impacted by the complexity of these systems. Hyperautomation can be a powerful toolset for companies in this position. 

For example, hyperautomation can utilize process mining to analyze system logs and understand how processes actually run in an organization. It can also leverage process discovery to identify which mundane, repetitive tasks – such as swivel chair data entries across multiple systems – employees engage in. Once companies understand how old systems are used, teams can implement hyperautomation technologies to streamline processes. 

Also, for legacy systems without modern user interfaces or application programming interfaces (APIs), there can be tremendous power extracted from RPA to automate tasks and processes. Once data is captured correctly from an existing application landscape, it can be processed using AI/ML techniques to increase speed. This makes hyperautomation a strategic tool, allowing organizations to connect various data sources – whether they’re modern, legacy, or unstructured – analyze business processes, automate mundane tasks, and develop end-to-end solutions.

Hyperautomation’s opportunities are everywhere

It’s easy to think that everything is already automated, but that’s certainly not the case for many back- or front-office functions.

Let’s take a look at a typical front-office scenario. For customer service agents in the telecommunications industry, they often manage customer inquiries regarding billing. Customer service agents then have to pull the customers’ billing information from different CRM tools. Because most telecommunications firms have been around for decades, they tend to acquire various CRM tools that have to be queried individually. 

This creates “system debt,” which impacts agent productivity and extends call times, detracting from the overall customer experience. Hyperautomation can address this by using bots to retrieve data from older legacy systems that do not have proper API interfaces. Low-code application development and API connectivity can then spin up a single solution that seamlessly compiles all customer billing information in a modern experience, putting the information needed at the fingertips of customer service agents.

Additionally, hyperautomation use cases are also found in operations and back-office services. For instance, many document tasks, such as shipping receipts, are received via printed forms or digital PDF documents that need to be manually scanned or entered. But this an error-prone task that can be automated. With computer vision and AI models, images are captured and data is processed into the proper back-end systems with a high degree of speed and accuracy. 

At the same time, many ERP systems have grown in complexity and need to be managed manually. As a result, departments such as finance deal with manual, error-prone, and repetitive data entry tasks across planning, procurement, accounting, reporting, and more. By implementing hyperautomation toolkits, finance departments can better understand these end-to-end processes with process mining and process discovery, as well as receive a blue-print for improving their processes by leveraging RPA, low-code application development, and integration techniques together. 

Hyperautomation can augment existing digital transformation efforts

Ultimately, what many firms want to know is where hyperautomation fits in their existing and ongoing digital transformation efforts. Replacing legacy systems and moving workflows to the cloud can take years, and most companies don’t have that kind of time. Fortunately, what’s so powerful about hyperautomation is that it allows firms to accelerate parts of their operations in tandem with their long-term digital transformation roadmaps. 

Firms don’t have to replace their existing systems overnight. Instead, they can leverage hyperautomation to develop more efficient ways for those systems to work together. Hyperautomation can be as iterative or as revolutionary as organizations want it to be.

Take the hyperautomation use cases discussed above, for example. The processes being addressed by hyperautomation are not particularly complicated, but they all have one thing in common – they are mundane and error prone. Automating these types of tasks has been the sweet spot for early hyperautomation adoption, and I predict we’ll continue to see even more applications spin up this year.