Hyperautomation is a term that was first coined by Gartner in 2019. As a catchall term for the combination of multiple different automation technologies, it can be a little difficult to visualize how artificial intelligence-driven data collection, analysis, and decision-making might play out in the real world. So, how does hyperautomation work, and is it worth the hype?
Hyperautomation refers to advanced technology like machine learning, artificial intelligence, or robotic process automation, specifically in the context of automating tasks that were once performed by people. In other contexts, it may also be termed “intelligent process automation,” or “digital process automation.”
While hyperautomation replaces human labor at many steps of a process, it isn't entirely intended to fully take over for people. Instead, it should ideally operate as a labor-saving strategy that offers humans more time to focus on high priority tasks that aren't easily automated.
Hyperautomation has some key components that distinguish it from similar concepts:
There are a lot of simple tasks that humans don't really need to spend time doing. Take approving insurance claims, for example. It's fairly simple for an algorithm to analyze a customer's history and claim information to detect fraudulent activity. Some specialized algorithms can even analyze damage to cars, boats, or homes to determine whether the damage is covered or not.
Support lines are another potential avenue for hyperautomation. Most customer service questions don't really require human intervention. Customer service reps are often prone to human errors, giving misleading information that leads to dissatisfied customers. Hyperautomation can allow algorithms to analyze, understand, and respond to customer needs and make any necessary adjustments.
Yet another example is transportation. Driving a vehicle—any vehicle—requires a lot of concentration and situational awareness, as well as a quick response time. Automotive software detects and analyzes information about obstacles, taking chaotic real-world images and structuring them into something the algorithm can understand and respond to.
As a highly complex, multi-step process that uses both structured and unstructured data, hyperautomation is subject to numerous disadvantages. For one, machine learning isn't without its flaws. Training algorithms can be a very slow process, especially if you're starting from scratch. Researchers have also uncovered numerous instances of biases within artificial intelligence, either based on the data itself, data collection methods, or subconscious biases unintentionally programmed into the algorithm at its inception. As an example, a healthcare risk predicting algorithm was found to exhibit racial biases based on a flawed metric. The algorithm used patients' previous health care spending to determine who was at most risk—sicker people tend to have higher healthcare costs. Unfortunately, this data didn't account for cultural and economic disparities in healthcare spending. This means that the algorithm's predictions were inaccurate. As is often said, “garbage data in, garbage data out.”
Another set of challenges come with simplifying processes. No matter how well-automated something is, humans will inevitably have to step in at some point to make adjustments and account for edge cases. Processes often suffer from a lack of documentation, and some information is difficult to document at all. Also, since hyperautomation still relies on basic robotic process automation, it's likely to falter when faced with situations that don't fit its standard response.
A lot of companies are already discreetly using hyperautomation in their regular operations. It allows them access to information and insights they wouldn't normally have, gives them a faster customer response time, and frees employees up to work on more important things. Like any technology, it has its pitfalls and may not be suitable for every potential use case, but hyperautomation is here to stay.
For a business to successfully deploy these technologies, there are a few considerations they need to keep in mind. The first is their labor force. What's their skillset? How can they be trained to adapt to these changes in their duties and work environment?
The next is the business processes themselves. Are they amenable to automation? What problems would be solved by automation, and do they balance out the time spent training an algorithm, testing processes, and analyzing data? A company that handles a large volume of complex client needs, for example, may not be. One that processes large numbers of straightforward online forms or customer service calls would.
Humans are smarter and more adaptable than any algorithm, which means that there are a lot of tasks that their potential is simply wasted on. Hyperautomation gives companies the ability to focus their manpower where they need it most, and collect and analyze data in a fraction of the time it would take for a human. While it's a new term that many still regard as a bit of a buzzword, it's becoming increasingly clear that it's not just hype.
