H is for Hyperautomation
From A to I to Z: Jaid’s Guide to Artificial Intelligence
As the name suggests, hyperautomation is automation taken to the next level.
Where regular automation removes or reduces the need for humans to be involved in specific tasks, hyperautomation uses AI, machine learning, and other advanced technologies to automate entire processes.
Let’s say you wanted to visit a friend who lives 250 miles away.
Your car’s automatic transmission means you don’t have to constantly change gear if you’re stuck in traffic or going up a steep incline.
You also have cruise control, which saves you having to keep your foot on the gas pedal, or worrying about accidentally going over the speed limit. And your in-car entertainment system can automatically play your favorite music and, at the same time, work out the fastest route to your friend’s house and give you directions so you won’t get lost.
Because these specific driving tasks are automated, driving is easier and your journey is more pleasant.
That said, the car won’t be able to take you to your destination safely without your intervention. You’ll still need to steer, watch the road, and make sure you don’t commit any traffic infractions. And the car won’t stop unless you press the brake.
By contrast, a self-driving car handles the entire driving process. Once you input your destination, it’ll pick the route and do the driving, including adapting to road and traffic conditions. Meanwhile, you could watch a movie, read a book, or even take a nap if you’d like.
Self-driving cars are just one example of hyperautomation. The process can be applied to any number of use cases, including fraud detection, cybersecurity, diagnosing illnesses, and customer service.
Crucially, once you hyperautomate a process, the AI is able to identify other opportunities for automation, as well as analyze its performance and work out ways it could improve.
Gartner is widely credited with having coined the term “hyperautomation” in Hype Cycle for Artificial Intelligence 2020 — a research report on the state of AI at the time. The report described it as “the combination of multiple machine learning, packaged software and automation tools to deliver work…“
While “hyperautomation” is a relatively new term, the idea of integrating various technologies to automate at scale has been a long time in the making. Robotic process automation — on which hyperautomation is based — has been around in some shape or form since the 1990s. The main difference is that, where robotic process automation is a strictly rules-based process, AI’s ability to learn and adapt enables hyperautomation to handle much more sophisticated and nuanced tasks.
Another fundamental component of hyperautomation — process mining — is an even older concept. The idea of using data science to discover, validate, and improve workflows was pioneered in the 1920s by engineers Frank and Lilian Gilbreth. Incidentally, Gilbreth is the inspiration for Steve Martin’s character in the movie Cheaper by the Dozen.
Want to know more?
This podcast episode is a fascinating whistle-stop tour of the history of hyperautomation, starting with the Gilbreths’ pioneering work to where we are today.
The Gilbreths first published their work on process mining in this 1921 paper. While a lot of the techniques they use are now obsolete, it’s a fascinating look at where it all started.
Hyperautomation makes it possible to automate anything that can be automated, operate in a far more efficient way than has ever been possible, and access data you might not have been able to tap into before. With hyperautomation, you can ‘set and forget’ entire processes, even when they normally involve staff from several different departments, freeing up more of your resources and creating opportunities for you to turn what you’d traditionally consider cost centers into unique selling points.
Take advantage of Jaid’s hyperautomation capabilities to optimize your business functions – contact us today!