In this blog, we’ll have a look at the tech behind the buzz — and where it fits into the bigger picture for companies intent on solving process problems.
What Is Hyperautomation: What's the Hype All About?
If you’re a process enthusiast, you’ve most likely heard about “Hyperautomation.” First appearing on the radar in 2020, it's now a staple on Gartner's Top Strategic Technology Trends list. And as such, it has garnered huge interest within the process community. Just take a look at the Google search trend:
What is Hyperautomation?
According to Gartner:
“Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.”
The global research and advisory firm sees it as the latest evolution of process automation, which until today has been about implementing bots into processes to automate simple, standardized, repeatable tasks.
Hyperautomation takes it a step further. It combines automation tools like RPA with technologies like AI and machine learning to automate processes in a more intelligent way. The idea is that you use the “smart component” to analyze and optimize processes first, and then apply the automation.
Gartner views Hyperautomation as an essential tool for businesses to stay competitive.
What's Missing from Hyperautomation?
All of that sounds great, in theory. But right now, it’s still just that — theory. Hyperautomation is a concept, rather than an actual out-of-the-box tool. It’s the idea of an all-in-one solution that combines RPA, AI, machine learning, Process Mining, decision management, and natural-language processing, all for the purpose of making automation smarter. And this theory has a fundamental flaw: it is based on the assumption that automation is always the right way to improve business processes.
In our quest for a one-size-fits-all solution to process friction, it’s easy to lose sight of the complexity of the challenges at hand. Automation for automation’s sake is a classic example of throwing technology at the problem, and forgetting why we’re trying to solve it in the first place: to achieve better business outcomes.
Let’s ignore all the background noise for a second and answer one simple question:
WHY do you want to improve your processes?
It sounds obvious, but process improvement (like any other business initiative) has to start with a clear goal. Do you want to speed up invoice processing? Maximize cash discounts? Improve working capital? Increase on-time delivery? Reduce audit risk?
“Just because everyone else is doing it” is not a KPI. You should identify the KPI you’re aiming for before you blindly start to automate.
Take Hamilton Health Sciences in Canada for instance. The hospital network set out to predict “code blues” — situations when a patient has a cardiac or respiratory arrest. A patient who codes blue only has a 1 in 4 chance of leaving the hospital alive.
Here, the importance of outcomes is obvious.
Hamilton could have just gone for an automation-based solution that records and monitors health data, and pushes it to doctors’ tablets. That would have helped their staff make their morning rounds faster, sure. But faster isn’t better in this case — in order to increase the KPI of patient survival, doctors need to know which cases to focus on first.
