Every year, the pace of digital transformation gains speed. What took 10 clicks now takes one. Goodbye 4G, hello 5G. Quantum computing, once a murky sliver of theoretical physics, has now gained enough commercial traction to go mainstream. There’s a new speedy term in the mix: hyperautomation, a twist on the concept of automation for business.
Gartner says that hyperautomation happens when companies “rapidly identify and automate as many business processes as possible,” using software, robotic process automation, and machine learning. Interest in enterprise and hyperautomation is at an all-time high, per Gartner’s top tech predictions for 2022.
Could hyperautomation simply be about speed? Gartner has acknowledged that it’s not, writing that companies would be wise to “strategize and architect across the toolbox of options, including RPA, iBPMS, iPaaS and decision management tools.” The trouble is: that’s an expensive toolbox, and plenty of the tools in it don’t sync with one another.
Enterprise automation overlaps with many related areas — process mining, analytics, and machine learning among them. While it’s easy to recommend that all these systems work together, executing on this is a different matter.
For example, as hyperautomation takes on more capabilities, current recommendations have evolved to include the following tools for a complete automation toolbox:
Business process automation has well-documented virtues — from saving time and money to making knowledge workers happier by removing tedious manual tasks. It can dramatically increase customer satisfaction by reducing wait times and fostering better experiences. It lets businesses gain efficiencies and reassign staff to higher-value projects.
But here’s the catch: robotic process automation — particularly the kind of RPA solutions peddled by first-generation companies in the space — simply add a patchwork solution to what was already a patchwork. RPA is good for automating repetitive screen tasks, not end-to-end business process automation. For example, if RPA was programmed to sort red apples from green, it would do well until it encountered a yellow one. Most RPA systems can only perform automations within predefined processes, which are rule-based, not ready for exception handling, and programmed in advance.
Instead of digging deep to find out why their tower is leaning, companies just stack another wobbly layer on top.
After deploying software from such RPA companies, enterprises are left with a stack of internal systems that are duct taped together, with simplistic automations running on top of legacy software. This has the indirect effect of cementing the legacy software in place.
Instead of digging deep to find out why their tower is leaning, companies just stack another wobbly layer on top. In fact, the cost of licensing a single RPA bot or unit is on average only 25% to 30% of the total cost required for its full functionality — with the rest going into infrastructure set-up, maintenance and expansion, consulting costs, and more.
When it comes to smart enterprise automation, or hyperautomation, this is the concept that separates companies that automate well from those that don’t. Gartner advises, correctly, that the hyperautomation journey should “focus on a wider spectrum of business functions and knowledge work.”
But the toolbox approach Gartner proposes for achieving hyperautomation is fragmented and expensive. We believe the fastest path to hyperautomation is to pass up RPA altogether, opting instead for an end-to-end platform in which automations are already integrated with analytics, AI, process mining, and decision modeling. The goal of a platform that’s truly built for hyperautomation will not just be temporary productivity boosts but fully autonomous business processes.
Companies that use RPA alone will fail — or at least lag far behind.
Hyperautomation platforms, end-to-end systems that are agile, iterative, and incorporate new information and workflows, will be the ideal way for the next generation of companies to streamline business processes and make better decisions.
To be sure, hyperautomation leaves plenty of room for humans. An agile system creates many forks in the road, decision points where humans can review progress, check quality, and have the usual range of attitudes and opinions. The goal of hyperautomation is not for the human to step out of the loop, but rather for the human to step up to the conductor’s podium, directing all orchestra sections to play in harmony, ultimately saving both time and money.
Originally published on Automation Hero. To know more about our AI and Automation solutions, reach out to us here.