We’ve seen the future—and it is fast. But you need a digital foundation to get there.
If you’ve ever played with Legos, you know the possibilities are endless. A few blocks can become a toy car. Add a few more and you’ve got a miniature house. Keep going and you’ve got an intricate city or life-size statue. The blocks themselves fit together in a near-infinite number of ways.
Technology is a lot like Legos in that you’re limited only by your imagination.
And while emerging and maturing technologies like artificial intelligence, process mining, robotic process automation, orchestration, and integration are all powerful on their own, their true potential is only unlocked when we see them as building blocks to something bigger.
We’ve seen this already with intelligent automation, which fuses multiple elements of AI so organizations can automate complex tasks like predicting IT outages or categorizing and routing work. What’s new and exciting are the variety of technologies that fit into this “better together” scenario.
The era of hyperautomation
With ServiceNow’s most recent Quebec release and acquisition of robotic process automation (RPA) provider, Intellibot, nearly all these technologies are present within the Now Platform. And although there’s a lot to discuss with both Intellibot and Quebec, the headline for me is the way they seed the ground for a trend Gartner calls hyperautomation.
With hyperautomation, technologies like AI, RPA, and process mining come together to help organizations evolve beyond rules- and task-based automation to the automation of as many end-to-end business and IT processes as possible.
It’s important to note, however, that hyperautomation is not a specific grouping of technologies. It is a family of enterprise tools that work together as a given use case requires.
For example, AI-powered virtual agents may draw upon document intelligence, integrations with multiple systems, and natural language understanding to provide fully automated customer service. Meanwhile, process mining may combine with machine learning to not only identify process inefficiencies but build workflows that automate those inefficiencies away—perhaps by using RPA to eliminate data entry blocks between legacy and modern systems, or AI to route work more intelligently to employees.
What’s exciting is that in both cases, hyperautomation allows for the automation of activities that span multiple departments, systems, and job functions that previously required some level of human intervention and cognition.
The result is that automation is evolving from something that improves efficiency and productivity to something that fundamentally transforms how an organization operates—eliminating low-level cognitive tasks and freeing employees to focus on higher-level creative work.
The leap to ludicrous speed
The shift to hyperautomation is far from simple. And without the proper digital foundation in place, you’ll end up like Lord Dark Helmet from the cult classic, Spaceballs, when he tries to jump from lightspeed to ludicrous speed without preparation.
Dark Helmet ends up ramming straight into a wall, and the same will happen to organizations who try to implement hyperautomation without first building the proper digital foundation. After all, you can’t automate what’s happening offline, and without a digital strategy in place, the technologies that comprise hyperautomation will simply increase enterprise complexity and cost.
So, building a true digital business is the first critical step, and that means defining how work flows through your organization and then building digital workflows to match. These workflows merge enterprise activities and data into one platform. As a result, they can be thought of as the medium through which hyperautomation acts.
Automation in action
Imagine you’re a mortgage provider looking to automate loan origination. Your first step would be to define your ideal loan process from initial contact through to evaluation and document signing. This is your end-to-end workflow, and within it are multiple opportunities for automation.
That could start with an intelligent virtual agent gathering information and documents before handing the case off to an AI algorithm to assign the appropriate loan officer.
As that’s occurring, RPA bots may be consolidating data from legacy systems so the loan officer has everything they need to reach a decision. A machine learning algorithm could even assist by suggesting the right decision based on historical data. Overseeing it all are process optimization tools that measure real-world activities against an ideal process before suggesting areas in which to improve.
Setting the scene for hyperautomation
This is end-to-end automation. For it to happen, organizations must become far more connected. I’d go so far as to say that the degree to which this connection occurs will determine the degree to which hyperautomation succeeds.
That’s why our Quebec release and our recent acquisition of Indian RPA provider Intellibot are so exciting.
On one hand, they enhance our ability to integrate and automate workflows across legacy and modern systems—in other words, to connect the organization within a single system of engagement. On the other, they bring the building blocks of hyperautomation—AI, RPA, process mining, integration, and orchestration—into one platform.
The result is that companies don’t need to stitch together various technologies from different providers. It’s all in one place, sharing the same codebase and data model. This makes it easier and faster to identify and act upon end-to-end automation opportunities—which translates to better agility, improved productivity, and reduced risk.
This is a truly remarkable leap forward. But first we must stop seeing tools like RPA and process mining as discrete technologies and begin seeing them as Lego blocks that click together to build the future of work.