A methodology once considered futuristic, almost something you could only imagine in a Sci-fi novel. Hyperautomation is today labeled by many as a complete must with a relentless demand to shift from manual to digital ways of working.
In a recent forecast made by Gartner, the expected market for Hyperautomation will grow from $481,635 billion in 2020 to $596,616 billion in 2022. That's close to 24% in just two years. Making the assumption or statement that it's here to shape our tomorrow is, after all, modest.
Let's take a step back and, first of all, establish the concept of Hyperautomation; what is it?
Three techniques merged into a new methodology that stretches end-to-end rather than making incremental improvements here and there. Digital Process Automation (DPA) provides the foundation and is perhaps the most crucial component to achieve Hyperautomation since it establishes a framework that enables further enhancement and automation.
Visualize it as the box in which everything is tied together. Work inside the box - think outside the box.
Taking the next step
While DPA focuses on end-to-end processes and takes a more holistic view of your ways of working, Robotic Process Automation (RPA) focuses on isolated work tasks that can be automated with the help of a software robot. These software robots can emulate human behavior such as copy and paste, keystrokes, and navigating systems.
The caveat is that the robot will only do what it's told to do. Software robots don't have a mind of their own, and therefore, human supervision and guidance are still needed.
The good thing with DPA, in this case, is that it doesn’t care if it’s a digital (RPA) or physical coworker who’s being assigned a task. Your DPA platform will orchestrate the process and assignees no matter what.
Insert the digital mind
Two puzzle pieces in place. It's time for the third and final to be put in place. That puzzle piece is Artificial Intelligence (AI). You're forgiven if your thoughts are running towards sentient and world-dominating humanoids. However, AI is a vast landscape that covers multiple disciplines.
You can teach an AI how to play chess, but that chess computer can't solve a mathematical problem. AI is similar to RPA in that aspect. Humans have scripted an algorithm based on a narrow field. To be brief, the difference is that AI can learn how to analyze data and suggest suitable actions. AI can function as a grand consigliere to decision-makers and any co-worker by giving this advice.
When considering decisions, many companies have multiple changes within the catalog and pricing, i.e. fast and frequent changes of rules. These are great candidates for implementing systems that support these kinds of processes.
Companies that strive to remain competitive in the digital era can't afford to ignore new and smarter ways of working. There are, of course, several trends each year that are promised to disrupt the business landscape.
Hyperautomation, on the other hand, is already transforming a lot of companies. Even Gartner has identified Hyperautomation as one of the top ten technology trends.
Here's what one of our clients, Granngården, has to say about Hyperautomation.
The fun and fascinating thing about Hyperautomation is that it'll no longer be 1 + 1 + 1, but rather 1 + 4 + 8. The exchange is potentially enormous.
Michael Wedin, Claims and Deviation Examiner, Granngården
The benefits of implementing Hyperautomation are not challenging to identify. Just to name a few examples:
- Being able to identify areas of improvement swiftly
- Empower the entire organization with automation
- Tear down the silos between the business and IT
- Enable humans to do more qualitative and value-adding work while robots and AI do some of the legwork
- Automate end-to-end
Hyperautomation and scalability
There should also be one obvious benefit added to this list; scalability. So let us examine how Hyperautomation enables scalability.
DPA is the foundation that orchestrates the process, end-to-end. RPA and AI are automation layers on top of the DPA. RPA emulates human tasks and performs said actions. Still, a human is needed for making decisions. That changes when implementing AI that can suggest a decision path based on certain thresholds. For example, no human interaction is required if the predictability precision is over 80 percent. If the score is lower, a human must intervene to decide.
This combination, therefore, enables a higher grade of automation. Virtually 100 percent, to be frank, based on the process it regards.
Less manual interventions mean more time focusing on enhancing current ways of working and implementing similar automation on other processes.