By Riccardo Conte, serial entrepreneur and founder of Virtus Flow, a no-code digital process automation platform that streamlines work through process automation.
Hyperautomation is a concept introduced by Gartner. However, the idea behind it has been in practice by companies leading the digital transformation race for some time. Forrester refers to it as digital process automation (DPA). Gartner identified it as one of the 10 trends of 2020 and predicted that adopting these technologies will reduce as much as 69% of the manager’s workload by 2024.
What Is Hyperautomation?
Hyperautomation is no more than the combination of different technologies to undertake end-to-end automation of an organization’s processes under a unified and intelligent system. It is the application of technologies such as robotic process automation (RPA), intelligent business process management (IBPM), machine learning and artificial intelligence (AI) to streamline and orchestrate as many tasks, activities and processes as possible. Hyperautomation is adopting automation technologies as a synergy of powers or capacities instead of adopting only one. It relies on data to autonomously offer input on the best decision to make.
Hyperautomation looks to augment workers’ abilities to reduce operating costs and errors. It seeks to accomplish digital transformation to its full potential to increase organizational agility and eliminate silos. With machine learning, hyperautomation enables organizations to learn from the processes themselves. It determines, for example, which route is the most efficient and which steps can be eliminated. It goes beyond the simple use of RPA to reproduce human tasks. It fixes the gaps left by RPA and allows organizations to automate even complex processes where human input was previously needed. This new approach has changed the scope of automation from individual tasks to knowledge work that drives better business outcomes.
Which Technologies Does Hyperautomation Use?
Although it uses a complementary range of technologies, hyperautomation has two key components: RPA to connect legacy systems and IBPM to manage long-running processes.
• RPA is the use of computer software to reproduce tasks, activities or processes that are repetitive and rule-based, such as data entry, payment reconciliation and data sharing. It’s also used to get two or more software platforms to talk or exchange information. Its use is limited to single tasks or short processes that have been established or defined, usually where humans are not required to intervene.
• IBPM combines business process management (BPM) and complementary technologies such as AI to help businesses dynamically automate processes from end to end, such as an onboarding process where data needs to be collected, shared and stored and following steps are triggered automatically. IBPM allows for automation of workflows instead of single tasks.
Hyperautomation Complementary Technologies
• Machine learning is the application of AI to allow the software to autonomously learn and improve without the need for programming. It aims at the autonomy of the system to access data, learning automatically from observing the way data moves. For example, machine learning can allow the software to identify activities where value is being created and those where it is not. It can provide data-backed validation to eliminate operational efficiencies.
• AI is the ability of a computer or software to reproduce an activity that is usually done by a human because it requires human intelligence or discernment, such as learning, planning, recognizing speech and solving problems. AI looks to perfect the capacity of these systems to work and think like humans.
Benefits Of Hyperautomation
Hyperautomation facilitates the connection between various business applications and operations with structured and unstructured data. It simplifies data analysis and process discovery as well as new automation opportunities. It empowers organizations to make informed decisions based on data gathered and analyzed by the automation systems. It directly shows which decision should be made. It complements human intelligence for better business decisions. Here are some concrete benefits:
• Erasing deficiencies and gaps: Businesses are no longer restricted by the limitations of one technology. They can now digitize and automate full lines of business processes and others that have a complete organizational reach. Instead of streamlining payment reconciliation, for example, the business can automate the entire process — from the moment a customer asks for a quote to the moment it’s fulfilled — and retrieve data to understand inefficiencies.
• Integrations: Hyperautomation simplifies data sharing across multiple apps that business lines and areas use. It facilitates smoother and quicker data access. For example, if the organization is using a human resources app to manage salary payments, now it can connect it to other apps that help manage other processes, such as employee hiring and onboarding. HR can have a better and quicker picture of the entire department’s work.
• Real time (for real!): Hyperautomation allows organizations to understand, in real time, what is happening. Managers can have immediate information regarding the number of open requests, those that haven’t been assigned, status, who is working on a project, what’s missing and who made a mistake, for example. Problems are easier to solve, and faster decisions can be made. Businesses can respond to customers’ requests sooner and have full access to what’s going on with their requests.
• Productivity: This is probably one of the most significant advantages of hyperautomation. Employees’ capabilities are potentiated and complemented. Staff can spend time on value-adding activities such as developing closer relationships with clients. No more data entering. No more printing and scanning. Instead, hyperautomation allows for digital signatures and automatic storage by profile or project. No more calling and back-and-forth emails between HR and IT to set tools and desks for employees’ first day. No more guessing, but data-based decisions.
• The best of human intelligence: We love the idea of a self-piloted system, but we like the driving experience. The same goes for hyperautomation. We want to get rid of inefficiencies, but we want to hold the helm strong. The first step is to understand that a machine needs a lot of data to become accurate, and this gives us an idea that in the short term, human decisions are always better. But we can use these new technologies to gather a huge amount of data to make better data-driven decisions. It is the best of the two worlds.
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