What is Hyper Automation?


Technology Trends for 2021 and beyond are Hyper automation in which organizations use a combination of Artificial Intelligence (AI) and Machine Learning (ML) to identify and automate all possible business processes.

It is a comprehensive end on automation using the power of many technologies. Those technologies are including;

  • Robot Process Automation (RPA),
  • Process Mining,
  • iBPMS,
  • iPaaS,
  • Digital signature,
  • Machine Learning (ML)
  • Artificial Intelligence (AI) to automate work.

In this post, we would try to understand that what Hyper Automation is and how it works?


  • The final goal of hyper-automation is to grow a process for automating enterprise automation.
  • The term hyper-automation was created by the IT research and advisory firm Gartner in 2019.
  • Hyperautomation delivers a framework for the planned deployment of many automation technologies.
  • It suggests a studied method to automation.
  • A hyper-automation practice includes recognizing what work to automate, selecting the suitable automation tools.
  • Also include the driving agility over the reuse of the automated processes.
  • Similarly, comprised of spreading their abilities using several tastes of AI and machine learning.
  • Hyperautomation enterprises are repeatedly synchronized through a center of excellence (CoE) that supports to drive automation energies.

What is Hyper Automation?

Goals of Hyperautomation

  • Save costs,
  • Boost productivity
  • Gain efficiencies over automating automation,
  • Capitalize on the data gathered and made by digitized processes.
  • Organizations can upright that data to make better and timelier business decisions.

Importance of Hyper Automation

  • Hyperautomation offers organizations a framework to expand on, integrate and optimize enterprise automation.
  • It generates the success of RPA tools and addresses their limitations.
  • RPA owes its rapid climb, relative to other automation technologies, to its simple use and intuitive nature.
  • For instance, because RPA mirrors how people interact with applications, employees can automate part or all their work by recording how they perform a task.
  • Since bots mirror human actions, automated work tasks are often measured for speed, accuracy, or other metrics employed by companies to gauge employee performance on equivalent tasks.
  • Early RPA efforts, however, had a significant drawback for enterprise use:
  • The technology didn’t scale easily.
  • Only about 13% of enterprises were ready to scale early RPA initiatives, consistent with a 2019 assessment by Gartner.
  • Hyperautomation forces believe in processes required to scale automation initiatives.
  • The key target is how enterprises can build a process for automating automation.
  • This separates hyper-automation from other automation frameworks that simply specialize in improving automation tools or from automation concepts like digital process automation (DPA), intelligent process automation (IPA), and cognitive automation, which specialize in automation itself.
  • Hyperautomation accelerates the method of identifying automation opportunities.
  • Then automatically generates the acceptable automation artifacts, including bots, scripts, or workflows that will use DPA, IPA, or cognitive automation components.
  • The idea of digital worker analytics focuses on performance and process: e.g., the way to track the value of developing, deploying, and managing automation to match the worth to the value delivered.
  • This analysis is vital for prioritizing future automation efforts.
  • Many sellers of RPA and enterprise automation are starting to introduce digital worker analytics into their tools.

How does Hyperautomation work?

  • Hyperautomation focuses on adding more intelligence and implementing a broader systems-based approach to scaling automation efforts.
  • The approach underscores the importance of striking the proper balance between replacing manual efforts with automation and optimizing complex processes to eliminate steps.
  • A key question lies in identifying who should be liable for the automation and the way it should be done. Frontline workers are in a better position to spot boring tasks that would be automated.
  • Business process experts are in a better position to spot automation opportunities that are handled by many of us.
  • Gartner has introduced the thought of a digital twin of the organization (DTO).
  • This is often a virtual representation of how business processes work.
  • The representation of the method is automatically created and updated employing a combination of process mining and task mining.
  • Process mining examines enterprise software logs from business management software like CRM and ERP systems to construct a representation of process flows.
  • Task mining keeps using machine vision software running on each user’s desktop to build a view of processes that span multiple applications.
  • Process mining and task mining tools might automatically generate a DTO.
  • That allows organizations to see how functions, processes, and key performance indicators interact to drive value.
  • AI and machine learning components enable automation to interact with the planet in additional ways. for instance, OCR allows automation to process text or numbers from paper or PDF documents.
  • Tongue processing can extract and organize information from the documents, like identifying which company an invoice is from, what it’s for, and automatically capture this data into the accounting.
  • A hyper-automation platform may sit straight on top of the technologies companies have already got.
  • One main gateway to hyper-automation is RPA.
  • Every one of the leading RPA sellers is adding help for process mining, digital worker analytics, and AI integration.
  • Furthermore, other sorts of low-code automation platforms also are adding support for more hyper-automation technology components.