Definition of Hyperautomation
6 November 2022
Organizations employ hyper-automation, a business-driven, disciplined strategy, to quickly discover, validate, and automate as many business and IT activities as they can. The coordinated employment of numerous technologies, instruments, or platforms—such as artificial intelligence—is known as hyper-automation (AI).
- Software with an event-driven architecture
- Automating processes with robots (RPA)
- Intelligent business process management suites and business process management (BPM) (iBPMS) the platform for integration as a service (iPaaS)
- Tools with little or no coding
- Packaged programs
- Additional tools for automating decisions, processes, and tasks
- Hyperautomation examples.
OCR-based document comprehension (Optical Character Recognition) utilizing NLP to interpret emails (Natural Language Processing) Automate replenishment and stock forecasting. Using AI/ML (Artificial Intelligence/Machine Learning), to improve automation processes.
What distinguishes automation from hyper automation?
To put it another way, hyper-automation is the expansion of automation; it does this by adding a layer of cutting-edge technology to automation, allowing for greater technological potential.
Hyperautomation makes use of the following cutting-edge technologies
Tools for locating and ranking automation opportunities include process mining and task mining.
Tools for automating the development of automation to save time and money. They consist of tools for workload automation, iPaaS for integrations, and no-code/low-code development.
Business logic technologies, such as intelligent business process management, decision management, and business rules management, make it simpler to modify and reuse automation.
Tools for enhancing the capabilities of automation using AI and machine learning. Natural language processing (NLP), optical character recognition (OCR), machine vision, virtual agents, and chatbots are among the techniques available in this field.
2019 saw the introduction of the term “hyper-automation” by IT research and consultancy company Gartner. The idea is founded on the realization that robotic process automation (RPA), a relatively new and enormously popular method for automating computer-based operations, is difficult to scale at the corporate level and has a restricted scope for automation. A framework for the strategic deployment of multiple automation technologies, including RPA, alone or in combination with AI and machine learning is provided by hyper-automation.
Hyperautomation means that automation has been thought out well. A hyper-automation practice entails determining which tasks should be automated, selecting the proper automation technologies, fostering agility through the reuse of automated processes, and enhancing their capabilities by utilizing a variety of AI and machine learning techniques. A center of excellence (CoE) that promotes automation efforts is frequently used to manage hyper automation endeavours.
Hyperautomation aims to take advantage of the data gathered and produced by digital processes in addition to reducing costs, increasing production, and improving efficiency. Businesses can use that data to better inform and accelerate business decisions.