Hyperautomation was first brought to the public's attention in 2020 by Gartner. It has been a hot topic ever since. According to Gartner, hyperautomation is a business-driven and disciplined approach that companies use to quickly identify and automate as many IT and business processes as they can.
Gartner named hyperautomation one of the leading technologies for 2020, 2021, and 2022. They predict it will continue to play an essential role in the future, helping organizations lower operating costs and find new revenue streams.
While Hyperautomation is a high-level concept that integrates a wide range of digital technologies to automate businesses, Intelligent automation is a subset of hyperautomation. It combines Robotic Process Automation, artificial intelligence (AI), process discovery, and analytics to automate procedures.
Hyperautomation is a holistic approach that can include organizational restructuring and cultural transformation. It also has advanced technologies like computer vision and natural languages processing (NLP), extending the reach of intelligent automation.
What type of challenge or situation triggers hyperautomation?
It is easy to access, process, and analyze structured data. It isn't easy to do the same with unstructured information. As has been reported, 88% of enterprise data is not structured.
Unstructured data can include phone calls transcriptions, emails and texts, social media comments, video, and audio recordings. Hyperautomation is processing this data using AI technology, including NLP, OCR, pattern recognition, computer vision, voice recognition, and computer vision, helping you extract real value out of your unstructured data.
It is easy to connect business applications with built-in connectors or application programming interfaces. Many legacy applications do not have these and can only be accessed via a graphical user interface (GUI). For terminal environments or virtual environments, if the system doesn't allow direct access to a GUI, the only way to integrate is to read the information on the screen. This requires unique machine learning technology.
Hyperautomation combines machine learning, computer vision, and deep learning to solve legacy integration challenges.
Hyperautomation environments allow bots to do more than automate simple tasks. Hyperautomation tools allow you to collect and analyze business data from legacy systems and quickly visualize the results.
You can also use deep learning algorithms to analyze big data to do predictive analyses to help you predict what's likely to happen.
Automate "long-tail" tasks
Hyperautomation can help business users automate long-tail tasks. The "long tail" is a collection of statistical points that, while minor in their own right, add up to a large number when added together. Long-tail tasks are all the small things that you do during your day, such as answering emails, attending status meetings, making appointments, and so forth. These "small tasks" occupy a lot of your time, and many of them could be automated.
Hyperautomation benefits include all of the benefits you would get from RPA deployment and intelligent RPA, plus many more. These are just a few:
Businesses with legacy systems that aren't compatible with one another can benefit from hyperautomation. Companies will experience dramatic improvements in connectivity, agility, and efficiency using hyperautomation tools.
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