Those keeping an eye on the top technology trends of each year would know that Gartner’s list for 2020 has hyperautomation in the first spot. While many companies are still mulling over including robotic process automations (RPA) in their core activities, technology has progressed by another leap and bound to include its newest, in-demand entry – hyperautomation. The simplest way of looking at hyperautomation is – if automated processes are the vanilla test cases of a test case suite, then hyperautomation processes are the complex ones. Automation is just a small part, the initial part of hyperautomation. When one learns its components, one would also be able to strategize the path that needs to be taken to implement it in a business.
What Steps Lead Up To Achieving Hyperautomation?
Just like IoT, all components of hyperautomation are inter-related. They work in a cyclical fashion. At the heart of this process is automation. When combined with artificial intelligence (AI), machine learning and intelligent business process management tools, automation achieves a whole new level altogether. These three form the pillars of hyperatuomation, but the foundation still remains business analyses and creating scenarios where hyperatuomation needs to be implemented. To close the loop, we have data analytics and other ROI measurement techniques, to ensure implementation of hyperautomation is yielding the desired, qualitative results. Where simple automation is used to get common, simple, and repetitive tasks done that don’t need a lot of intelligence to be executed, hyperautomation enables an organization to combine complex business requirements, business intelligence systems, and augmentation of human knowledge and experience.
Here are the key stages that lead up to hyperatuomation:
- Automating tasks – Using different ways to run the tasks that are identified for hyperautomation, they are first executed on their own, without any manual intervention.
- Building Architecture – The application that is used to perform the automated tasks should be adaptive enough to be able to exchange information with other intelligent business process management systems. Setting up feeds to be exchanged between interfaces that are required to make the hyperatuomation work, and using APIs that are common enough to allow maximum number of software to be combined without any complications, are important to make the whole thing a success. Remember – one of the primary goals of hyperautomation is to save the company a lot of time and resources to achieve the desired results faster. If the framework for it is complex, then it won’t be much of a success.
- Automating Processes – Once we know what tasks or actions are to be automated, it’s time to automate the calls to actions, or the processes which lead up to certain events. Each organization has defined parameters based on which the tasks need to be performed. An intelligent hyperautomation setup contains details of the rules and workflows that drive the activities. Also known as intelligent business process management systems, it contains information that identifies the data required for a task to run, in a segmented manner, so it is easy to access them at all times. These processes need to be automated so the controls based on which events need to occur will run correctly and sequentially on their own.
- Smart Tools – In order to monitor the processes and collaborate with other people working on it, a hyperautomation loop should contain tools such as chatbots and virtual assistants.
- Artificial Intelligence and Machine Learning – With the help of these tools, the data gathered from the events up to this point are analyzed so that the robots or machines that will further execute the business processes can define how it should be done. So far, data from automation and business processes were running in separate compartments. With ML and AI, further enhancement scopes are identified and implemented if feasible. Take for example a language translating software that runs on a simple automated basis. Business processes require that in addition to translating data into just one language, they be translated into 5 more languages. The business also requires the data to be fact checked and its graphics be scrutinized for deep fakes before the final, translated copies of the data are distributed to the end clients. The automated tasks can reach the final stage only when AI and ML tools are used, and their programming execute these enhances, intelligent processes to convert raw data into finished, better, end user products in much lesser time than it would have taken for a person to do them manually.
Some companies promote the use of low codes in as many stages as possible. With a basic set of codes already entered into a framework by a third party vendor, the business can use graphical UI techniques such as dragging and dropping of the codes they need to set-up their hyperautomation system. Coding from scratch and fitting into a network with the right API can be eliminated with the use of the right low code in automation, so more focus can be applied to business intelligence tasks.
Authored by Admin