What is hyperautomation and how does it work?
Hyperautomation is a relatively new AI and machine learning topic that deals with automating the automation of businesses with different tools synchronized to work in a centralized framework.
Hyperautomation is a strategic technology trend that has gained massive adoption with several businesses due to its end-to-end mode of automation. Its approach allows businesses to optimize and integrate processes at larger scales to improve performance and ensure maximum productivity.
According to Gartner’s Top 10 Strategic Technologies in 2020 and 2021, hyperautomation does not only describe a new technology, it also dictates a new form of company growth because it takes a holistic approach of automation that introduces new possibilities into every business department.
Hyperautomation focuses on optimizing business processes through a studied approach by identifying, assessing and automating workflows using an advanced set of technologies. It is based on a framework of tools and technologies, most of which continue to evolve to accommodate enterprise scaling processes. The goal of the scaling process is to continuously optimize automation processes for non- and already existing workflows without future complexities.
Hyperatomation is grouped into three components. These components create a shift from the focus of conventional automation. Instead of creating and maximizing tools through digital process automation (DPA), intelligent process automation and cognitive automation, hyperautomation is process-centered. It considers every automation possibility in a flexible manner to include undocumented processes. The three components of hyperautomation include:
RPA lays a foundation through process discovery to reveal business processes that can be automated using smaller automation tools and bots. Through a defined sequence, built-in analytics and artificial intelligence (AI) are also used to create an intuitive system from CRM and ERP systems to add more intelligence to automation efforts.
This component considers every automation process and creates a synchronized framework that brings all those tools together. The large pool of centralized data drawn from the built-in analytics is used to draw up a framework where every employed tool can work in harmony. Orchestration also considers the impacts of automating processes on value and revenue with the use of data transfer objects.
In some cases, autonomous processes can be created to ensure automation in those processes will not hamper revenue generation. Further, optimization is achieved through conclusions drawn from correlations of large processed data.
This is the final layer of intelligence that is added to ensure seamless integration. By monitoring processes to recognize critical information and complex patterns, the digital traces of business processes can be analyzed to aid decision making.
The orchestration process is also improved further through validations and continuous learning. This helps to determine if the technologies and tools used should be deployed individually or in synergy.
While robotic process automation is at the core of hyperautomation, other sophisticated technologies like AI are integrated to mine and analyze processes.
