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With the crises that accompanied COVID-19, businesses now have a greater need to streamline their workforces. They can lower costs, reduce risks and increase productivity through hyperautomation, making it an essential component for streamlining workforces. It helps organizations visualize how key performance indicators, functions and processes interact to drive value by further enhancing automation capabilities.
We’ll look at the definition of hyperautomation and its benefits, discuss the tools used and look at some examples of hyper automation.
Hyperautomation Definition
Hyperautomation is a method of accomplishing end-to-end automation by harnessing multiple technologies. It’s an extension of legacy automation beyond the limitations of individual processes. Hyper automation can use Robotic Process Automation (RPA) with Artificial Intelligence (AI) tools to enable the automation of repetitive tasks that business users execute. It can even create bots to automate businesses that have been dynamically discovered.
Hyperautomation is a superior framework of advanced technologies with the ultimate goal of developing processes for enterprise automation. It uses a wide variety of tools that work in tandem to automate complex processes. Here are some examples of hyper automation tools that are commonly used:
- Task mining and process mining tools are used to identify and prioritize automation opportunities.
- The cost and effort for building automation can be reduced using automation development tools. These include RPA, iPaaS for integration, workload automation tools and low-code/no-code development tools.
- Business logic tools make it easier to adapt automation and reuse them whenever necessary. Intelligent business process management, business rules management and decision management are widely adapted and reused.
- Machine learning and AI extend automation capabilities. Some tools used in this area include optical character recognition, virtual agents, chatbots, machine vision and natural language processing (NLP).
In practice, hyper automation identifies work to automate, choosing the right tools, reusing automated processes to drive agility and using various aspects of machine learning and AI to extend capabilities of automated processes.
Benefits Of Hyperautomation
Besides saving costs, boosting productivity and gaining efficiencies, hyper automation also aims to capitalize on the data generated and collected through digitized processes. Organizations can use machine learning and RPA to pull data and produce reports from social media platforms. They can analyze patterns that can help create targeted and real-time customer campaigns.
Here are the key benefits of hyper automation:
- It can significantly lower the costs of automation.
- It effectively aligns business with information technology (IT).
- Not just RPA developers and testers, workforce engagement capability enables everyone to contribute to automation, including business analysts, business users and subject matter experts.
- Hyper automation improves governance and security by reducing the need for shadow IT.
- The chances of business processes adopting machine learning and AI are enhanced.
- Advanced analytics allows measurement and demonstration of return on investment of automation and its impact on the business outcome.
- The ability to measure the impact that digital transformation efforts have had is improved.
- It helps prioritize automation efforts for the future.
Hyperautomation has become a means for real and efficient digital transformation by automating complex processes that previously required expert supervision. Managers have to master it to discover the various ways in which this discipline can improve complex business operations.
Hyper Automation Examples
At the start of this initiative, there is a specific goal that looks to improve a process or a metric. Here are a couple of hyper automation examples relevant to business managers:
- A finance team set the goal of rapidly processing invoices with fewer mistakes and less human overhead. The project started by using task mining software to study how human accountants received invoices, their recorded data and the fields filled using that data; this served as a template to generate a basic bot. The template was then passed over to the center of excellence to develop a final bot using an Optical Character.
- Recognition/Reader (OCR) engine to improve invoice reading capabilities and a Natural Learning Processing (NLP) engine to interpret terms and conditions laid down in the invoice.
- An organization set the target of reducing order fulfillment time. They used process mining software to analyze Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) data to identify the reason behind varying fulfillment times. Process analytics were used to reduce such delays by adjusting requirements for credit checks and automating certain manual processes.
Whether it’s insurance, health or banking, today, hyperautomation is relevant across all industries. It can be leveraged almost limitlessly; managers must identify where the largest returns can be found.
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