Definition:

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 (AI) and machine learning, is known as hyper automation.
Hyper automation means that automation has been well thought out. A hyper automation practice entails determining which tasks should be automated, selecting the proper automation technologies, fostering agility through the reuse of the automated processes, and enhancing their capabilities using a variety of AI and machine learning techniques. A center of excellence (CoE) that promotes automation efforts is frequently used to manage hyper automation endeavors.

How does hyper automation work?

Hyper automation focuses on increasing intelligence and using a wider systems-based approach to extend automated initiatives rather than referring to a particular, out-of-the-box technology or solution. The strategy emphasizes the significance of achieving the ideal balance between automating manual tasks and streamlining complicated processes to remove steps.

A key question lies in identifying who should be responsible for the automation and how it should be done. Front line employees are better able to spot tedious tasks that could be automated. Experts in business processes are better equipped to spot chances for automation that are handled by numerous individuals.

Digital Twin of Organization (DTO) can be automatically generated by process mining and task mining systems, allowing organizations to see how functions, processes, and key performance indicators interact to create value. Organizations can examine how new automations generate value, open up new opportunities, or produce new bottlenecks that need to be fixed with the use of the DTO.

Need of Hyper Automation:

Organizations can use the framework provided by hyper automation to enhance, integrate, and optimize enterprise automation. It solves the shortcomings of robotic process automation (RPA) tools and builds on their success.

Compared to other automation technologies, RPA has grown quickly, and this is thanks to its usability and intuitiveness. Employees can automate all or a portion of their jobs by documenting how they complete a task because RPA mimics how people interact with apps. The automated job tasks can also be measured for speed, accuracy, or other factors that businesses use to assess staff performance on the same tasks because bots mimic human actions.

In order to generate the necessary automation artifacts, such as bots, scripts, or workflows that may use DPA, IPA, or cognitive automation components, hyper automation steps back and considers how to speed up the process of discovering automation opportunities.

Advanced technologies used in Hyper Automation:

1. Process mining and task mining tools – for making construction automations easier and less expensive They consist of tools for workload automation, iPaaS for integrations, and no-code or low-code development.
2. Business logic tools – Business logic technologies, such as intelligent business process management, decision management, and business rules management, make it simpler to modify and reuse automations.
3. AI and machine learning tools – for extending the capabilities of automations. The range of tools in this area include natural language processing (NLP), optical character recognition, machine vision, virtual agents and Chatbots.

Where Hyper automation in AI Fits into Top Industries:

Hyper automation is taking shape in various industries, helping organizations improve business operations on a bigger scale than ever before.

1. Banking – Regulatory compliance, marketing, sales, and distribution, customer service, payments, loans, and back-office operations are the areas where hyper automation may benefit banks. Robotic Process Automation(RPA) takes care of lower-level chores so teams may enhance strategic decision-making, consulting services, risk and opportunity identification, and data reporting in real-time. Banks can enhance their “Know Your Client” (KYC) procedures and compliance, for instance, by converting manually recorded customer information into electronic versions for quicker analysis and action. Time, money, and human involvement are all saved via hyper automation.
2. Call Centers – Another real-world example is the use of RPA and AI in call centers to automate manual processes like mouse clicks and application launches to help agents pull information about a client from multiple systems quickly. When a customer calls an agent, the agent can see a more complete customer profile without having to keep switching from one app or screen to another.These activities can be extended to other service-related functions such as CRM, package tracking, and project automation.
3. Healthcare – The healthcare sector is in a unique position to use hyper automation with technologies like natural language processing (NLP) to provide automated services, speed up processes, lower costs, and provide higher quality because there are so many repetitive processes, contractual obligations, and regulations to comply with.
4. Retail – The retail industry is undergoing a major transformation as more companies choose to conduct their business online through e-commerce platforms. Due to safety and hygienic precautions, many customers now choose to purchase items conveniently online as a result of the epidemic. Being relevant to customers has become crucial for organizations to not only prosper but also survive few processes, including order administration, payments, transportation, warehousing and inventory management, supplier management, risk management, procurement, and data monitoring, among others, may be mechanized with the use of hyper automation.

Hyper automation vendors:

At the present  time, no companies provide complete hyper automation technology. To offer a greater range of hyper automation capabilities, different automation suppliers are growing their tool portfolios.

Vendors expanding their automation repertoires include the following:

• To expand its process mining capabilities, Vui Path purchased Process Gold and Steps hot.
• Automation Anywhere has been developing its own task and process mining tools for autonomously creating bots.
• Blue Prism has established a cooperation with Colonies and has been developing its own internal process mining capabilities.

Intelligent Business Process Management Suites (iBPMS):

At the present  time, no companies provide complete hyper automation technology. To offer a greater range of hyper automation capabilities, different automation suppliers are growing their tool portfolios.

1. Process Mining – A detailed understanding of a company’s processes may be attained through the analytical discipline of process mining. There are many applications for process mining, from conventional ones like process improvement to industry-specific ones like risk detection in an audit. Process mining is essential for process comprehension and simplification, two key elements of hyper automation.
2. APIs – The technology and frameworks to provide black box software interfaces has been around for a long time. Jeff Bezos famously pushed all Amazon to embrace APIs back in 2002. However, even today numerous enterprises rely on legacy interfaces to exchange data between applications. Embracing APIs facilitates machine-to-machine communication and therefore automation.
3. Computer Vision – Computer vision (CV) is a combination of AI techniques including image classification and segmentation, and object detection and tracking, which enable machines to interpret information from unstructured data such as images and videos.
4. Natural Language Processing (NLP) – Automating tasks that knowledge workers would do is made easier by NLP. It makes it possible for computers to comprehend unstructured data like films, social media posts, and emails. Depending on the level of automation required by your company, it then does sentiment analysis, automatic language translation, or automatic text classification into categories.

Benefits of hyper automation:

1. Reducing the price of automation.
2. Increases business and IT alignment.
3. It also cuts down on the need for “shadow IT,” which enhances security and governance.
4. It helps business processes integrate AI and machine learning more effectively.
5. It enhances the capacity to assess the effectiveness of digital transformation initiatives.

Conclusion:

The aim of hyper automation is not only to save costs, boost productivity and gain efficiencies through automating automation, but also to capitalize on the data collected and generated by digitized processes. Organizations can plumb that data to make better and timelier business decisions.