The term "hyperautomation" was created by Gartner. To boost the amount of automation and digital transformation in businesses, automation technologies like RPA and process mining are used in conjunction with machine learning and other cutting-edge technology. A recent development that is being enthusiastically embraced by numerous industry sectors is hyperautomation. Hyperautomation combines several process automation elements, integrating tools and technology that increase the capacity for job automation. The integration of data across business lines, systems, and applications is made simpler by hyperautomation, which also assures quicker and more effective data access. Linking data from hiring, onboarding, and salary payments, for instance, can help an HR department get a more complete picture of how the entire department is operating. Employees are able to spend more time on tasks that bring value by using hyperautomation instead of manually entering data, printing, and scanning customer information. To promote a better overall employee experience, for instance, it stops manual email communication between HR and IT departments. To enable end-to-end automation procedures like procure-to-pay, hyperautomation digitizes activities across departments. Furthermore, suppliers like UiPath are adopting hyperautomation, which is being used increasingly frequently. Given that it is brief, merchants find it appealing. The criterion can be applied to almost any automation technique, making hyperautomation applicable to any emerging technology.
It is anticipated that the market for hyperautomation will reach USD 2 billion, growing at a 22% CAGR by 2030. Some of the main factors driving the worldwide hyper-automation business are the increase in digitalization, improved efficiency, lower operating costs, and increased demand for industrial process automation. Further, the Asia-Pacific region accounted for a sizeable portion of the market and is projected to develop rapidly in the years to come. The area offers the greatest potential for growth in the field of hyper-automation.
Robotic Process Automation (RPA): Hyperautomation's core component is robotic process automation. Software bots are used in robotic process automation for the following jobs:
According to data more than 20% of organizations employ robotic process automation (RPA).
Intelligent Business Process Management Suites (iBPMS): The development of physical, real-world processes in the virtual world is made possible by iBPMS. They make it possible to design, run, and monitor these processes and offer a way to strategically manage automation.
Artificial Intelligence (AI) and Machine Learning: Machine learning and artificial intelligence are both crucial automation techniques. Owing to its ability to read data and take previously established actions, AI is increasingly being used in the hyper-automation sector. These are a few examples of AI- and ML-based technologies that enable hyperautomation:
Optical Character Recognition (OCR): Using an OCR tool, the printed document may be scanned and the entire text can be converted to digital format. OCR excels in translating human-to-human conversation, but it struggles to translate highly organized texts, such as forms that must be processed by computers.
The various advantages of hyperautomation:
The foundation of hyperautomation is the automation of business and IT processes, hence it is critical for enterprises to have visibility into the processes in order to choose which activities to automate and how. Businesses may boost their business value by 40% while lowering the risk of their RPA projects by 60% and cutting the time it takes to execute RPA by 50% by employing process mining. Financial AP and AR procedures, inventory management processes, and order management processes are a few typical areas that might benefit from process mining. Hyperautomation has also been fueled by process mining in sectors including healthcare, retail, and insurance.
Hyperautomation in Healthcare
Hyperautomation has countless applications in the healthcare sector, and its advantages can benefit the company, its partners, and its clients. Hyperautomation can benefit the healthcare sector by improving patient satisfaction, boosting revenue, and producing more precise data. Moreover, it can deal with managing patient records, gathering information, and producing meaningful output for more precise treatment plans. Additionally, it can be used to schedule personnel and other resources as well as manage drug inventories and procurement.
Hyperautomation in Banking and finance
Hyperautomation can provide staff with higher data quality, enabling them to use business process management (BPM) more effectively to provide customers with information that aids in better decision-making. Hyperautomation provides the back-end effectiveness required to maintain the continuous availability of online banking and financial apps, in addition to the essential rules and reporting. The banking and finance sectors use a lot of data, which can be challenging to manage. Hyperautomation makes processes faster, more dependable, and error-free by streamlining the necessary procedures. Businesses can collect pertinent data about consumer transactions from various data sources using RPA. After processing this data, an NLP system can run analytics to spot any fraudulent behavior.
Hyperautomation in Retail
Back-end retail activities like procurement, billing, supplier management, inventory, and transportation can be made more accurate and efficient while also being less expensive thanks to hyperautomation. Hyperautomation has been used to monitor and analyze market factors including pricing competition and consumer feedback, enabling quicker, more precise decision-making that boosts revenue and profitability.
Hyperautomation in Call Centers and Customer Service
The use of RPA and AI in call centers to automate manual tasks like mouse clicks and application launches to help agents swiftly retrieve customer information from various systems is another example from the real world. When a consumer calls, an agent can access a more thorough customer profile without constantly navigating between apps or screens. Other service-related tasks like CRM, shipment tracking, and project automation can be added to these activities. In order to provide customer support services, it is necessary to comprehend and respond to emails, questions, and queries as well as modify client data. Companies can automatically respond to customer inquiries by fusing these technologies.
The pace of insurance companies' digital initiatives is accelerating. Insurance companies may be able to accelerate the underwriting procedure through hyperautomation without sacrificing the precision of their risk assessment. The majority of submission/application processing operations can be automated using intelligent automation tools that integrate RPA with AI technologies like natural language processing (NLP). The understanding of insurers of the risk profile of consumers can be greatly improved by machine learning models and other cutting edge analytical techniques. To anticipate the risk profile of new submissions, these models can be trained on data from internal and external sources, such as third parties, prior claims, or customer-provided documentation. By using analytical models, underwriters can price the risk more profitably because these programmes are better than humans at spotting patterns in massive volumes of data.
The next stage of digital transformation entails automating as many business operations as is practicable, which is known as hyperautomation. Combining traditional automation technologies like RPA with intelligent ones like AI, process mining, or chatbots is what this entails. Hyperautomation aims to take advantage of the data gathered and produced by digital processes in addition to reducing costs, increasing production, and improving efficiency. According to the consultancy Gartner, hyperautomation will be one of the technological trends that will have the greatest impact in the next decade.