13 April 2022
This year, the global hyperautomation market is expected to reach nearly $600 billion, according to Gartner. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world,” said Fabrizio Biscotti, Gartner’s VP Analyst.
- Digital transformation expenses are projected to reach almost $2 trillion in 2022 and are forecast to hit nearly $3 trillion by 2025 worldwide.
- At the moment, 56% of large organizations handle 4+ simultaneous automation initiatives.
- Over 80% of business leaders reported expanding remote work opportunities and accelerating work process automation.
Although the term has been trending for the past few years, the hyperautomation definition often remains vague. The Rocketech analysts prepared a guide with everything you need to know about hyperautomation and why it is important.
Hyperautomation, Explained
According to Gartner, hyperautomation is an established approach that organizations take to “automate as many business and IT processes as possible.” It’s a combination of standard software, automation tools, and machine learning designed to accelerate digital transformation, reduce human workload, and, as a result, increase revenue and business value.
Hyperautomation is not just the introduction of individual technologies but a strategic, end-to-end redesign that involves a continuous effort to automate all business processes across the enterprise.
As an integrated approach, hyperautomation involves “multiple technologies, tools or platforms.” However, the main hyperautomation components include:
- Robotic process automation (RPA);
- Machine learning;
- Artificial intelligence (AI);
- Low-code/no-code tools;
- Business process management (BPM) and intelligent business process management suites (iBPMS);
- Integration platform as a service (iPaaS).
The fastest-developing type of such software includes instruments providing the visibility of all business processes. This category of tools automates content creation and management, provides execution mechanisms for complex systems of rules and regulations, and controls work processes across multiple structures.
For example, hyperautomation technologies more often take a leading role in optical character recognition, signature verification, NLP (natural language processing), and document management. It will eventually lead to the digitization and fully automated structuring of all business data and paper documents.
Although hyperautomation trends may seem intimidating, they are not here to replace human labor. Most experts and business leaders rather consider these new technologies people-centered — whether it’s more comfortable digital working environments, structural changes, or more complex decision making.
RPA behind Hyperautomation
RPA (Robotic Process Automation) is the technology at the core of hyperautomation. It implies the active participation of software bots in certain business processes along with human employees. By performing pre-defined actions, these bots can interact with business applications the way people do, up to using completely “human” interfaces. The technology imitates user actions but minimizes the error rate.
Despite the futuristic name, RPA has very little to do with actual robots and rather involves specific software.
By identifying repetitive actions in the algorithms for solving various tasks, organizations can reassign them to a virtual assistant for faster processing and higher accuracy (in comparison to most humans). People, in this case, have more time for solving high-level problems that require intellectual effort.
Besides interacting with data and each other, RPA solutions address real employees as their colleagues would: sort requests by department, set tasks, control processes, analyze and generate reports.
Invoice processing, payroll calculation, supplier price comparisons, sorting and retrieving customer data, managing returns, and even hiring employees are likely tasks of RPA in the near future.
The latest RPA systems not only transfer data from one spreadsheet to another but can also process non-digital (including unstructured) incoming data. Computer vision, text recognition, and natural language processing transform these bots into customer and employee interaction tools, substantially reducing operating costs.
Hyperautomation Use Cases
Simply put, hyperautomation applications increase the percentage of automated business tasks. It leads to significantly less manual labor, fewer mistakes, and increased business efficiency. And one of the biggest hyperautomation benefits is cross-industry implementation. Here is how it works in different fields and areas.
Hyperautomation in Banking
Automated basic banking processes are already an integral part of neobanks. They include servicing retail and corporate clients, working with financial institutions as well as activities on the stock and financial markets.
However, AI solutions now become necessary in the digital transformation of traditional banks — chatbots, voice assistants, and personal financial advisers are already a requirement to stay on market. Moreover, cost savings from customer service chatbots in banking are predicted to reach $11.5 billion next year.
Hyperautomation in Healthcare
With RPA software, healthcare organizations can retrieve and optimize patient data more quickly and easily. When interacting with other digital systems, RPA software can assess the collected data to generate analytics.
IBM’s digital tool Watson Health recognized a rare form of leukemia in a patient after she had previously been misdiagnosed. It took the AI-powered software ten minutes to process data from 20 million scientific articles about different types of cancer.
The global pandemic has shown the devastating impact of medical staff shortages globally. Introducing a hyperautomation ecosystem to medical institutions streamlines reporting, data entry, and scheduling. On top of that, automation simplifies patient claims processing and evaluation.
Hyperautomation in Retail
In retail, besides data collection and processing (again), hyperautomation tools can optimize the processes in three main directions.
- Inventory Management
RPA solutions allow fast data exchange between several systems, including warehouse reporting and financial documents.
- Product Categorisation
Large retailers handle sizeable numbers of goods. When operating in multiple countries, corporations also have to deal with different product cataloging systems. Automated solutions help bring all the data together. According to The Everest Group, the categorization accuracy of such software can reach 98.5%.
- Customer Support
By using chatbots, customers can get updates on their order status or resolve payment issues 24/7. In addition, an RPA bot can perform simple queries or send feedback data to the marketing department.
Hyperautomation in Supply Chain
Schneider Electric reduced order processing time for personal protective equipment deliveries during the COVID-19 pandemic from four hours to two minutes. The project required just one software robot and two and a half days for implementation. In addition to the qualitative acceleration, the bot considerably reduced the error rate.
Hyperautomation in Insurance
A large health insurance company in the North and Latin American region implemented a software bot able to analyze medical risks associated with 6–8% of pregnancy cases. The process that took several weeks before the project now takes a few minutes. Besides freeing up the medical center’s staff time, the RPA system significantly increased patient safety.
Low-Code Is the Main Hyperautomation Trend
Low code is an approach to creating, configuring, and modifying systems and applications using little or no programming code. Low-code platforms use visual interfaces with simple logic and drag-and-drop features instead of various programming languages. These intuitive tools allow users without knowledge of programming or software development processes to create their own applications.
With a holistic hyperautomation approach, it is not an enterprise resource planning (ERP) platform at the center of the entire corporate IT architecture but rather a comprehensive business process management (BPM) system. And those BPMs are usually designed to support a low-code toolset.
The ultimate goal is to fundamentally reduce the implementation time of new instruments and speed up the automation of already existing ones. This way, small and mid-sized companies don’t have to initiate long, expensive IT projects or spend time on extra employee training.
Final Thoughts
The unprecedented events of the past two years have changed the entire global business paradigm. Today, total digital transformation is no longer a luxurious choice but a market requirement and a long-term business strategy.
Hyperautomation involves robotic process automation, artificial intelligence, machine learning, intelligent business process management, and process analysis for improving and enhancing all internal and external operations while reducing costs.
The approach allows business teams and their leaders to redesign their processes without the one-technology limitations. By carefully streamlining existing processes, companies discover new opportunities, achieve higher levels of satisfaction, and make additional contributions to cognitive challenges and customer issues.