The Hyperautomation Takeover - Beyond RPA
Gartner further predicts that by 2024, the drive towards Hyperautomation will result in organizations adopting at least three of 20 types of software that enable Hyperautomation. It's also predicted that companies that start adopting Hyperautomation this year will be able to reduce operating costs by 30-50% by 2024.
The fastest-growing category of Hyperautomation software includes tools that provide visibility to display business operations, automate and manage processes such as content ingestion or customer service, organize work across multiple systems, and provide complex rule engines.
This article will figure out the difference between robotic process automation and Hyperautomation, provide an overview of the most in-demand technologies that will be driving Hyperautomation in the months to come, and bust the myth that Hyperautomation will make the human factor obsolete.
Contents
Key takeaways
- Hyperautomation is the combination of advanced technologies such as machine learning, intelligent business management software and automation tools to enable the complete automation of complex business processes.
- Hyperautomation can greatly contribute to higher productivity within the company.
- In contrast to Robotic Process Automation (RPA) that is limited and not suitable for handling complex tasks, Hyperautomation is characterized by a much wider range of functions and much more advanced capabilities required by modern business realms.
- Hyperautomation doesn’t necessarily replace human workforce. Humans remain an important decision-maker in the company.
- Costs are significantly lower because automation reduces the time and resources spent on manual tasks and also reduces the number of errors.
What is Hyperautomation?
As the name suggests, Hyperautomation transcends the typical automation strategy and takes conventional automation measures to the next level. Hyperautomation is the combination of advanced technologies such as machine learning, intelligent business management software, and automation tools to fully automate complex business processes.
Unlike traditional automation, Hyperautomation enables more effective process automation because it refers not only to the broad range of tools but also to the complexity of each automation step, i.e., recognizing, analyzing, designing, measuring, monitoring, and re-evaluating process steps.
Another key feature of Hyperautomation in this context is its ability to involve people in the process. Because Hyperautomation involves technology and humans working side-by-side, employees can begin to train automation tools and move toward a state of AI-enabled decision making through machine learning.
We’ve taken the first big step toward fully automated processes through the use of RPA. However, Robotic Process Automation is not suitable for handling complex tasks. This is because RPA focuses on standardized and repetitive processes that have a high volume of work. Hyperautomation enables companies to process unstructured input data with the goal of increasing AI-driven, informed decision-making. The combination of artificial intelligence, machine learning, process intelligence, content intelligence, robotics, IoT, and other technologies is driving rapid progress across domains and verticals and encourages next-gen product development and evolution. Hyperautomation is the catalyst for change in many organizations, as it’s the only most effective way to achieve all-embracing digital transformation and not just some of its standalone elements.
Technologies and approaches crucial to Hyperautomation development
As discussed above, the term Hyperautomation involves the combination of the emerging technologies. From a global perspective, the focus is still on the automation of simple tasks, but the combination of different technologies ensures more efficient processes. In addition, the need for human intervention is eliminated – the freed-up talent is available for other tasks. That’s how Hyperautomation contributes to higher productivity within the company.
This approach makes sense from a strategic perspective in particular, because people are a scarce resource nowadays. Competent employees remain in short supply. In addition, automated solutions are less prone to errors.
However, a clean process definition is the prerequisite here. Ultimately, companies become more agile and the necessary data and insights can be retrieved more quickly.
Let’s have a look at the different technologies and approaches that need to be seamlessly combined to create a feasible Hyperautomation approach.
Machine Learning
Machine learning (ML) is a subfield of artificial intelligence. With the help of self-learning algorithms, a program attempts to
- recognize certain patterns and regularities in the data and intelligently link them together,
- recognize correlations and deviations,
- draw data-based conclusions and make predictions.
With machine learning, humans hardly ever have to intervene at all, as the patterns identified are used to predict the following steps. Accordingly, complete processes can also be optimized with the help of this method. For the training process, suitable training data must be used and a model for using this data must be created.
Robotic Process Automation (RPA)
In robotic process automation, software robots take over the processing of rule-based business processes. These are simple and repetitive activities. With the help of RPA, a software robot can be programmed to execute the desired process on the basis of a repetitive procedure. The results here are standardized and not very complex.
Process Mining
Many companies know that automation is important to work more efficiently and improve customer satisfaction. But many don’t know exactly where to start. Process mining can be used to analyze patterns and tasks and uncover automation potential. In doing so, organizations can also take future challenges into account – for example, when it comes to complying with new or changed regulatory requirements.
Workflow optimization
This enables multiple processes to be accurately mapped, optimized, and executed in digital workflows, taking into account people and existing applications. By orchestrating multiple people, actions, software robots, policies, and systems in a unified way, organizations can better analyze, measure, and optimize business activities as needed.
Intelligent Business Process Management Suites (iBPMS)
iBPMS is the further development of the classic BPMS, offering more advanced functions for more intelligent business processes. Features such as validation (process simulation, including “what-if” scenarios or digital twin) and logical conformance (i.e. verification), optimization, and the ability to gain insight into process performance are all part of the intelligent Business Process Management Suite.
iBPMS also offers enhanced support for human collaboration such as integration with social media, streaming analytics, and more.
Optical character recognition (OCR)
OCR is the use of AI technology to recognize printed or handwritten text characters within digital images of physical documents, such as scanned paper documents. The basic process of OCR involves examining the text of a document and converting the characters into code that can be used to process the data. OCR is sometimes also referred to as text recognition.
OCR systems consist of a combination of software and hardware used to convert physical documents into machine-readable text. Hardware such as an optical scanner or a specialized circuit board is used to copy or read text, while AI-based software usually does advanced processing.
Natural Language Processing (NLP)
Natural language processing aims to build machines that understand text or voice data and respond logically with their own text or speech in much the same way that we, humans, do.
NLP combines rule-based modeling of human language (a.k.a. computational linguistics) with statistical, machine learning, and deep learning models. Together, these technologies allow computers to process human language as text or voice data and “understand” its full meaning, including the intentions and feelings of the speaker or writer.
NLP runs computer programs that translate text from one language to another, respond to voice commands, and quickly summarize large amounts of text—even in real time. Chances are you’ve interacted with NLP in the form of GPS voice systems, digital assistants, speech-to-text dictation software, customer service chatbots, etc.
However, NLP also plays an increasingly important role in enterprise solutions that help simplify business operations, increase employee productivity, and simplify mission-critical business processes.
Interoperability is key
While RPA solutions are often the products of a software development company that takes over individual tasks through programming, Hyperautomation goes a crucial step further. As part of software development, companies need to use different tools and utilities that work together. Interoperability is, therefore, the foundation for a highly efficient and effective Hyperautomation solution. Consequently, the various tools must be able to communicate with each other in order to generate the greatest possible added value.
Companies should say goodbye to classic technology stacks in the context of Hyperautomation. Instead, a holistic approach must be employed so that no negative effects are encountered when a new tool is introduced.
Only the interaction of all tools can make a positive contribution to improved processes. Accordingly, the focus is on technologies with plug-and-play solutions and APIs.
Why Hyperautomation doesn’t make the human factor obsolete
Hyperautomation doesn’t just refer to the mere implementation of new tools for managing business processes. Rather, people must cooperate with each other in the course of this development. Although software robots take over classic tasks that humans performed in the past, this doesn’t mean that the human factor loses relevance. Humans remain an important decision-maker in the company. The application of new technologies is also a task that people take on. As such, the interpretation of the data, as well as the logic behind the analyses, also remains a task performed by people.
Social media for customer engagement is an excellent example of the relevance of the human factor. In the future, companies will most likely implement various tools and utilities that leverage RPA and machine learning to achieve Hyperautomation of their social media marketing efforts. Consequently, extensive reports will emerge that paint a detailed picture of current customer sentiment.
However, interpreting this data is not the software’s job. Rather, the technology merely provides the foundation that is subsequently used by the marketing team. Based on the insights gained, this team can take various measures and define campaigns, promotions or incentives to address any shortcomings. In this way, satisfied customers can be retained and dissatisfied customers identified in a timely manner and turned into real brand supporters and advocates.
Hyperautomation vs traditional automation vs RPA
In contrast to software robots, Hyperautomation is characterized by a much wider range of functions. This allows employees to familiarize themselves more quickly with the latest business and market information. The bottom line is that the quality of the work performed improves.
The positive effects of Hyperautomation can be felt throughout the entire company. Here just to name a few.
- Costs are significantly lower because automation reduces the time and resources spent on manual tasks and also the number of errors.
- Processes become scalable as Hyperautomation transforms a manual, complex task into a reliable, repeatable process.
- Better business outcomes as a result of a collaborative ecosystem made up of technologies and people working together.
As an added benefit, process automation allows organizations to respond much more quickly to customer questions and needs – creating a personalized customer experience. Not only does this increase customer satisfaction, but at best, the positive experience leads to higher loyalty and more revenue.
Hyperautomation also creates an efficient workforce (consisting of human and digital colleagues) so that flexibility and sustainability enter the organization. When people can focus on value-added activities, it gives companies an important competitive advantage. Ultimately, this also benefits employees, who appreciate this work more. They can focus on activities and projects that add real value to the company and can thus help optimize the customer’s experience. This leads to happier employees because they now see added value in their work.
Studies show that employees who recognize the purpose and importance of their contribution, work harder and more efficiently.
Hyperautomation Benefits in a nutshell
- Agility and flexibility: hyperautomation relies on the seamless cooperation between a wide range of automation technologies. This allows companies to overcome the hurdles of a single digital technology. This makes Hyperautomation solutions more flexible and scalable.
- Better productivity: by automating repetitive, time-consuming tasks, employees can take on more valuable corporate roles that require less resources but better strategic focus.
- Integration: Hyperautomation allows companies to integrate digital technologies across different legacy systems, which allows for seamless communication between stakeholders.
- Better ROI: Hyperautomation elevates profits by reducing costs and potentially boosting revenue.
- Better analytics: a better ROI can also be achieved through advanced analytics to better evaluate trends and developments. Organizations can optimize their deployment of available resources.
- Adherence to compliance and reduction in entrepreneurial risk.
- Higher level of employee training.
- Better employee satisfaction and motivation.
- Focusing of cooperation and holistic approaches within the company and reduction of divisional responsibilities.
