What are the benefits of hyperautomation? The added value is immense: companies can make strategic decisions based on data from digital twins and optimize and better understand previously automated processes. Sounds simple in theory, but for most organizations it is a multi-year project with many individual steps and challenges. However, they can master these with the right approach.

One of the most important aspects: Hyperautomation is a company-wide process. This is because the wealth of data, organizational knowledge and expertise required for successful implementation is immense. It is therefore not only a technological challenge, but above all an organizational challenge that companies should face in 2022. In essence, Gartner defines hyperautomation as follows: "Hyperautomation is a business-driven, disciplined approach that enables organizations to quickly identify, review and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms."

Automation versus hyperautomation

The differentiating factor from pure automation lies in the overarching approach. One example: In a factory, thanks to digitalization, all relevant information about the status and use of the production machines can be accessed digitally and the resulting findings are used for automated decision-making. This process is part of the digitalization and automation of processes that has often already been implemented. When all factory information is recorded in the broader context of the entire company and machine learning (ML) and artificial intelligence (AI) analyze the processes in order to optimize capacity planning, logistics or research, for example, this is known as hyperautomation.

Hyperautomation does not only affect industry, but can be implemented in any company. "Digital native" organizations, which have always focused on data-driven decision-making processes and usually have high data quality and knowledge of how to use this data, will certainly benefit the most. They can automate entire end-to-end workflows. In the HR department, for example, the entire process from the selection of applicants and recruitment to the training, development, support and retention of employees can be digitized. At the same time, it is possible to link with other departments such as IT, which in turn provides the right devices or access for employees. This enables companies to standardize best practices, improve efficiency and eliminate staff shortages.

The virtually cloned company

No hyperautomation without data of the highest quality and a high degree of standardization and integration.

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The benefits are many, but before implementing it, companies should think about what they want to achieve with hyperautomation. Is it primarily to reduce costs or rather to keep up with the competition? One of the most interesting applications is identifying how your own company works and asking which processes are still in place and which should be rethought. Digital twins make it possible to view a virtual version of the entire company, change processes, try them out and ultimately optimize them - all without any negative impact on ongoing operations.

With the increasing digitalization and software-based nature of companies and the networking of physical assets, it is possible to create virtual representations of entire systems. In this case, the system is the company itself. Digital twins are not simulations or models. They are digital counterparts that are created from real-time data streams and are theoretically capable of representing the past, present and possible future of the system. Industry has been using such applications for some time to monitor property, plant and equipment and infrastructure and to act with foresight. Extending this concept to business operations opens up almost unlimited possibilities. For example, management can make important strategic decisions and refine forecasts based on far-reaching performance insights. It can also model different ways of executing certain processes to determine the best possible approach. The main benefit is to understand business processes through structured and digital mapping and gain complete transparency.

It all comes down to the data

However, in order to use digital twins and take advantage of these exciting new opportunities, organizations need data of the highest quality and a high degree of standardization and integration. Because: no hyperautomation without data. Data science and hyperautomation are both trends that have really taken off in recent years and are closely intertwined. The skills and expertise of data scientists are indispensable. Establishing this type of expertise and mindset in the company is an initially time-consuming but rewarding change.
Although many companies have an incredible amount of data, they find themselves in the "80/20 trap". The organization spends 80% of the effort on data provision and preparation and only 20% on analysis and optimal use of the data. However, the use of AI and ML in combination with data opens up optimization opportunities that go far beyond all current analysis applications. The basic prerequisite for this is a solid database and analysis. Hyperautomation then gives data teams the flexibility they need to improve the profitability of data provision by eliminating mundane tasks and leaving the higher value work to humans.

Successful implementation of hyperautomation

In practice, setting up a hyperautomation infrastructure can be divided into two phases. Phase 1 is used to create a solid basis - here companies enter at different points depending on the progress made. Phase 2 is for the actual implementation of hyperautomation.