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How intelligent process automation combines RPA and machine learning
How intelligent process automation combines RPA and machine learning

With regard to concrete projects in German companies, an IDC survey concluded that, at the moment, many companies (37 percent) are working on using AI to filter out findings from the data available in their departments. 32 percent and 23 percent, respectively, want to advance speech and image recognition projects. A quarter are planning to introduce supervised learning and 23 percent are considering automated content aggregation. The difficulty is that there is often a lack of comprehensive and well-founded strategies and methods that take all areas and concerns of the company into account.

This is because individual tools, systems or new devices are not sufficient to handle this. According to a recent study by TCS and Bitkom Research, 75 percent of companies focus on digitisation strategically, but only 39 percent control the various aspects of digitisation as part of a cross-departmental strategy. There is a lack of comprehensive and methodical approaches as well as clear distinctions of terms. For example, AI and Robotic Process Automation (RPA) are all too often mentioned in the same breath, even though there are significant differences. While machines and therefore robots can be intelligent and contain self-learning components, they are missing a thinking component, which is at the core of artificial intelligence.

Difference between RPA and AI

Robotic Process Automation (RPA) is an efficient and intelligent approach for accelerating digital transformation in companies and tapping into direct cost reduction potential. These in turn can be used to generate financial resources for further digitisation strategies. RPA can take over repetitive tasks, reduce workload and thus offer more freedom for value-added activities. The human employee no longer has to do everything by hand. AI always makes sense in connection with RPA if the intent is to approach automation holistically and strategically. In such a scenario, AI is particularly suitable for data structuring processes, process mining and as an RPA enabler, for example, to identify processes to be automated.

RPA as an approach

Since there are still many misconceptions, it is worth asking what exactly is RPA and what can it do? RPA is a minimally invasive technology for the automated handling of rule-based business processes by software robots. These virtual assistants can be divided into the following three automation types, here sorted according to their increasing level of intelligence: Robotic Desktop Automation or RDA in short, runs on the user‘s desktop. As a rule, the user cannot continue with his work during automation. The next step is Robotic Process Automation (RPA). This is a scalable solution that can be adapted to the individual process requirements of the user. RPA uses multi-skill robots and works independently in the background. The third and smartest type of automation is Intelligent Process Automation (IPA).

This technology enables companies to handle and use even unstructured data. IPA extends automation solutions by adding AI components, such as machine learning. Machine learning and AI in connection with process automation
are currently primarily used where large amounts of data are analysed, compared or structured. While AI covers the area of learning and thinking, RPA takes care of the handling of the respective work steps. To automate business processes efficiently, intelligently and in a forward-looking manner, it makes sense to combine both areas. Examples are so-called „self-healing robots“, which can independently intercept any changes in the graphical user interfaces of the automated programs following a software update or patch and repair themselves.

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