Software robot-controlled process automation (Robotic Process Automation, RPA) is a technology that has emerged from classic process automation. The spectrum ranges from simple, manually set up workarounds to complex software on a virtual machine. Thanks to RPA, companies can run their processes autonomously and at the same time increase their system security. Software robots mimic industrial robots. They automate processes that are not adequately mapped by the applications themselves.
This gives employees valuable time to concentrate on other, more critical tasks. This includes customer service, for example. RPA is not only about cost savings but also about increasing and standardizing the quality level. The provision of services is highly dependent on processes that require particular judgment. Companies that use artificial intelligence (AI) and machine learning in addition to software-robot-controlled process automation can use resources, time, and money far more sensibly.
For example, back-office functions can be made more efficient through software robotics, with the more productive robots replacing their human predecessors. Software robots can work around the clock if necessary, not take a vacation, and not get sick. As a result, the available working hours per year are five times that of an average human worker, and tasks can be completed ten to twelve times faster. This can increase a company’s productivity by 50 or 60 times.
The Two Types Of RPA Robots
- Served or supervised: Human users control the process and determine when transactions are carried out.
- Unattended or unsupervised: The software robot can carry out transactions continuously and without human involvement. People only intervene in exceptional cases.
The “intelligence” of a software robot depends on how much it can learn while executing its processes. Simple RPA robots that carry out unattended or operating procedures must be trained: They lack intelligence or independent learning ability and are dependent on regular software updates. Such applications can be automated very quickly depending on the complexity of the logic with which the robot has been equipped.
So-called cognitive technologies based on artificial intelligence (AI) and machine learning significantly expand the capabilities of software robots. Machine learning refers to software that can recognize and analyze patterns and adjust the future behavior of the software accordingly. Such systems can be impressively complex and appear “intelligent” through extensive logic to explore ways. In the case of “real” AI, on the other hand, software systems can learn and adapt beyond their original environment. AI systems can evolve and change their behavior without human intervention. Almost every second company in the telecommunications industry is therefore already relying on artificial intelligence.
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The Two Types Of RPA Applications
- Supervised applications are prepared and programmed offline and then embedded in the live environment. This type of AI is trained with the help of large data sets to develop classification functions that are used, for example, for the following applications:
- acquisition: visual Information such as photos or other images used for face recognition Audio analysis: transcription of spoken language, speech dialogue systems (IVR) or voice UI / UX
- Data extraction: from unstructured documents or for automated online assistants and chatbots
- “Unattended” applications receive training similar to the simpler versions but can study and learn from their environments. This allows them to adapt and adopt new behaviors. This increases their ability to make decisions and makes their thought patterns more like a human.
- This type of AI is used for data analysis, for example, to recognize patterns in customer data.
- Providers such as Google and Facebook use this type of application to analyze the behavior of their users and provide them with customized search results, news feeds, and advertising.
AI is supplemented by options such as natural language processing (e.g., understanding unstructured documents such as e-mails or letters), conclusions (actions based on the available data), and anticipation (e.g., assessing purchasing behavior based on previous purchases). This is how data is converted into helpful Information.
Despite their similarities and overlaps, there are significant differences between RPA and AI. As mentioned earlier, RPA software robots precisely perform tasks they have been taught – ideal for rule-based processes where compliance and accuracy are critical. With ambiguous inputs or data (e.g., unstructured e-mails) or a large amount of data, machine learning and AI are better suited, as they handle ambiguity better and learn and optimize their skills over time.
RPA Alone Is Not Enough
Thanks to RPA tools, employees have more time for more critical tasks that require judgment and experience. However, with RPA alone, the full potential for cost savings and increased efficiency cannot be exploited. To fully benefit from RPA and cognitive technologies, companies must use additional tools to analyze the enormous amount of operational and customer data collected and generated by the robots in every work process (keyword: big data).
Software robot-controlled instruments alone are only able to develop end-to-end process intelligence to a limited extent. During individual, small-scale work steps within a process, the robots continuously collect a certain amount of data and monitor their local activities. However, robot-controlled instruments do not provide the necessary visibility of the overall process status, for example, to evaluate the effects of automation on the service level. They do not provide the required knowledge about possible ways of optimizing the process.
What Is Operational Intelligence?
Operational Intelligence (OI) is the latest form of data analysis that companies optimize their business processes. OI is an improvement over previous analytics that reported past events. OI systems examine current events, provide recommendations and notify in real-time that predefined threshold values have been reached. With the help of OI platforms, companies can react quickly to changing business conditions and predict process courses and results based on trends determined by a primary data set within the company data.
Suitable OI tools include the following functions:
- Customer analysis to evaluate customer data and activities to understand their behavior, improve customer benefits and loyalty, and increase sales
- Operational analysis to measure and ensure quality, ensure compliance and reduce risks, demand management, and optimization of business processes based on the efficiency of the transaction processes
- Use of appropriate methods, knowledge, and experience to identify the correct parameters
However, it is essential that the company and management also understand this Information as a call to action and optimize business processes accordingly, regardless of whether they are carried out by humans, software robots, or both.
Why Operational Intelligence Is Essential To RPA
With OI tools, users can make faster, smarter decisions. They enable companies to combine data from robotic, transactional, analytical, and workflow and database-based systems to develop a single customizable overview of key metrics and performance indicators for the company and its processes. This, in turn, enables companies to make optimal use of their operational data.
They can use the knowledge gained for their decision-making and, if necessary, change or set up their processes and develop and offer new products and services faster and more effectively. To successfully implement an OI tool to support RPA, integration into the transactional RPA system is required. In addition, the results and analyzes of the system must be taken up by the people who carry out the operational processes so that a culture of continuous optimization is possible.
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