How RPA and machine learning go together to achieve more efficiency
It is well known that many companies are increasingly working on automating their internal work flows as much as possible so as to be able to remain successful in a digital future. In this context, many terms are used that are already so widely used that nobody inquires into their meaning. But if you work more intensively with them you find out that they are not so clear.
The terms “Robotic Process Automation” (RPA) and “Machine Learning” are frequently heard in discussions about automation. But what can these concepts actually contribute to corporate work flows? How do they differ from one another? And is it possible to use them together?
Robotic Process Automation: Handling routine tasks without any great effort
We can clear up one point right away: “Robotic Process Automation” has nothing to do with robots as generally understood. We’re not talking about large machines that are used in manufacturing or series production here, but software solutions that work independently just like a robot.
Robotic Process Automation primarily refers to a standardized and consistent automation of a process. RPA programs are programmed in such a way that they know how to act when a decision is required so as to fulfill their function – the decision paths are thus specified precisely.
The capabilities of such RPA automation systems remain unchanged unless a programmer intervenes and undertakes optimization measures informed by experience. This means that what we think of as “intelligence” in the IT world plays no role here – it is simply the integration of the stock of human experience, but not its autonomous optimization.
RPA applications are used, for example, for processing forms, for the automation of customer service or for typical back office tasks. Here they are intended to provide more efficiency, faster operational processes and error-free work flows. In addition, they create additional freedom – the staff profit from having more time for more important tasks. Last but not least, they reduce costs significantly; the resources that are freed up can be used for more radical digitization projects.
Machine Learning: an important constituent part of Artificial Intelligence
One disadvantage of Robotic Process Automation is that such solutions cannot adapt automatically to changed overall conditions. They also cannot convert new experience into application knowledge. Concepts from the field of Artificial Intelligence are suitable for such purposes.
The core of AI is that machines and units are provided with the ability to be able to handle tasks by themselves on the one hand, and to be able to learn from success and mistakes on the other hand. In this sense, machines should be able to imitate human behavior, however, this being in areas that humans could only manage from enormous amounts of effort.
Along with neuronal networks and Deep Learning, Machine Learning is among the best known applications of KI. Systems that master machine learning collect and analyze data and automatically adapt their behavior on that basis.
Amazon shoppers encounter this in the form of carefully tailored buying recommendations; the same applies to individual search results. Social Media also makes use of machine learning to make the feeds as user-oriented as possible.
Machine Learning thus differs significantly from Robotic Process Automation. While RPA is designed to solve routine tasks automatically and efficiently, Machine Learning also seeks to respond to new problems as effectively as possible. But however great the differences between the two may be, the question nonetheless arises of whether it might be possible the draw on the advantages of both concepts together.
How Robotic Process Automation and Machine Learning work together
What do companies gain when they link RPA and machine learning? Simply put, it greatly expands the potentials of Robotic Process Automation.
When Robotic Process Automation is supplemented with such forms of digital intelligence, we can then also speak of“Intelligent Robotic Process Automation”. The degree of automation is thus raised to a new level; the RPA solution becomes more independent and can also handle more complex situations. This makes sense in particular when it is not only necessary to manage massive quantities of data, but also manage the associated tasks even better over time.
What are conceivable scenarios for the use of Robotic Process Automation in conjunction with Machine Learning? One possible area of application is the collection and processing of customer data. Subsequently RPA and Machine Learning can be used for the entire management of customer-related processes, starting from the very first customer contact up to the closing of the sale. Ultimately, such a software combination can provide greater clarity and more transparency, especially in the financial sector, and in the best case can offer Predictive Analytics as well.
Simply put: Machine Learning thinks, RPA acts. In the long term, companies can save a huge amount of time and money through this combination. This strategy significantly improves analysis, decision making and execution by a machine. Last but not least, media and communication breaks can be reduced.