Robotic process automation (RPA) is likely to be the year’s most popular term in the tech world. And for a great reason. RPA is an innovative technology, that automates business processes using software robots that perform repetitive and monotonous tasks, their susceptibility to error is zero.
It’s like magic!
Due to robots, the employee no longer needs to waste time performing manual activities that are nerve-wracking and time-consuming. Software robots make relieved, happy workers that have additional time for tasks that actually matter, to improve the work outcome, adding a pinch of value to the employees' daily life.
None the less, RPA has already proven to be an effective and intelligent driver that pushes digital transformation in businesses by increasing productivity, reducing cost, and finally resulting in revenue and business development. Hence, RPA is more than just a buzzword, which is why its market size is expected to reach $3.97 billion by 2025.
How can we make RPA intelligent?
We learned that software robots automate repetitive and routine tasks, thereby significantly increasing employee productivity and customer satisfaction.
But these robots can’t make decisions or draw any conclusion on their own, they are not smart. This is where machine learning (ML) comes in.
Linking machine learning with RPA is meaningful if business automation is pursued in an integrated and strategic manner. To automate business processes efficiently, future-oriented, and strategically, combining both technologies is essential.
By infusing intelligence into RPA, hence mixing machine learning capacities with process automation, we could design an advanced type of RPA, a software robot which can analyze, comprehend, and draw conclusions from both structured and unstructured information. This effective symbiosis is consequently able not merely to process, but effectively utilize data. This recently created smart RPA assesses data before acting on it, continuously learns from information, becomes smarter over time, makes smart decisions based on previous learning.
Therefore, automating processes with the support of both RPA and ML especially makes sense whenever an enormous number of data has to be processed, analyzed, compared, and structured. While ML covers the job of learning and thinking, RPA executes. ML functionalities that come to perform in relationship with RPA are, for example, technology like speech and image recognition or record information extraction.
In conclusion, combining these two advanced technologies will only enhance any process in any business, with incredible results in satisfaction (company, employees, clients), increased revenue, and productivity.
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