The Automation age can simply be defined as the age of “Do it yourself.” The definitive meaning of the phase is that automation is going to be the future of business strategies.
At the time when computers were designed the developers were trying out the best approaches to expand the computing technology as much as they can.
Nowadays, RPA (Robotic Process Automation) is a simple way to mimic human actions and help in the conditional decision processes.
Thus, RPA technology can imitate human actions that involve a series of steps to perform a meaningful activity. However, RPA is slightly different from automation.
With automation, the current manual task can be performed such as calculation using a calculator, but this still requires human involvement to enter the values.
In the case of RPA, the values in the calculator can now be entered through bots also called web robots in place of a human. Now the question arises Is Conditional Decision Making out of the scope of RPA automation?
The answer to this question is No. Conditional decision making is within the range of the RPA regime and is not out of its scope. With the use of RPA, it is possible to take care of several decision matrices proficiently along with a simple rule-based decision that can be taken accurately.
RPA doesn’t require any complex form of decision-making processes. There exist four testing phases in Robotic Process Automation which includes Planning, Development, Testing, Support and Maintenance.
Which Automation Robots Exhibit Decision-Making Capabilities?
Decision-making capabilities are mostly achieved with the help of Artificial Intelligence. Cognitive Automation is one that exhibits decision-making capabilities.
Cognitive Automation is identified as a subdivision of Artificial Intelligence technologies that can process natural language as well as speech recognition.
Robotic Process Automation and Machine Learning are the two technologies discussed most of the time. RPA is process-driven whereas machine learning is the data-driven concept.
RPA is all about automating repetitive tasks that require interaction with multiple IT systems. On the other hand, machine learning is largely dependent on the analysis of good quality structured data.
Why the Speed of Decision Making is Important for Businesses?
The speed of decision making is extremely important for industries and their supply chains. Any delay at the time of delivery will have a direct impact on several business metrics.
This is where software robots will offer a competitive edge to enterprises, enabling humans to do innovative work. RPA can help organizations to make faster decisions by automating tasks delayed because of excessive process time.
In traditional automation process, a programmer would produce a sequence of actions to perform a task that integrates to the back-end system like an ERP or CRM using API.
However, RPA implementations can generate an action list by recording user performed tasks in the application UI. Those recorded tasks are directly and repeatedly performed on the UI, in an automated manner.
All you need is to get the right machine learning algorithm to interpret the processes smartly. Both machine learning and RPA can contribute significantly to transforming any enterprises digitally.
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A Near Perfect Solution
Businesses in the fast-moving industry segments cannot afford to ignore RPA. Several research reports have shown that nearly all high-end firms have started their RPA journey, and many are experimenting with pilot projects.
RPA offers not only productivity but faster processing of business processes. In today’s era of affordable automation, RPA’s rule-based efficiency have become smarter and more predictive. RPA’s aided decision making will surely provide a better foundation in the quest to remain competitive for several businesses.