What is the difference between RPA and AI?

Robotic process automation vs artificial intelligence

Now-a-days with artificial intelligence, data science and machine learning becoming buzzwords, people and organizations have started considering anything and everything as AI/ML or data science. Increasingly, robotic process automation(RPA) is conveniently considered as AI in today’s world when it is not. While learning and talking to organizations about their data practices, if a team or a person is unaware about the difference between RPA and AI, it just goes to show the depth of one’s understanding.

Photo by Franck V. on Unsplash

What is RPA?

The origin of the word “robot” comes from the Czech for “forced labor,” and in reality it just means that. A process that is defined as “robotic” is executing a set of instructions based on inputs.

Robotic process automation excels at carrying out strictly-defined workflows and executing repetitive operations.

What is AI?

Artificial intelligence can distill large volumes of information into focused, actionable insights based on the underlying patterns present within the data. AI is able to learn and adapt based on the information it acquires and the feedback it receives. It does this through the process of machine learning.

RPA and AI differences

From first principles, the difference is the same as it is between a totally static solution (RPA) and a dynamic one (AI) capable of changing and improving over time. RPA will execute a process in a highly repeatable fashion. AI will read between the lines. RPA will excel at carrying out a set, repetitive workflow, again and again but will be unable to adapt on the fly or mold itself to changing circumstances.

From an application perspective, most RPA technology is best-suited for tasks that are static and unchanging, like data entry. On the other side, if the team is trying to automate something that requires some characteristic of human judgment, the odds are that the team will need something with a little more intellectual firepower of ML.

As an example, if a team executes a set of automated processes based on an event, let us say a customer signup, a order closure or a case being created, these are all examples of RPA. On the flip side, based on a document generated invoice, if the application is able to read and interpret invoice, generate cash flow analysis and categorize the invoice based on its characteristics which are changing over time, it is closer to an ML application.

Engineering Manager | Founder of Acing AI

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