Difference Between RPA and AI


Difference Between RPA and AI

What is RPA?

Robotic process automation is an automation technology that acts like software designed to mimic human workflow patterns, process rule-based processes, and ultimately automate manual work. The technology is designed to alleviate repetitive, cumbersome, high-capacity and time-consuming tasks. The RPA robot does not make people redundant but is more like a virtual assistant, allowing you to offload repetitive tasks that are not complicated but consume valuable staff time.

RPA technology is process-based, which means you can simplify your business through interconnected communications within a digital system. Therefore, it is the most effective tool for companies to maximise efficiency, improve customer experience, and automate business workflows. 

For example, robotic process automation can do amazing things through invoice processing. Instead of manually retrieving and downloading invoices, RPA bots automate the manual part of this work and create invoices in accounting software. However, RPA needs ML algorithms to intelligently “read” invoices for a healthy and comprehensive process.

What is AI (Artificial Intelligence)?

Artificial intelligence simulates human intelligence and can make cognitive decisions. Unlike RPA, which simply mimics human behavior, artificial intelligence is data-driven and results-focused. These processes include learning (context rules for obtaining information and using information), reasoning (using context and rules to draw conclusions), and self-correction (learning from successes and failures). Popular applications of artificial intelligence include image recognition, computer vision, speech recognition, chatbots, natural language generation, and sentiment analysis.

This means that when there is a large amount of unstructured data, for example, artificial intelligence algorithms can analyse information patterns based on time, location, etc., and correlate the data and manage it appropriately. Artificial intelligence enhances any form of automation with cognitive capabilities and is suitable for performing more complex decision-driven tasks. 

RPA vs. AI 

We can compare RPA vs. AI with “doing vs. thinking” or muscle and brain. The difference between these two concepts lies in their approach. 

RPA: Process-centric 

RPA  automates regular, rule-based processes that typically need to interact with multiple IT systems simultaneously. It imitates the behavior of humans using the keyboard and mouse to work on different applications and platforms and strictly follows algorithms created by people to perform tasks. 

AI: Data-Driven

AI focuses on high-quality data. Artificial intelligence processes large amounts of data and converts it into actionable information by detecting potential patterns and connections. You must have data to train machine learning algorithms and to ensure that these samples are of good quality so that the output is representative of the data set and more. 

Earlier, we’ve mentioned about the ability of  RPA to automate daily tasks using invoice processing as an example. RPA can retrieve and download invoices and create invoices using “copy and paste” operations, but it requires artificial intelligence algorithms to extract relevant data (i.e., supplier names, invoice numbers, product descriptions, etc.) and interpret and manage variability intelligently. 

In RPA, the robot cannot extract specific information for each invoice received for clearly scripted activities. Therefore, artificial intelligence began to decipher data like humans. Artificial intelligence needs to train ML algorithms to recognise data (such as invoices) faster and more effectively than humans to obtain high-quality data.

In conclusion, many parts of a tedious workflow appear simple in RPA, but  robotic process automation requires the ability to manage more complex issues, especially in a more dynamic environment. When AI is installed with RPA, it will offer an excellent opportunity for businesses to process large amounts of data, lead highly automated and data-centric operations, and remove human intervention to manage bots and unstructured data. This will allow employees to work more effectively by focusing on business decisions rather than carrying out repetitive tasks. 

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