Several people interested in learning or entering the tech industry often get confused between Robotic Automation, Machine Learning, and Artificial Intelligence. While at some point, these technologies worked independently, nowadays they generally work together in order to provide smarter business solutions that are efficient and resource-saving.
In this post, we will look at the major differences between these three mainstream, world-changing technologies and how they work in conjunction to provide some of the best technology solutions that make our daily lives much easier.
Robotic Process Automation (RPA)
Robotic Process Automation or RPA is a bot program based on an algorithm that helps automate certain business processes that we earlier performed manually. It is a process-driven system, which means that it performs certain sequential process-based tasks that are usually repetitive, rule-based, and require the human end-user to interact with more than one line of the business system.
In short, it can save time by parallelly completing redundant tasks such as downloading invoices and preparing account statements in the same format as it would be every day for employees.
That being said, these tasks call for handling tons of data.
However, RPA is a process-driven system. It is a fantastic tool for organisations to be able to deploy what is known as ‘Digital Workers’ that can be a true teammate for the human employees allowing them to be freed from the mundane, repetitive, daily computer based activities that drain creativity and swallow up working time.
That’s why today’s RPA software systems are generally adopted in organisations of all sizes. RPA is today being combined with other AI tools to make automation ’intelligent’.
Machine Learning
We are all familiar with how popular online platforms present us with smart suggestions based on our previous internet activities or the platform itself. For instance, as you stream content on Netflix, you must have seen an additional tab that provides suggestions with headings such as ‘Suggested For You’ or ‘Based On Your Interests’. Tech platforms such as Netflix, Apple, Amazon, Google, etc., use machine learning to process your data, i.e., the movies you watch, search terms, etc., to provide you with a more personalised user experience with the sole goal of retaining you as a paying customer.
From this example, you might have noticed that machine learning (ML) is a data-driven process, unlike RPA, which is process-based. While RPA can only understand structured data, machine learning works with unstructured data and analyses and learns from it.
Large companies have tons of unstructured data. With the help of ML, they can not only automate business processes but also learn from repetitive ones and eventually develop into smart solutions that can be much more productive than just RPA-based solutions.
Artificial Intelligence
Artificial intelligence generally refers to a system that almost has human-like capabilities except for the ability to feel and show empathy. Machine learning is one of the subsets that significantly contribute to building such a system.
Again, this makes AI a data-driven system. However, while machine learning propels data acquisition, AI works toward creating intelligent systems that minimise human intervention and errors.
To Summarize
Robotic Process Automation is a process-driven system that can automate business processes that are generally deemed repetitive and rule-based. These processes eat up time and resources that can now be efficiently sorted out using technology integrations such as RPA.
Today, technology companies strive to develop smarter systems by integrating advanced technologies like artificial intelligence and machine learning with brownfield technologies like RPA. Large companies that require advanced solutions now deploy AI with RPA, fused into a single automation solution that performs tasks seamlessly.
Alongside Siri and Alexa, we additionally witnessed the introduction of the metaverse. All these systems are products of these three technological interventions that can be only expected to grow further and bring many more world-changing solutions for the better.
