The mountaintop of process and organization efficiency is Automation driven by AI
Automation is becoming the go-to term in several industries. From self-driving cars to social media posts, automation has become omnipresent. According to a report, Robotic process automation (RPA) software revenue grew 63.1% in 2018 to $846 million. It became the fastest-growing segment of the global enterprise software market. It was slated to reach $1.3 billion in revenue by the end of 2019, a figure we have yet to check up on.
Even though RPA today is invading almost every industry, the major adopters of this tech are banks, insurance companies, telecom firms and utility companies. This is because companies in these sectors usually have legacy systems and RPA solutions get easily integrated with their existing functionalities.
RPA is often mentioned in the same breath as artificial intelligence, deep learning, machine learning and natural language processing. However, there are differences — many people think every aspect of automation is artificial intelligence, which is not true. RPA and AI are two horizontal technologies with a different set of goals and interfaces.
It’s easy to get robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI) mixed up—especially when people use them interchangeably. It can be confusing to differentiate between the three when they’re flying around in conversation, but they’re not as mystical as they seem: You use them every day when you ask Alexa to set a timer, listen to your recommended songs on Spotify, or break down and order those footie pajamas that Amazon has been recommending you to buy for the last two weeks (just me, or…?).
You can take a look at Facebook’s AI Research to understand better. Here, the social media giant feeds the AI system with different images and the machine delivers exacts results. When a photo of a dog is shown to the machine, it not only recognised it as a dog but also recognised the breed.
RPA is a technology that uses a specific set of rules and an algorithm and based on that it automates a task. While AI is focused more on doing a human-level task, RPA is practically a software that reduces human efforts — it is about saving the business and white-collar workers’ time. Some of the most common examples of RPA are transferring data from one system to another, payroll processing, forms processing etc.
Even though AI is steps ahead than RPA, these two techs have the capability to take things to the next level if both are combined. For example, suppose you need your documents to be in a specific format to get them scanned, and RPA does this job. If you use an AI system that would filter out the poorly formatted or unsuitable documents, the work of the RPA would be much easier. And this collaboration is called Automation Continuum.
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