What's the Difference Between Robotic Process Automation, Machine Learning, and Artificial Intelligence?
By Tori Cameron on July 17, 2019
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...?).
Hopefully, this short crash course will give you a better understanding of the differences between these terms and how they’re applied in process automation.
Let’s get started!
What is RPA?
As Jason explained in his post on RPA earlier this month, robotic process automation (RPA) is a software that helps a human automate a manual process. Unlike ML and AI, which are data-driven, RPA is process-driven. By process-driven, I mean that RPA works by being given pre-built processes—ones that are repetitive, rule-based, and usually require the human end-user to interact with more than one line-of-business system (such as SharePoint and Office 365)—that it then automates for the human end-user. Think of it as RPA being the brawn and ML and AI being the brains.
Why Should I Care?
When it comes to process automation, RPA does a great job with invoice processing (Super exciting, I know). Normally, an employee would retrieve the electronic invoices from their email, download the attached invoices into a folder, and create the bills in their accounting software. An RPA robot can absolutely help automate the manual parts of this process (retrieving the invoices, downloading them, and creating them), however, it would require some help from machine learning to finish the job; machine learning would need to intelligently “read” the invoices and extract the required information from them (supplier name, invoice number and due date, any other pieces of information it has been asked to extract, etc.) before handing the job back to RPA to create the invoices in the system.
Why is this? Well, RPA isn’t capable of intelligent “thought” like ML and AI is; every process that RPA automates must be explicitly programmed by a human beforehand so that the robot doing the automating knows exactly what to do. That means RPA is best used for automating rule-based, highly repetitive tasks.
What's Machine Learning?
Machine learning (ML) is an application of artificial intelligence that enables systems to learn from data without being explicitly programmed. ML is based on the premise that we can build technology that can process data and learn from the data on its own, without the constant supervision of programmers. It aims to learn from data, improving accuracy as it learns. Spotify uses machine learning in creating your Discover Weekly playlist every Monday; as you stream music, Spotify’s machine learning algorithms uses your data (i.e. the songs you listen to) to create a weekly two-hour-long playlist of music it thinks you’ll enjoy.
Netflix, YouTube, Amazon, and many other services you use every day also use machine learning in this way, using your viewing/browsing history to recommend to you content or products they think you’ll like. Have you ever noticed that services like these seem to know you as well as—or even better than—you know yourself? That’s because the more you use services like Spotify or Amazon, the more they learn about you; the more they learn, the more accurate their recommendations become. This is machine learning in action.
In process automation, machine learning can find like documents, identify what they are by using image recognition, and sort them under the correct classification. As you can imagine, classification is completed much faster with machine learning than when done manually by an employee; yes, even faster than when you’ve had too much coffee at work and type like this:
What's Artificial Intelligence?
Finally, we’ve reached the most mystified, easily misunderstood of the three terms.
Although artificial intelligence can seem like magic, behind the scenes, there’s really nothing mystical about it. At a high level, AI is essentially an umbrella term for various software—such as machine learning, as I mentioned earlier—that can demonstrate intelligence. Unlike RPA, AI is driven by data. And while machine learning aims to acquire knowledge, AI actually aims to become more intelligent. Its goal is to simulate intelligence. Most recently, AI's been in the news for its use in creating FaceApp, the app that uses AI technology to produce a creepily realistic aged version of a photo of your face. But probably the most well-known example of AI today is Sophia, the human-like robot created by Hanson Robotics in 2016. Sophia can (for the most part) hold conversations and generally act like a real person (again, for the most part). She doesn’t have a scalp though. Creepy!
Hopefully, this helped you better differentiate between RPA, ML, and AI. All three have very useful business process applications and can be enormously helpful in improving productivity. Now go out and flaunt your newfound knowledge—and try not to be haunted tonight by nightmares of Sophia trying to make human facial expressions.
KnowledgeLake provides content management solutions that help busy organizations intelligently automate their most important document processes. Since 1999, we've created award-winning, Microsoft-centric solutions that have helped thousands of companies around the world focus on their mission rather than their mission-critical documents.
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