AI, RPA, and Machine Learning


by Apoorva Komarraju
May 3, 2021

Here is a guide to understanding the unique uses and choosing the right technology for your business.

AI, RPA, and machine learning, you must have heard these words in the tech industry. Be it blogs, websites, videos or even product descriptions, disruptive technologies have made their presence strong. The fact that we all have AI-powered devices in our homes is a sign that the technology is so advanced.

If you think AI, robotic process automation, and machine learning have nothing in common, here’s what you should know: They are related concepts. Often times, these names are used interchangeably and incorrectly, creating confusion for companies looking for the latest technological solutions.

Understanding the differences between AI, ML, and RPA tools can help you identify and understand where the best opportunities are for your business to make the right technology investments.

The big difference – RPA

According to IBM, “Robotic Process Automation (RPA), also known as software robotics, uses automation technologies to mimic back office tasks of human workers, such as extracting data, filling out forms, moving files, etc. It combines APIs and user interface interactions (UI) to integrate and execute repetitive tasks between enterprise and productivity applications. By using scripts that emulate human processes, RPA tools complete the autonomous execution of various activities and transactions across independent software systems. “

With this in mind, RPA tools enable highly logical tasks that do not require human understanding or intervention. For example, if your work revolves around entering account numbers on a worksheet to run a report with a filter category, you can use RPA to fill in the numbers on the sheet. The automation mimics your actions when you set up the filter and generates the report itself.

With clear instructions, RPA can perform any task. But there is one thing to remember: RPA systems do not have the ability to learn as they go. If your task changes (for example, if the filter in the spreadsheet report has changed), you will need to enter the new instructions manually.

Industrial applications

The biggest users of this technology are banking, financial services, insurance, and the telecommunications industry. Federal agencies like NASA have also started using RPA to automate repetitive tasks.

The big difference – AI

According to Microsoft, “artificial intelligence is the ability of a computer system to deal with ambiguity by making predictions using previously collected data and learning from errors in those predictions to generate newer, more accurate predictions of future behavior.” “.

With that in mind, the main difference between RPA and AI is intelligence. While these technologies perform tasks efficiently, only AI with capabilities similar to human intelligence can do so.

Industrial applications

Chatbots and virtual assistants are two popular uses of AI in the business world. In the tax industry, AI is making tax forecasts more and more accurate with its predictive analysis functions. AI can also perform in-depth data analysis, which makes identifying tax deductions and tax credits easier than before.

The big difference – machine learning

According to Gartner, “advanced machine learning algorithms are made up of many technologies (such as deep learning, neural networks, and natural language processing) that are used in unsupervised and supervised learning and are guided by insights from existing information.”

Machine learning is part of AI, so the two terms cannot be used interchangeably. And that’s the difference between RPA and ML, machine learning intelligence comes from AI, but RPA lacks any intelligence.

To better understand this, let’s apply these technologies in a property tax scenario. First of all, you can create an ML model based on a hundred tax bills. The more invoices you add to the model, the more precisely the predictions for future invoices will be made. However, if you want to use the same machine learning model to edit an assessment note, the model is useless. You would then need to create a new machine learning model that knows how to handle assessment cues. This is where the intelligence capabilities of machine learning draw a limit. If ML does not recognize the similarities of the document, an AI application would recognize it thanks to its human-like interpretation abilities.

Industrial applications

The healthcare industry is using ML to accurately diagnose and treat patients, retailers are using ML to get the right products in the right stores at the right time, and drug companies are using machine learning to develop new drugs. These are just a few of the uses for this technology.

Is RPA part of the AI?

No, but they can work together. The combination of AI and RPA is known as Smart Process Automation or SPA.

Also known as intelligent process automation or IPA, this duo enables an automated workflow with advanced capabilities as RPA using machine learning. The RPA part of the system works on completing the tasks, while the machine learning part focuses on learning. In short, SPA solutions can use patterns to learn to perform a specific task.

The three technologies AI, RPA and ML as well as the Duett SPA offer exciting possibilities for the future. But only when companies make the right choices can the fruits be harvested. Now that you know the different capabilities of these technologies, you can adapt and innovate.

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July 5, 2021