How AI and ML Can Up An Organisation&#8217;s RPA Game
As technology continues to advance, especially AI, the world is witnessing some of the most amazing use cases of technology. Automation is one such process that emerging in business process management (BPM) in the form of robotic process automation (RPA). And the impact RPA seems to be significant enough as it has the ability to rapidly drive the automation of business processes without disrupting existing enterprise applications. However, while many companies across the world are relying on RPA tools with human effort, there are other companies that are making the most of AI. If you don’t know what exactly RPA is, it is a technology that lets a company configure its computer software, or a robot mimic or match the level of a human to execute a business process. So, how does RPA help business? An RPA software is designed and built in such a way that, just like a human, it can perform human tasks such as run applications, move files, copy or paste files, fill in forms and more. RPA is best suited for businesses with a process that keeps changing and are also repetitive. Also, business with costly or complex to re-engineer processes, RPA is a great choice. Therefore, using RPA software, a business can increase process efficiency. That is not all, another major advantage of using an RPA software is the fact it can be shared with another department as it can be customised. When AI & RPA Works Together While RPA is becoming the talk of the town, the use of AI in RPA is becoming a significant, strategic investment in the enterprise world. Many companies across the globe were not aware of how to make the most of unstructured data. However, off lately, they have started to see how. While data scientists are extracting relevant and critical insights out of unstructured data, many have found out that this data can also make a great impact in unleashing the full potential of intelligent RPA. Talking about data, it is difficult to extract insights from unstructured data and that is why most companies struggle. While on the other hand, Artificial intelligence over the years has been a vital part in making the most out of data. And in this scenario, insights from sources like emails, annual statements, contracts, or other types of documents need to be extracted, and this can be done using AI and ML. UiPath has taken significant steps towards making RPA go-to in today’s business world. The company is actively using artificial intelligence and machine learning to read structured and unstructured data and converting them into digital data that can be used. “We have been using AI, ML, NLP and cognitive computing in our products and they are directly integrated. We have integrated ABBYY to process structured and unstructured documents. Also, we have integrated Python development in UiPath; meaning, a person who is a hardcore AI developer writing python code can directly put that into UiPath and start using it,” said Raghunath Subramanian, President and CEO, UiPath India. Outlook Artificial Intelligence and machine learning over the years have proved that these sought after technologies have the ability to transform almost every sector for the good and RPA is no exception. Witnessing the way UiPath is leveraging the superpowers of these technologies, it seems that with time, it is just going to get bigger and better.
As technology continues to advance, especially AI, the world is witnessing some of the most amazing use cases of technology. Automation is one such process that emerging in business process management (BPM) in the form of robotic process automation (RPA). And the impact RPA seems to be significant enough as it has the ability to rapidly […]
RPA is a technology that lets a company configure its computer software, or a robot mimic or match the level of a human to execute a business process. 
["Automation", "RPA", "uipath"]
["Go", "artificial intelligence", "machine learning", "AI", "RPA", "ML", "Automation", "Git", "RAG", "NLP", "Python", "uipath", "R"]
["AI", "artificial intelligence", "machine learning", "ML", "NLP", "RAG", "Python", "R", "Go", "Git"]
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