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Intelligent Process Automation (IPA) combines RPA and AI to transform customer service, offering businesses a powerful tool to enhance efficiency and satisfaction.
Copyright: techopedia.com – “Intelligent Process Automation (IPA)”
What is Intelligent Process Automation (IPA)?
Intelligent Process Automation (IPA) is a sophisticated technology that blends traditional automation techniques with artificial intelligence (AI) to create systems capable of handling complex tasks that usually require human cognition.
IPA leverages robotic process automation (RPA) to perform routine, rule-based tasks and improves these capabilities with AI technologies such as machine learning (ML), natural language processing (NLP), and cognitive decision-making.
IPA started gaining traction in the early 2000s as businesses looked to enhance efficiency. The automation was initially limited to simple, repetitive tasks that could be easily codified into software routines.
But, as the volume and complexity of data increased, it became evident that basic RPA could not handle processes involving unstructured data or decisions that required context understanding.
The integration of AI with RPA was a response to these limitations. AI technologies brought the capability to analyze large volumes of data, understand natural language, and make informed decisions based on patterns and context that were not explicitly programmed.
By the late 2010s, IPA systems had begun to take on tasks that were previously thought to be possible only for human workers, such as interpreting documents, making customer service decisions, and even predicting outcomes based on historical data.
Techopedia Explains the Intelligent Process Automation (IPA) Meaning
The simple intelligent process automation definition is a technology that combines robotic process automation with artificial intelligence to automate complex business processes that require human-like judgment and decision-making.
IPA uses machine learning, natural language processing, and cognitive computing to learn from data, make decisions, and manage workflows that involve both structured and unstructured data.
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