Let’s look at a few ways it’s being used in healthcare, retail, HR, finance, and banking. There are many examples of conversational AI use cases in different industries. This year, millions are forecasted to be using voice assistants, and it will likely continue to grow as technology progresses.Įxamples of industry use cases for conversational AI It can then pivot its responses and optimize as needed to give customers the answers they need without having to involve a human agent, which extends your customer self-service even further. (Learn more about NLP in customer service.) Not the best conversational experience.Ĭonversational AI, on the other hand, is much better at understanding more complex needs and conversational styles via NLP and deep learning, and keeps getting smarter as it learns more about patterns or frequently asked questions from customers. ![]() ![]() Through advanced machine learning, ASR ( automatic speech recognition), natural language processing (NLP), and natural language understanding (NLU) technology, conversational AI can provide even more accurate answers and resolutions for customers than your typical chatbot-with less human error and more human-sounding dialogue.įor example, with a traditional chatbot, a customer would have to choose between multiple choice answers to a preset question, like “Refund,” “Support,” and so on in response to “How can I help you today?” From there, they’d go down the branches of that question tree to (hopefully) resolve their issue. ![]() Instead of having a rigid set of standard answers that responds to preset questions or inputs (like traditional chatbots), conversational AI can provide more varied, context-dependent responses. Conversational AI (artificial intelligence) uses natural language processing (NLP) and machine learning to essentially simulate natural-sounding conversations with computer programs.
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