As is the case with other emerging branches of AI, the capabilities of conversational AI tools are systematically improving, thereby ensuring these tools are no longer relegated to simplistic queries and answers with consumers.
Conversational AI is making its way into more types of applications, such as getting answers to customer service questions or checking HR policies. The most obvious use cases for conversational AI are chatbots or digital assistants that answer repetitive questions. However, augmented analytics platforms are also taking advantage of conversational AI to allow more people in an organization to interact with data. Instead of typing an SQL query, a business user can simply type a natural language query which is answered in natural language.
Conversational AI is enabled by natural language processing (NLP) , specifically natural language understanding (NLU) to determine what a user is trying to communicate and natural language generation (NLG) to respond back to that person. It also uses machine learning to constantly improve its accuracy.
One major criticism of conversational AI is that it doesn’t understand human emotion, though that’s changing. The more human AI appears and behaves, the less likely it is for an interaction to be escalated.
Source: techtarget.com
