Conversational AI, is the quintessential part of marketing and audience engagement strategies. Traditional methods like IVR have become passe as they cannot handle complex problems such as responding to a blind person, explaining the way they understand, or having omnichannel support. Conversational AI obviates the need for people to stand in queues and keep on punching numbers all to get a reply, which is even remotely connected to the question. Conversational AI with its omnichannel and multilingual support, has become inevitable and indistinguishable from human-like communication. This is precisely because it can process the random conversational data well, only because it can listen to human conversations and extract patterns. Also called Natural Language Processing (NLP), conversational AI tools are evolving rapidly to adapt to human conversations. According to research by Allied Market, the global market in conversational AI was $5.78 billion in 2020 and would go up by $32.62 billion by 2030.

Interaction between other devices and technologies like intelligent virtual agents or chatbots is an essential part of faster and more reliable conversational AI. A human conversation is certainly a nuanced art, evolved over centuries, and certainly, algorithms would need to do a lot of eavesdropping, for them to sound human. Grasping context, and relevance to deliver a personalized experience would require more than feeding data. To overcome this hurdle, NLPs designed to flow into a loop for machine learning algorithms to process the next response instantly based on the previous conversation. With this machine learning algorithms help enhance the learning process along with improving the conversation quality. Getting the algorithms unstuck from unforeseen and uncertain frivolity in human conversation remains the goal.