Conversational artificial intelligence (AI) uses machine learning and natural language processing to interact with humans, addressing problems and influencing customer interactions. It guides conversational messaging systems that accomplish tasks using natural language, recognizing context and maintaining conversation. Conversational AI is built upon NLP concepts such as named entity recognition, intent classification, dialogue management, and sentiment analysis. It can be implemented in various ways, including bot frameworks or large language models (LLMs), with LLMs being better at understanding natural language but often requiring more resources and fine-tuning. Businesses should consider whether conversational AI can increase revenue, create new products, or reduce costs before implementing it, as its suitability depends on the benefits it offers. The key advantages of conversional AI include increased revenue, enhanced customer experience, informed innovation, reduced operational costs, and improved productivity. However, chatbots differ from conversational AI systems in terms of predefined content versus dynamic content, simple use cases versus complex use cases, rule-based logic versus language model-based implementation, and simple understanding versus reasoning capability with context resolution.