The demand for conversational AI is expanding beyond large-scale language models, with many enterprises struggling with information silos that hinder the sharing and surface of helpful business information. Knowledge graphs, such as those built using Neo4j Graph Data Science, have proven effective in building internal chatbots that can accelerate projects and timelines by providing contextual awareness, personalization, natural language understanding, and multimodal interactions. By capturing relationships between entities, building user profiles, leveraging NLP and graph algorithms, and representing different types of information, knowledge graphs enable chatbots to provide more personalized, relevant, and sophisticated responses, ultimately improving business outcomes.