Contextual AI enables systems to interpret information like humans do, allowing for more accurate and relevant outputs. It takes a human approach to processing content, recognizing cultural and environmental contexts, and analyzing the tone of voice or emotions behind it. This results in more collaborative partnerships between humans and machines, providing dynamic conversations, highly relevant responses, and generating increasingly accurate predictions. Contextual AI requires domain knowledge, explainability, and customization to build intelligent systems that can learn in an unsupervised manner, transfer knowledge between contexts, and infer context to problem-solve. It is particularly useful in scenarios involving self-driving cars, facial recognition, quality control, voice-based assistants, and conversational agents, where it can make human-like behavior, meaningful insights, and better user experiences.