While Large Language Models (LLMs) are exceptional at generating text and understanding unstructured content like emails and complex documents, and inference engines excel at logical reasoning and decision-making, they are just two components in a broader ecosystem of AI technologies. Other elements, such as image recognition, speech-to-text and deep learning, also play crucial roles. By integrating these diverse AI capabilities, we can create a powerful “new interface” that will enable dynamic interactions. AI is not designed to replace existing systems or handle every task. Just as you wouldn’t ask an engineer to perform a doctor’s job, AI should be viewed as a complementary tool, enhancing rather than substituting human capabilities. To maximize its effectiveness, AI must have access to both structured data (like databases) and unstructured data (such as internal documents or emails). This comprehensive access is essential for improving AI-driven decision-making and automating processes. Future AI architectures should be built to handle multi-modal inputs, including text, voice, images, and potentially other data types. This approach would support a more integrated and flexible method for managing diverse user interactions and requests.