Enterprise leaders are increasingly focused on leveraging Generative AI to create measurable business value, with a strong emphasis on managing risks in production settings. A primary challenge in deploying AI models is addressing "hallucinations," where AI generates plausible yet incorrect responses. Grounding is a crucial process that ensures AI systems rely on specific, authoritative organizational data rather than generic public information, thus transforming AI from speculative projects to strategic investments. Retrieval-Augmented Generation (RAG) is a prevalent technique for grounding, enhancing AI reliability and trust by providing verifiable and context-specific responses. Overcoming challenges in grounding, such as balancing precision with recall and ensuring factuality, is essential for creating trustworthy AI systems that deliver significant return on investment (ROI). AI21 Labs' Jamba models exemplify advancements in grounding, featuring improvements like a 256K-token context window and a hybrid architecture, which enhance the accuracy and efficiency of AI responses. As enterprises navigate these complex challenges, grounding remains a foundational discipline for building trust and unlocking the potential of AI technologies.