Artificial intelligence (AI) systems are often trained on broad, general data rather than specific information about how a business operates. This means they lack what is called "business memory"—the detailed knowledge of an organization's unique processes, decisions, and records. As a result, many companies find it difficult to deploy AI effectively, as the focus tends to be on improving the AI models themselves, rather than integrating them with the internal data that could make them more useful. Most enterprises already have vast amounts of internal data, including contracts, workflows, and decisions, stored in various systems. However, this information is often fragmented and underused. Despite representing about 80% of enterprise data, only around 10% is actively used. This untapped data holds valuable context that AI needs to generate accurate and relevant outputs. To make use of it, organizations must create a "context layer" that ensures AI only accesses relevant, authorized, and up-to-date information. Creating this context layer involves identifying which internal documents are most authoritative, maintaining access controls, adding metadata to clarify information, and testing AI outputs to ensure they are accurate and useful. However, context alone isn’t enough. For AI to be truly trustworthy, systems must also track where information comes from (provenance), evaluate its reliability, monitor AI performance, and involve human oversight. Additionally, the data must be organized using an "ontology," a structured framework that defines key terms, relationships, and rules specific to the business or industry. An ontology helps AI understand how different pieces of information relate to each other and to the organization’s goals. This is especially important in regulated industries like healthcare and finance, where accuracy and compliance are critical. While unlocking the value of unstructured data is essential for AI, it must be guided by these frameworks to ensure proper understanding. Importantly, this doesn’t mean starting from scratch or replacing existing systems. Instead, organizations can build the necessary infrastructure to enhance AI performance and data integration, making the most of their current systems and data without waiting for major overhauls or new AI models.