The CIO of Global Foundries emphasized that AI agents require real-time, governed data, rebuilding a unified platform across fabs on three continents with centralized identity and audit trails before deploying agents across IT and procurement functions.

RAG (Retrieval-Augmented Generation) enables companies to build domain-specific chatbots, but its success hinges on the embedding model — the translator converting text into vectors for semantic search. A flawed embedding model retrieves irrelevant chunks, causing the language model to generate confident but incorrect answers. For example, a chatbot might wrongly confirm a refund for a 45-day-old subscription despite documentation stating a 30-day limit, because the embedding model failed to distinguish the critical time constraint.

RAG operates in two phases: indexing (chunking documents, generating embeddings, storing vectors with metadata) and retrieval (embedding the query, searching nearby vectors, reranking top chunks, feeding them to the language model). The language model cannot compensate for poor retrieval. Key considerations include evaluating embedding models beyond benchmark scores, choosing between commercial APIs and local deployment, understanding the cost of switching models later, and leveraging Matryoshka embeddings for flexible vector sizing.