Every enterprise AI initiative contains an architectural decision that rarely makes it into the business case or the steering committee deck. It doesn’t have a line item. It often gets made by a developer on a Tuesday afternoon based on whatever the default configuration was. And it determines, more than almost anything else, whether your AI system produces answers worth trusting.
The decision is this: How should your AI system be architected to find, relate, and reason over information at the moment it needs to? Three dominant architectural patterns answer that question differently — vector embeddings, knowledge graphs, and context graphs. They are not competing technologies. They are different approaches to a fundamental problem, each with distinct capabilities, costs, and failure modes.