Vector: complete definition in AI for SMEs

In AI, a vector is a structured list of numbers representing an object (document, email, image, product) in a space where proximity = meaning similarity. It is the foundation of vector databases that power semantic search, recommendations and RAG.

What it really means

Let's break it down:

• each document is converted into a vector (via embeddings);
• a vector database stores millions of these vectors and answers "give me the N closest to this question";
• approximate nearest neighbor (ANN) makes this search fast even at scale.

For an SME, the vector is invisible: it is the mechanism that lets AI find "last year's Dupont quote" or "the quality procedure applicable to this check" in milliseconds.

Why it is strategic

The vector database is where your data "lives" for AI. Its architecture (chunks, metadata, index) drives the quality of everything above: search, assistant, agents. Structuring it correctly from day one — with an expert — avoids re-indexing everything later.