How Businesses Can Prepare Their Data for AI

Businesses often focus on choosing the right AI model or provider. In practice, the more common limiting factor is the data the AI has access to — how complete it is, how consistent it is, and whether it's actually reachable by the system trying to use it.

An excellent model given poor or inaccessible data will still produce poor results.

Structured data — like CRM records or database tables — is usually the easiest for a system to use directly. Unstructured data — documents, emails, call notes, PDFs — holds a lot of valuable context but needs to be organized, indexed, or summarized before AI can use it reliably.

Most businesses have far more useful unstructured knowledge than they realize; the challenge is usually organization, not scarcity.

Duplicate records, outdated information, and inconsistent formatting all reduce how useful data is for AI. A practical starting point is often smaller than expected: clean up the records the business relies on most, rather than trying to fix everything at once.

For AI to use business knowledge, there usually needs to be a pipeline that gathers relevant information and makes it retrievable at the moment it's needed — often called retrieval-augmented generation in technical contexts, but the underlying idea is simple: give the AI the right context at the right time.

Permissions have to travel with that data. An AI system should only be able to see and use information the person or process invoking it is actually authorized to access.

As AI touches more business data, having a clear policy about what data can be used for AI purposes, how long it's retained, and who's accountable for it becomes important — not as a formality, but as a practical safeguard.

A reasonable starting sequence for most businesses: