AI can summarize one contract beautifully but choke on "find every contract missing a termination clause." The reason isn't model intelligence — it's the missing layer between the documents and the question.
AI summarizes a contract in seconds. But ask it to "find every contract in your library that's missing a termination clause" and it falls apart. The model isn't broken — the system around it is missing the one critical layer that makes the question answerable at scale.
This is the gap most teams hit after their first wave of AI demos. The demos used a small, hand-picked file. Production hits a library with hundreds or thousands of documents — and the same prompt produces missed files, slow runs, and silent failures.
When a contract lands in the library, an AI extraction skill reads the text and pulls structured fields out of it — risk level, document type, contract quarter, energy category, continent, complexity. Those values get stamped onto the document as columns. The extraction runs once. The values stay.
The skill that does the extraction is itself worth tuning carefully — you spend time on the extraction prompt because it runs against every new document and locks in the answer. Get it right once, then every downstream query benefits.
Once extracted, the metadata appears as ordinary SharePoint columns — Risk Level, Document Type, Contract Quarter, Continent, Complexity. They look like any column you'd add by hand, but they're populated by AI and they're governed.
Because they're structured fields, they work with every filter, sort, view, and query SharePoint already supports. Copilot can use them. Power Automate can use them. The list view can use them. The AI extraction is just the producer; consumers are everything else in M365.
Search is great for "find documents about X" — fuzzy, conceptual lookup. But there's an entire category of questions search cannot answer: ones that require knowing the absence of something, or counting across the whole library, or filtering by a derived attribute that isn't a literal phrase in the text.
Without metadata, the only path is reading every file. At scale, that's slow, expensive, and error-prone — and the AI will often give up before it finishes. With metadata, the same question becomes a one-second checkbox filter against a structured column.