Why does the structured versus unstructured split matter for AI?
Because the techniques divide along it. Forecasting, scoring and classic analytics want structured inputs; most of what a company actually knows sits unstructured, in contracts, tickets, emails and calls, where estimates put the large majority of enterprise data. The business value of language models is largely that they read the unstructured majority, which previously needed a person. Document AI earns its keep by moving information across the line: PDF in, typed fields out.
How do LLMs change what counts as usable data?
They make the conversion cheap in both directions. A model can turn a messy email thread into a structured summary with named fields, and turn a database row into a readable answer. The catch is reliability: a model’s extraction is a prediction, not a lookup, so production pipelines validate extracted fields against rules before anything downstream trusts them. That is also why data contracts and a semantic layer matter more with AI, not less: the structure you define is what keeps model output checkable.
What does structured data mean in SEO?
The same phrase, a different field. On a website, structured data means machine-readable annotations, JSON-LD markup using schema.org vocabulary, that tell search engines what a page contains: a product, a review, an FAQ. It matters to AI search too, since engines assembling answers lean on explicit structure. The two meanings share the idea, information shaped for machines, and nothing else; this page’s main subject is the data meaning.
What should a company do with the split?
Map it before buying anything. List the decisions you want AI to support, then ask whether the information behind each sits in tables or in documents, because the answer picks the technique and the vendor. Structured problems have mature, cheap tooling; unstructured ones need language models plus the validation around them, often feeding a knowledge graph or search index. The highest-ROI projects usually sit on the boundary: structured records that gain one or two fields currently trapped in documents.