The category is no longer just about extraction
Most early document-AI evaluations focused on how well a system could capture fields from invoices, forms, and semi-structured files. That still matters, but it is no longer sufficient for teams dealing with contracts, diligence materials, reports, or mixed collections. Buyers want systems that can retrieve, compare, synthesize, and preserve evidence across documents.
Evidence quality is becoming a buying criterion
The market is learning that fast outputs are not operationally useful if reviewers cannot see where they came from. Cited answers, visible context, and clear escalation paths are becoming part of the buying discussion because they reduce trust debt and shorten review loops.
The winning workflow is bigger than the model
A useful document-intelligence product has to fit around the model. Buyers increasingly ask about deployment options, review queues, audit trails, and integration boundaries. The platform decision is about how the work gets done, not just what the model can answer in a demo.