The easiest AI system to demo is the one where the model can answer in prose. The hardest AI system to operate is the one where that prose has to become a CRM field, a Slack alert, a meeting packet, or a queue decision.
That is where I reach for a strict schema. Not because schemas make the model smarter. They make the failure visible. A missing citation becomes a validation error. A low-confidence claim becomes a flagged field. A disagreement between two sources becomes an object the user can inspect instead of a quiet guess.
What the schema buys
The schema gives the system a contract with the rest of the workflow. It says what can be written, what cannot be written, what must be cited, and what should stop the run. That is boring. It is also what lets the output move from a chat window into HubSpot, Slack, Linear, or an internal app without asking a human to clean it up every time.
In Enterprise Researcher v2.0, every useful claim needs to carry enough structure to survive rendering, routing, and review. If the model produces an account note without the right evidence, the pipeline should not deliver a weaker answer. It should fail the run and record why.
What the schema costs
You lose speed in the first week. You write more validation code. You spend time naming fields that would be easy to leave as paragraphs. You have arguments with yourself about whether something is optional, nullable, unknown, or not applicable.
You also lose a certain kind of impressive demo. The system will sometimes refuse to answer. It will say that two sources disagree. It will return less text than the model could have produced. For production work, that is usually an improvement.
The practical rule
If a model answer will be read once by a person, prose may be enough. If a model answer will be stored, routed, reported, searched, or used by another system, I want a schema between the model and the workflow.
The schema is not the product. It is the discipline that keeps the product from lying quietly.