Why it matters for your business
Most business automations need data that another system can accept, such as a CRM record, an accounting entry or a ticket field. Free-form text has to be parsed and often breaks on edge cases. Requesting structured output, then validating it in code, makes AI steps predictable enough to sit inside a workflow. A schema also documents exactly what the AI step is responsible for, which makes testing easier. Validation still matters, because a response can match the schema and still contain a wrong value.
Example
A freight broker extracts data from bills of lading. The workflow sends each scanned document to a model with a schema requiring shipper, consignee, BOL number, piece count, weight and line items. Responses that fail validation, such as a weight that is not a number, go to a person instead of being written to the transportation management system.