Where AI workflow automation fits, and where it does not
Traditional automation breaks when inputs vary. A rule can move a file, but it cannot read a supplier invoice in an unfamiliar layout or tell a complaint from a quote request. Language models close that gap. They read unstructured text, classify it, and return structured output that matches a schema you define. That makes them a strong fit for the reading and sorting steps that used to require a person, even when the surrounding process is simple.
They are not a fit for everything. Calculations, approvals based on fixed thresholds and updates to financial records belong in ordinary code, where behavior is exact and repeatable. Our designs keep the model's role narrow. It extracts and classifies. Validation rules check the result against your data, such as whether a purchase order exists and totals add up. Only then does the workflow write to a system of record, and it logs what it wrote.
- Schema-validated extraction, not free text
- Business rules enforced in code
- Confidence checks before any write
- Idempotent updates that are safe to retry
- Full log of every automated change