Most operations teams are being told to "use AI" without being told where. The useful distinction is not which model you pick. It is which parts of your process tolerate a wrong answer and which parts do not.
The two halves of a process
Almost every operational workflow splits into interpretation and execution.
Interpretation is reading a supplier email, working out which PO a scanned invoice belongs to, deciding whether a complaint is a warranty claim or a delivery issue. It is judgement over messy input, and people are slow at it because it is repetitive, not because it is hard.
Execution is posting the entry, applying the discount, updating the stock figure, producing the number that goes to the auditor. It has to be exact and it has to be explainable months later.
AI is genuinely good at the first half. It is the wrong tool for the second.
What this looks like in practice
A distribution team receiving several hundred supplier documents a week does not need a model to calculate totals — a database does that correctly every time. What consumes the day is opening each PDF, finding the PO number, and typing it somewhere.
Point a model at that step and you get a draft: this looks like PO 4471, confidence high. Then the deterministic system takes over. It checks that PO 4471 exists, that the supplier matches, that the amount is within tolerance. If any check fails, a person looks at it.
The model proposes. The system disposes. That division is what makes the result trustworthy.
Where teams get into trouble
The common failure is asking a model to be the system of record. Anything that has to reconcile, total, or survive an audit needs deterministic logic and a stored trail of what happened and why.
The second failure is skipping the review path. A process that is right 95% of the time sounds excellent until you realise nobody knows which 5% was wrong. Extraction without validation moves the error from data entry to reconciliation, and reconciliation errors surface later and cost more.
A reasonable way to start
Take one process and list its steps. Mark each one as interpretation or execution. If the interpretation steps are where the hours go, there is probably something worth automating. If the hours go into execution, you need better software, not a model.
That assessment usually takes an afternoon and saves considerably more than it costs.
