What should actually be an AI workflow?

LLMs are useful when the work is fuzzy. They are less useful when the answer should be deterministic.

Good candidates

Tasks such as extraction, classification, summarization, rewriting, and research synthesis often benefit from a model because the input is messy and the expected output can still be checked.

Weak candidates

If a task is a lookup, calculation, validation rule, state transition, or exact transformation, normal code is usually faster, cheaper, and easier to test.

Keep the boundary clear

A useful pattern is:

1. Gather input
2. Let the model handle the ambiguous step
3. Validate the output
4. Let conventional software execute the action

This keeps the model in the part of the system where it has an advantage.