AI is a tool, not a default. Skipping it is often the right product decision—especially when the work is already deterministic, the downside of a wrong answer is high, the data is not ready, or the volume is too small to justify the machinery.
Rules already suffice
If the decision can be expressed as clear conditions—thresholds, eligibility rules, routing tables, schema validation—code it. A model adds latency, cost, and non-determinism without improving the outcome.
Use AI at the edges of a rule system: classifying ambiguous free text, drafting an explanation, or suggesting which rule might apply. Do not replace the rule itself.
Wrong answers are expensive without review
Refunds, access changes, legal language, medical or safety guidance, and irreversible writes need a person in the path—or a hard block. If you cannot afford a wrong answer and you cannot afford review latency, do not automate with a generative model.
Draft-and-approve is fine. Silent send is not.
The data is not ready
Retrieval over stale docs, missing permissions, unlabeled examples, or contradictory sources will produce confident nonsense. Fix ownership, freshness, and access before you train or prompt.
A thin evaluation set of real tasks will tell you this faster than another model comparison.
Volume is tiny
If a person handles ten cases a week and each takes five minutes, the integration and monitoring cost of an AI workflow will dominate. Spreadsheets, macros, and templates win.
Revisit when volume, repetition, or after-hours load make the overhead pay for itself.
A short checklist
Ask: Is the answer already decidable by rules? What happens if the model is wrong and nobody reviews it? Can we retrieve current, authorized context? Is the volume high enough to amortize build and ops?
If two or more answers push against AI, ship the simpler path and keep the option open.