Applied AI ·
Use AI where the work can be checked
The best candidates are repeated tasks with clear inputs, useful outputs, and a manageable cost when the result is wrong.
Begin with the consequence of a wrong result. A draft, classification, or summary can be useful when a person already reviews the output and a correction is cheap. A hidden error inside money movement, access, or customer communication needs a much stronger control path.
Then look at repetition. Work that returns every day and follows a recognisable pattern may justify automation. Work that happens rarely and depends on context may be better served by a clear checklist.
The model's role should be narrow enough to explain. Approved material, exceptions, review, and fallback belong in the design before launch.
AI earns its place when the team can see what changed: less repeated work, faster review, better coverage, or fewer missed cases. If a rule can achieve the same result more reliably, use the rule.