The workflows where AI pays off share a common shape: high volume, repetitive structure, and a cost of error that's recoverable. Document classification, support ticket triage, data extraction from semi-structured forms — these are places where an AI system handling the bulk of cases, with a human reviewing exceptions, beats either a fully manual process or a rigid rules engine that breaks on every edge case.
Where it adds risk instead of removing it
AI adoption goes wrong most often in low-volume, high-stakes decisions — the kind where a wrong call is expensive or irreversible, and where there isn't enough historical data for a model to have learned the pattern reliably. We've turned down AI projects where the client's actual problem was a handful of complex, judgment-heavy decisions per week; automating that doesn't save meaningful time and introduces a new failure mode for no real benefit.
The assessment question that matters
The question that separates a good AI investment from a bad one isn't "can a model do this?" — modern LLMs can produce plausible output for almost anything you ask. It's "what happens when it's wrong, and how would we know?" If a workflow has no good answer to that question, it needs better guardrails before it needs AI, not after.
