AI feels like the obvious answer when you need to automate judgment: scoring, classifying, and reading messy inputs. And to be fair, it often is the right solution: general-purpose models are flexible, quick to prototype, and handle input no RegEx survives. However, they can add cost, latency, and drift where simple, deterministic tools would do. Skip the comparison and you're pay a cost you never notice you're paying.

In this talk, we show how to run that comparison before you start building. We'll walk through a 2022 production system (scoring assignments against a rubric), where that era's models underdelivered, and why RegEx, static checks, and tiny heuristics carried the results. Items humans disagreed on couldn't be evaluated by any model. You get a prioritization formula for where automation pays off, per-item measurement that makes failures visible, and drift monitoring that catches decay. New for 2027: we run cost math against today's pricing and show what structured output changes on sample scoring problems. The simple route's upside is determinism and near-zero cost, with a hard ceiling on messy input. That messy input is where hybrid approaches pay off: rules handle the easy wins, AI takes the ambiguity. For engineers who want to pick the right tool; no AI fluency required.