Can AI calculate landed cost correctly?

Landed cost is a useful AI test because the answer depends on structure and arithmetic. The model has to notice which quote is incomplete, add the right freight and handling layers, apply the Incoterms scope correctly, and keep the currency assumptions straight.

In the July 2026 PAIR-20 run, the structure mostly worked. All four platforms rebuilt the landed-cost stack under the structured brief. None treated the headline TranStar freight quote as the final answer. The problem was the number: none matched the EUR7,940 case answer. ChatGPT and Gemini were each EUR112 low, Claude was EUR87 low, and Copilot received a partial verdict because its table and prose contradicted each other.

So the honest answer is: AI can help calculate landed cost, but the calculation still needs review.

The stack is the useful output. If AI lists bunker adjustment, terminal handling, customs, insurance, and Incoterms scope in a clear table, it has made the work easier to audit. That is real value, especially when the source quotes arrive in different formats.

The final total is not proof. In this run, the platforms agreed that the headline quote was incomplete and still disagreed with the case answer. A clean total can hide a missing layer, a double-counted fee, or a currency assumption that only appears in one row.

Use disagreement as the review queue. If two passes produce different landed-cost totals, do not average them. Trace the line items that changed. In a working procurement team, the check is not "did the AI produce a number?" It is "can we see every assumption that made this number?" Until that answer is yes, the landed-cost result should stay in draft.