Accuracy percentages get reported as if they're the whole story, and the actual business cost of a wrong answer depends entirely on what happens next — a fact covered from the technical evaluation angle in the piece on evaluating an AI vendor's accuracy claims, and worth returning to specifically from the cost side, because the same accuracy figure can represent wildly different financial exposure depending on the use case.
A hallucinated answer in an internal tool an employee can visually double-check against a source document costs almost nothing — the employee catches it, corrects it, and moves on, with maybe a minute lost. The identical hallucination rate in a customer-facing system giving out incorrect policy information, or an automated system taking action based on the model's output with no human review, costs considerably more — a wrong refund issued, an incorrect commitment made to a customer that the company then has to honour or walk back publicly, a compliance obligation stated incorrectly to someone who then relied on it.
Modelling this properly means multiplying the error rate by the actual cost of an individual error occurring, not treating a lower error rate as an unqualified win in isolation. A cheaper model with a slightly higher error rate, used in a context with human review and low individual error cost, can be the better economic choice over a more expensive, more accurate model where the cost difference in API spend exceeds what the accuracy improvement is actually worth for that specific use case.
The most valuable design lever, worth more than chasing marginal accuracy improvements on the model itself, is often reducing the cost of an individual error rather than reducing how often errors occur — routing low-confidence outputs to human review before they reach a customer, designing the interface so a wrong answer is easy for someone to catch and correct rather than presented with unearned authority, and building an easy correction and escalation path so a mistake that does reach someone doesn't have to be expensive to fix. A system honest about its own uncertainty, that fails safely, is frequently worth more than one with a marginally higher headline accuracy figure that fails confidently.
