Why AI Teams Need a Metrics Baseline Before Scaling Any Feature

Software teams building AI features often struggle to evaluate whether those features are actually working once usage scales up. A metrics baseline provides a small set of before-and-after measurements to determine if an AI workflow is improving, degrading, or simply becoming more costly. Unlike generic software tracking, AI features require additional signals because model outputs are probabilistic and can be fluent yet wrong, correct but incomplete, or useful but prohibitively expensive. Key baseline categories include cost per successful task, output quality, latency, user adoption, and real-world task improvement. Experts recommend starting with just one or two metrics per category tailored to the feature's specific risk and purpose, rather than building sprawling dashboards that obscure decision-making.