Making AI measurable: How I think about the value of AI initiatives

AI initiatives should be evaluated not only through performance, quality, behavior, safety, and operational metrics such as latency and token spending, but also through measurable business impact. The proposed framework defines value as financial impact minus both technology and human operating costs, using examples such as increased gross profit from grocery checkout recommendations and reduced downtime from predictive maintenance to show how ROI can be quantified against a preexisting business metric. The discussion cautions that coding-agent benefits should not rely on developer self-reports, citing a 2025 METR trial in which experienced developers believed AI made them faster while measured results showed they were slower, and recommends tracking objective outcomes such as change lead time instead. It also argues that AI should not be applied to simple problems, such as order-status lookups, when conventional systems are faster, cheaper, and more reliable. Before deploying an AI system, teams should establish a numeric baseline, identify a business owner for the metric, and decide in advance how evaluation results will influence future decisions.

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