Defining AI Product Success Beyond Technical Metrics
This analysis explores the strategic shift from technical AI evaluations to customer-centric value metrics. It argues that product teams must define 'what good looks like' through contextualized outcomes rather than generic performance indicators. The discussion highlights the parallel between AI instrumentation and historical analytics evolution, emphasizing the need for explicit success criteria to avoid automating low-value processes.
