Pin where value occurs
AI value usually lands in four classes: efficiency (hours, cycle time), effectiveness (conversion, quality, risk), experience (responsiveness, consistency), and optionality (reusable capability and data assets). Declare the primary class at kickoff — avoid decorating results with unrelated metrics later.
We deliberately cite no invented client percentages. Honest measurement starts from your baseline: 4–8 weeks of process and outcome metrics before launch, plus a comparable window or control cohort.
Metric layers: north star, process, guardrails
North-star metrics connect to business outcomes (e.g., first-contact resolution, stockout rate, underwriting turnaround). Process metrics reflect system health (adoption, human takeover, latency). Guardrails catch side effects (complaints, compliance exceptions, cost runaway).
North star alone misses weak adoption; process alone becomes self-referential. All three keep the management conversation complete.
Acceptance is contract language and product language
Acceptable delivery should state scope, data premises, target metrics and measurement methods, exit criteria, and remedies if unmet. Internally and externally, that reduces “feels about right” debates.
Eval sets must align with business metrics. High offline accuracy with no frontline use means acceptance targeted the wrong object; heavy use with broken guardrails means the launch bar was too low.
A note on marketing visibility metrics
For GEO / generative visibility, metrics should center on representation coverage, citation quality, and monitoring loops — not mythical traditional SEO rankings. See the product methodology for method detail; this brief only stresses: do not let marketing metrics pose as the ledger for enterprise AI transformation.