Scope the deployment against targets you can audit in your own stack: time-to-campaign for localized GTM motions collapsing from weeks to days; pipeline conversion in non-English markets closing the gap against your English baseline as messaging becomes buyer-intent-aligned rather than generic; the localization hours coming off marketing ops' week; and ad spend concentrating on language-persona combinations your own conversion data proves out. NRR is the long-game target - customer success delivering region-specific onboarding at scale is what reduces churn in high-value international segments. Baseline each metric before go-live; every one of them lives in dashboards you already run.
ROI compounds over 12 months as the AI model matures: more localized campaigns per quarter at flat operational cost, with each campaign's conversion improving as the model learns which language-persona combinations drive the highest LTV. The staffing math runs as a stated assumption: if localization grunt work currently absorbs one or two roles' worth of marketing ops capacity, that capacity shifts to strategy - and the localization hires international growth would otherwise force never get posted. Sales reps stop doing manual asset customization because language-matched assets load into Salesforce automatically. The compounding effect: as your product roadmap evolves, new features localize and deploy in parallel with engineering releases, closing the GTM lag that delays international revenue capture. The free AI Opportunity Assessment sizes a directional version of the dollar case from your intake answers and a scan of your public site - the actual campaign-volume and pipeline-data model gets built with your team once you're in scoping.