Full Answer

Traditional attribution — last-click, view-through, data-driven — assumes a closed system where every touchpoint between ad and conversion is measurable. AI search breaks that assumption. A prospect might click your Google Ad, visit your landing page, leave, then ask ChatGPT or Google AI Mode the same question. If the AI recommends a competitor, you've paid for the lead but lost the conversion to an unmeasured channel. Your ad platform still reports the click, but the true conversion path ran through AI — and your analytics never saw it.

The problem works both ways. AI engines regularly cite content from businesses whose ads the user never encountered. When that citation leads to a purchase, your ad platform may claim it through view-through or time-window attribution — reporting a conversion your ads didn't actually drive. This inflates your reported ROAS while masking the real driver: your content's visibility inside AI engines.

Here's the thing... every attribution model in use today — from Google Ads' data-driven attribution to Meta's Conversions API — was built before AI search existed as a conversion pathway. The result is an [AI search attribution gap](https://seresa.io/seed/attribution-measurement/what-is-the-ai-search-attribution-gap) that sits between your paid and organic channels. Until you measure AI visibility as its own channel alongside paid search and paid social, your attribution data describes a partial picture — and the gap widens as AI search adoption grows.