Matt Gilbert, CEO of Partnerize, argues that the current industry focus on AI visibility mirrors the early days of Search Engine Optimization (SEO). Writing on the Partnerize blog, Gilbert suggests that while marketers currently prioritize where brands appear in AI-generated answers, the real value lies in connecting that visibility to commercial outcomes.
Search History as a Roadmap for AI
The evolution of SEO provides a roadmap for the future of AI in marketing. In the early 2000s, search marketing metrics were limited to rankings and keyword positions. Businesses focused on appearing at the top of the Search Engine Results Page (SERP) without understanding the financial return of those positions.
A major shift occurred around 2010. During this period, enterprise SEO platforms like BrightEdge integrated search data with web analytics tools such as Omniture and Google Analytics. This integration allowed marketers to move beyond rankings to measure the entire funnel.
The question was no longer simply: Where do we rank? It was increasingly: What happened because we were there?
This progression transformed SEO from a technical tactic into an investment discipline. By linking visibility to traffic, and traffic to revenue, brands treated organic search as a predictable channel for capital allocation.
Measurement Gaps in the AI Interface
The mechanism of measurement separates the SEO era from the emerging AI era. For two decades, "the click" bridged search intent and brand conversion. When a user clicked a link, they entered a tracked environment where brands analyzed behavior.
AI interfaces, such as Large Language Models (LLMs) and generative search summaries, provide answers directly within the interface. This reduces click frequency, creating a "zero-click" environment that challenges traditional attribution models. Marketers must now bridge this gap, repeating the process used when SEO and analytics first merged.
Moving Beyond Visibility
The industry currently occupies the "visibility phase" of AI. Brands focus on whether their products appear in AI agent recommendations or generative summaries. However, the next phase requires new measurement tools to prove commercial value.
The transition from visibility to a measurement discipline remains inevitable. Gilbert argues that the brands that invested early in search rankings capitalized most effectively when measurement tools matured. The same principle applies to AI today: finding the brand is the first step toward profitability in an AI-driven ecosystem.
Five Stages of Marketing Progression
Gilbert outlines a specific trajectory for digital marketing disciplines:
- Visibility: Where does the brand appear?
- Traffic/Engagement: How does the user interact?
- Conversion: Does the interaction lead to an action?
- Revenue: What is the financial return?
- Capital Allocation: Where should the next dollar go based on performance?
As AI reshapes consumer behavior, the performance marketing industry must look past the novelty of AI presence. Success depends on the data infrastructure required to measure the subsequent consumer actions.
Source: Partnerize Blog
This article was created with AI assistance and editorially reviewed.
