AI-Search Visibility Is Moving From Experiment to Budget Line as Adobe Builds the Measurement Layer

AI traffic to U.S. retail sites surged 1,324 percent between October 2024 and May 2026. That number is no longer a research forecast. It is a measurement problem, and the marketing technology industry is beginning to build products around it.

Two developments this week put the transition into sharp relief. Adobe launched Brand Visibility, a new module inside its CX Enterprise platform that tracks and optimizes brand presence across AI search surfaces including ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. On the same day, L’Oreal announced a deeper partnership with OpenAI at VivaTech Paris, integrating its Maybelline virtual try-on application directly into ChatGPT and formalizing what has become a real advertising channel inside that platform.

Together, the two moves signal that generative engine optimization (GEO) has crossed from experimentation into something with measurement tools, vendor products, and budget lines behind it.

What Adobe Brand Visibility Does

Adobe’s product is built on the company’s May 2026 acquisition of Semrush, which brings a keyword database of 28.5 billion terms and 43 trillion backlinks into the CX Enterprise platform. Brand Visibility draws on a proprietary database of nearly 300 million real-world AI search prompts to surface where a brand appears across AI surfaces, how often, and against which competitors. Adobe describes this as the largest global dataset of its kind.

The tool measures mention frequency, audience reach, and competitive share-of-voice across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. It identifies content gaps, surfaces AI-agent-driven recommendations for closing those gaps, and tracks the impact of optimization work directly within the platform. The Semrush integration adds traditional SEO intelligence alongside the AI-surface data, giving marketing teams a unified view of search visibility across both web and AI layers.

Loni Stark, Adobe’s vice president of strategy and product, acknowledged the foundational uncertainty: “We don’t have all of the answers, but we have the best data.”

That framing matters. GEO is a discipline built around optimizing for systems that remain partly opaque. The data layer is the only current proxy for what is actually working, which is why Adobe’s bet on the largest prompt database is a meaningful differentiator rather than just a product marketing claim.

L’Oreal and OpenAI: The Commerce Side of GEO

L’Oreal’s deal with OpenAI, announced by Chief Digital and Marketing Officer Asmita Dubey at VivaTech Paris on June 19, approaches the same shift from the opposite direction. Rather than measuring AI-search visibility, L’Oreal is paying to embed its products directly into the AI interface as an advertising channel.

The partnership gives L’Oreal access to OpenAI’s latest models for its CreAItech marketing production platform, which the company launched in earnest in 2025 and built with models from Google, Adobe, and others. The system has generated 50,000 marketing assets, reduced production costs by 40 percent, and been adopted by 70,000 trained employees. The new OpenAI integration adds reasoning-model access and, most visibly, an in-ChatGPT implementation of Maybelline’s virtual try-on application.

L’Oreal has been running paid ads on ChatGPT for its CeraVe, SkinCeuticals, and Garnier brands since April 2026. The company’s advertising and promotions budget grew 10 percent in 2025 to $15.48 billion, and AI platforms are now part of that media allocation.

“I absolutely believe that it augments creativity,” Dubey said of the AI tools driving L’Oreal’s content production system.

The Measurement Gap That Both Moves Are Addressing

The underlying shift that Adobe and L’Oreal are both responding to is the same: AI-mediated search is now a volume channel, and it currently has almost no measurement infrastructure that marketing teams can act on reliably.

Web analytics platforms measure traffic from known referrers. AI interfaces like ChatGPT do not consistently pass referral data, and brands frequently discover their AI-search presence, or lack of it, only anecdotally. Adobe’s Brand Visibility is one of the first products built explicitly to close this gap at scale. HubSpot moved earlier, pricing AI search visibility tracking as a $50 monthly product line with its AEO launch, a signal that the category was forming fast enough to carry commercial pricing.

The urgency behind these products is justified by the volume data. AI traffic to U.S. travel sites grew 2,215 percent in the same 18-month window as the retail figure. These are not marginal channels growing at the edge of the marketing mix. They are becoming primary discovery surfaces for purchase intent, particularly for users who arrive at an AI-generated response rather than a list of links to click through.

What This Means for the Marketing Leader

GEO is no longer a future-state consideration. The question has shifted from whether to optimize for AI search to how to measure it and what to fund.

The category is still early, which means measurement definitions, attribution models, and competitive benchmarks are being invented in real time. Adobe’s approach leans on proprietary data at scale. HubSpot’s approach is accessible and priced as a feature add-on. L’Oreal’s approach is direct paid integration with the AI platform itself. Each reflects a different theory of how AI-search visibility will be won.

For marketing leaders evaluating GEO investments, three questions structure the decision. First, which AI surfaces matter for your category. ChatGPT and Google AI Mode have different user populations with different intent profiles. Second, whether the brand visibility problem is a measurement gap or a content gap. Adobe’s tool addresses measurement. Improving the underlying content that AI systems surface is a separate workflow requiring different resources. Third, how much of the AI-search strategy is owned (content, traditional SEO) versus paid (advertising directly on AI platforms). The two are complementary but require different teams and different budget lines.

Brands that build measurement infrastructure now will have a data advantage as the category matures. The traffic numbers suggest the window is narrower than it might appear.

Source: MarTech.org