Unlocking AI Search Success with Adobe’s New Tool

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I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.

Brand visibility

With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.

Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.

AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.

As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”

Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.

Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.

Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.

Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.

Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.

While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.

As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”


Inspired by this post on Search Engine Land.


crushpress.ai community screenshot

FAQs

What is the focus of Adobe's new tool?

Adobe Brand Visibility focuses on boosting brand visibility in AI-driven searches. It tracks mentions, measures share of voice, and identifies content gaps across major AI platforms using data from Semrush and 300 million real-world prompts.

What data sources power the tool?

The tool leverages Semrush data, 28.5 billion keywords, 43 trillion backlinks, and 300 million real-world AI prompts. It also uses first-party data from owned channels to assess brand appearances on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity.

What can teams do with the tool?

It helps teams pinpoint which prompts lead to brand exposure or loss and offers prioritized recommendations. It also enables competitive brand analysis, benchmarking, and trend tracking.

Who commented on the shift in AI search results?

Loni Stark, Adobe’s Vice President of Strategy and Product, commented on the shift from a standard SERP to results that seem random. She noted these results can be understood when scaled with the right tools.

When was the post published?

The post was published on June 18, 2026. It was updated later the same day at 16:10:58 UTC.

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