AI traffic to U.S. retail sites surged 1,324% between October 2024 and May 2026. That's not a typo. And until this month, most enterprise marketing teams had zero instrumentation on whether their brand showed up in any of those AI-generated answers.
Adobe Brand Visibility, launched June 17, 2026 as part of Adobe CX Enterprise, is the company's first GEO product since acquiring Semrush in May. It combines Adobe LLM Optimizer with Semrush's AI Optimization tool, and it's built to answer a question that's been nagging every ops team running a content program: which AI prompts are we winning, and which ones are we losing?
What the product actually does
Brand Visibility pulls from nearly 300 million real-world AI search prompts (Adobe claims it's the largest global corpus of its kind) and maps your brand's presence across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity AI. The metrics it surfaces: mention frequency, audience reach, competitive share of voice, content gaps, and historical comparisons against competitors.
The Semrush side brings 28.5 billion keywords and 43 trillion backlinks collected over 17 years. Adobe's pitch is that this dataset bridges traditional SEO authority signals with AI citation behavior, so teams can figure out where existing search equity should be translating into LLM mentions but isn't.
Loni Stark, VP of strategy and product at Adobe, told MarTech: "We used to get back the same thing (a SERP page with links on it). Now, the answers appear to be random, but they aren't at scale. But companies don't have tools to do it." The product also includes AI agents that surface prioritized recommendations and measure impact after content updates ship. Coverage spans 10 LLM families, 25+ languages, and state/city-level breakdowns.
The ops angle: new KPIs, same measurement discipline
For marketing ops, this creates a new reporting surface. Prompt-level win/loss data, competitive SOV in AI answers, content-gap identification. These are genuinely new operational KPIs that didn't exist six months ago. But here's where the discipline matters.
Adobe positions Brand Visibility to connect AI mentions to business outcomes via integration with Adobe Experience Manager and Adobe analytics workflows. That integration is the difference between a vanity dashboard and something that feeds pipeline reporting. If your team is evaluating this, the first question isn't "what's our AI SOV?" The first question is: can we map a change in AI mention frequency to a measurable change in qualified traffic or pipeline creation?
Don't treat AI SOV the way early social media teams treated follower counts. Measurement skepticism is warranted until you can run a controlled experiment: improve visibility on a set of prompts, hold others constant, and measure downstream conversion lift. Directional, not definitive, until you've done that work.
The vendor lock-in trade-off
Everest Group flagged this directly: GEO and agentic search optimization are becoming "mission-critical for marketers," but tighter integration with Adobe's ecosystem increases dependence on it. That's a real trade-off for ops teams that have spent years building portable, multi-vendor stacks.
Practitioner commentary on LinkedIn echoes the concern. Semrush could get more expensive and less useful for teams not already standardized on Adobe. If you're running a mid-market stack with standalone SEO tooling, the pricing and integration story deserves scrutiny before you commit. Build a portability checklist: data export capabilities, reporting continuity if you switch vendors, and whether competitive SOV data is locked inside Adobe's walled garden or exportable to your BI layer.
Constellation Research takes a more positive view, noting the Semrush deal adds search, LLM, and brand-intelligence capabilities that align with Adobe's broader AI marketing direction. Both perspectives are valid. The right answer depends on your stack and your switching costs.
What to do with this right now
If you're on Adobe CX Enterprise already, get Brand Visibility into a sandbox and run a 30-day baseline on your top 50 product/category prompts. Document current SOV, mention frequency, and content gaps. That baseline is the only way to measure whether any GEO work you do later actually moved anything.
If you're not on Adobe's stack, the product announcement still matters. It validates that prompt-level analytics and AI citation monitoring are becoming a real ops function, not a novelty. Start building your own tracking, even if it's manual audits of how ChatGPT and Perplexity answer your top 20 buying-intent queries. Capture screenshots, log citation sources, note competitors mentioned. Ugly and manual beats no data.
The hypothesis worth testing: if you improve structured content and entity markup on pages where you already rank well in traditional search, AI citation rates for related prompts will increase within 60 days. Measure citation frequency (manual or via Brand Visibility), and track whether cited pages show any lift in direct traffic or form fills.
Success = measurable SOV gain on target prompts. Guardrails = no decline in organic search traffic to the same pages. Stop-loss = if citation rates don't move after 60 days of content changes, the lever is somewhere else.
Stark's closing line to MarTech was honest: "We don't have all of the answers, but we have the best data." For ops teams, that's actually the right framing. GEO is early. The data is thin. But 1,324% traffic growth means the channel exists whether you're measuring it or not.
