Over the past decade, brands have invested heavily in SEO, content strategy, and personalization to drive visibility and engagement across digital channels. But the landscape is rapidly shifting. Today, the customer journey increasingly begins not with a traditional search engine, but with an AI assistant. As generative AI platforms like ChatGPT, Claude, and Perplexity become go-to tools for information and decision-making, large language models (LLMs) are emerging as the new front door to brand discovery.

These systems don’t just index content — they summarize it, interpret it, and recommend it. That means they’re shaping brand perception long before a user visits a website. Yet many brands remain unaware that their content is already being surfaced — or overlooked — by these AI tools. Bain & Company reports that 80% of consumers rely on AI-written summaries for at least 40% of their searches. And Gartner projects that by 2028, brands could see a 50% or greater decline in organic traffic as consumers move away from traditional search.

This shift to AI-powered discovery presents an exciting new opportunity for brands to influence how they’re found, cited, and trusted by customers in the moments that matter most. With that in mind, we’re introducing Adobe LLM Optimizer to help brands capitalize on this transformation.

A new discipline for a new channel: Generative engine optimization.

Generative engine optimization (GEO) centers on understanding how LLMs interpret brand content and making strategic improvements to increase how often and accurately your brand appears in AI-generated responses.

With LLM Optimizer, marketers can begin to answer critical questions:

• Is my content showing up in generative AI tools?
• Are my competitors being cited where I’m not?
• What can I do to improve my brand’s visibility across AI-driven queries?

LLM Optimizer provides visibility into this new discovery layer and equips organizations to take action.

Identify, benchmark, and act with full visibility into AI-driven traffic.

Traditional analytics platforms were built for human visitors, not for intelligent agents. As a result, brands have limited visibility into how generative AI tools interact with their websites.  LLM Optimizer changes that by providing real-time insight into agentic traffic — activity from AI crawlers, LLM-based assistants, and AI-powered browsers that access and reference site content.

These agentic interactions include retrievals for summarization, citations in AI-generated answers, and content requests used during conversational sessions. LLM Optimizer not only detects and quantifies this traffic, but also reveals which specific pages, assets, or product details are most frequently accessed by these systems. This allows marketing and content teams to understand what’s informing AI-generated brand narratives — and what’s being overlooked.

Beyond detection, LLM Optimizer enables brands to benchmark their visibility across generative platforms. Teams can analyze how often their brand is mentioned or cited in response to key queries and compare that performance against competitors or industry leaders. These benchmarks are organized by high-value themes, product categories, and search intents offering a granular view into where your content is gaining traction and where there are gaps.

Turn insights into impact with prescriptive recommendations and one-click deployment.

Understanding where and how your brand appears in generative AI tools is essential, but visibility alone doesn’t tell the full story. To measure real impact, brands need to connect AI-driven discovery to tangible business outcomes. LLM Optimizer includes an attribution layer that makes this possible, linking generative engine visibility to key user behaviors. By mapping these journeys, marketing teams can see how often LLM exposure translates into qualified traffic, how users engage with AI-referred landing pages, and what percentage of those users convert.

The engine continuously analyzes both on-domain content, such as product pages, landing experiences, and support documentation, and off-domain sources like Wikipedia, Reddit, and other publicly indexed forums that LLMs often use to shape brand understanding. These off-domain properties frequently influence how your brand is described in AI-generated answers, even if you don’t control them directly. For example, it might identify inaccuracies or gaps in a Wikipedia entry that are leading to misrepresentation, or suggest engagement strategies to influence authoritative third-party sources.

With LLM Optimizer’s integration with Adobe Experience Manager Sites, teams can move directly from insight to action. Recommended changes can be reviewed, approved, and implemented with a single click, dramatically reducing time to execution and eliminating the need for handoffs to technical teams. This means content strategists and marketers stay in control, while still benefiting from AI-guided optimization at enterprise scale.

And by supporting enterprise standards like Agent-to-Agent (A2A) and Model Context Protocol (MCP), LLM Optimizer is designed to scale with large organizations. It ensures content is optimized for AI consumption from the moment it's published and provides governance tools to maintain brand consistency across generative platforms.

Your brand’s next frontier.

Adobe LLM Optimizer gives marketing teams a proactive way to engage with this evolution to AI-powered search and discovery — empowering them to enhance visibility, grow influence, and stay ahead in a discovery landscape defined by AI.

Learn more about Adobe LLM Optimizer and how to ensure your brand’s visibility in the era of AI search.

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