Is AI the New Storefront? Understanding AI Citation Patterns and Commerce Visibility

Large Language Models (e.g., ChatGPT, Claude, Gemini) are emerging as a new discovery channel for e-commerce. Findings indicate that brand visibility is no longer determined by paid search and PDP optimization alone, but by a brand's ability to structure its data and experiences for AI consumption.

Key findings demonstrate:

1. Off-site content (3rd party sites, e.g., Reddit or Forbes) overwhelmingly dominate AI recommendations
2. Citation patterns directly correlate with user intent
3. Brand authority is derived from a holistic digital ecosystem, not isolated assets.

In this new world, brands that don't build a dedicated infrastructure for AI-native commerce risk becoming invisible. This shift is also paving the way for 'agentic commerce,' a future where AI assistants make purchases for consumers. To win, brands need to build the right infrastructure now to become the go-to choice for AI-driven shopping.

"AI Search is especially important for retailers because the job is no longer just optimization — it’s showing up where discovery is happening, then converting that attention through connected experiences.” –– Rishabh Jain

Reframing the Narrative: From Data to Strategy

The landscape of e-commerce is changing. The established model of search engine results pages, product detail pages, and targeted advertising is being disrupted by artificial intelligence.

AI is rapidly evolving from a supplementary channel into the main way people discover and shop for products, presenting a critical challenge and a generational opportunity for brands.

AI is quickly becoming a trusted entry point for consumers to discover, evaluate, and compare brands and their products. In this new era, market leaders won’t be defined by how much they spend on advertising or how often they tweak their websites. They will be defined by the intelligent commerce infrastructure they own – the systems that help transform AI visibility into personalized, shoppable experiences.

Under the Hood: How We Track AI's Shopping Habits

To truly understand how AI models are discovering and recommending brands, we had to go beyond simple observation. FERMÀT built a system to actively monitor the AI ecosystem, giving us a dynamic, real-time view of how AI search is becoming a new channel for customer acquisition.

Our monitoring approach involves:

1. Systematic Prompt Tracking: We identify and track high-intent commercial prompts across various product categories by using product data, metadata, ad account signals, pixel data, and keywords. We identify and track high-intent.
2. Large Language Model (LLM) Cadence Analysis: We then run these prompts through leading LLMs like OpenAI, Anthropic, and Google on a regular basis to see what they recommend.
3. Pattern Measurement: We measure and analyze everything, visibility, recommendation patterns, and citation behavior, to spot trends and understand what drives brand presence over time.

The key takeaway is simple: we treat AI search as a measurable system. By quantifying the factors that drive brand presence, FERMÀT moves beyond guesswork to define what it truly takes for brands to show up and compete in the age of AI.

What We Learned: How AI Really Discovers Brands

Recent research by Omniscient Digital analyzed how LLMs source brand information by running prompts through multiple AI engines and tracking citation patterns. Their findings reveal three principles that have critical implications for commerce brands. Here's what every brand leader needs to know.

These findings collectively indicate that brands can no longer treat their website as the canonical source of truth. They must actively manage their presence across the entire digital landscape that feeds these AI models.

From Insight to Action: The Rise of Agentic Commerce

The data points to an inevitable end state: a world of zero-click and agentic commerce. In this new world, the "customer" is a hybrid of a human and an AI agent acting on their behalf, armed with the user's preferences and context, and tons of data. The primary strategic objective for brands is to become the trusted, default choice for these AI agents.

But how do you earn that trust? It's not about a single campaign or a better-optimized product page. It requires a purpose-built infrastructure layer that can:

• Understand the Entire Customer Journey: You need a "commerce brain" that connects the dots between a shopper's initial AI question and their final purchase, synthesizing data from SEO, on-site behavior, and purchase history to truly understand intent.
• Speak the Language of AI: Monitor the questions being asked in your category, understand which sources AI engines trust, and generate content formatted for AI consumption.
• Deliver on the Promise: When a shopper clicks through from an AI answer, don't send them to a generic homepage. Deliver a personalized landing page that continues the conversation the AI started.

Consider a practical scenario: a user asks ChatGPT for a recommendation, and your product appears in a sponsored slot (given OpenAi is beginning to test ads). The experience that follows that click is a critical variable. An AI-native infrastructure can ensure the user lands on a page that is perfectly tailored to the context of their original query, dramatically increasing the likelihood of conversion.

So, What Should You Do About It?

The reality is that most brands are still optimizing for yesterday's tactics: better PDPs, more paid search, incremental SEO tweaks. Meanwhile, the landscape is shifting to AI-driven discovery, and the window to get ahead is closing fast. Here’s where to start:

1. Current AI Visibility: Where your brand appears in AI-generated answers today
2. High-Value Prompts: Which specific user prompts are preceding purchases in your category
3. The AI-Native Funnel: A tangible model of what a FERMAT-powered, AI-native experience could look like for your brand in a zero-click, agentic world.

The decisions you make in the next 6 months will determine whether AI agents default to your brand or your competitors when they shop on behalf of customers. This isn't about a single campaign or a one-off optimization. It's about building infrastructure that can adapt, learn, and scale as AI commerce evolves. The time to build your AI-native future is now.