What is Generative Engine Optimization?

The shift from traditional search to AI mediated discovery is not theoretical. It is happening now, and it is measurable. Adobe Digital Insights reported that generative AI traffic to United States retail sites increased 4,700% year over year as of July 2025. During the 2025 holiday shopping season, AI driven traffic to retail sites surged 693% compared to the prior year, and AI referred visitors converted 31% more often than visitors arriving from other sources.

Euromonitor International projects that AI powered search will influence over $595 billion in retail ecommerce by 2028. Their analysis of more than 8,700 online skincare brands in the United States found that as many as half may face declining relevance as consumer attention shifts toward AI driven discovery. McKinsey reports that half of all consumers now use AI when searching the internet, and what begins as AI mediated discovery increasingly carries through to evaluation and purchase.

As Euromonitor's Rabia Yasmeen has put it, “in AI generated answers, there is no guaranteed slot, even for market leaders.” That is the core insight behind GEO. The rules of visibility have changed, and the brands that adapt first will define the new defaults.

SEO optimizes for search engine results pages, where brands compete for link placement. GEO optimizes for AI generated responses, where there are no links, no ads, and no guaranteed positions. In traditional search, a strong brand could rely on paid media, backlinks, and keyword density to secure visibility. In AI generated responses, the model synthesizes information from across the web and decides which brands to mention based on the quality, structure, and consistency of the information it has encountered.

The distinction matters because the tactics are different. SEO rewards keyword optimization and link building. GEO rewards structured data, factual accuracy, and authoritative third party mentions. A strategy designed for one will not automatically work for the other.

Two categories of factors influence whether an AI model mentions your brand. The first is owned sources, meaning the information on your own website. This includes structured data such as schema.org markup that gives AI models machine readable facts about your brand, your products, your pricing, and your locations. It also includes the depth, accuracy, and specificity of your website copy. Thin product descriptions and generic about pages give AI models very little to work with. Rich, factual content gives them material to cite.

The second category is earned sources, meaning third party signals from across the web. Press coverage in authoritative publications, product reviews on trusted platforms, mentions in industry directories, and references on retail partner sites all contribute to how AI models perceive your brand's authority. A brand mentioned in Vogue, carried by Nordstrom, and reviewed on independent blogs will appear more frequently and more favorably than a brand with no external footprint.

The interplay between owned and earned is what makes GEO a distinct discipline. You control your website. You influence your earned presence. Both need to work together for AI models to develop a clear, accurate picture of who you are.

In traditional marketing, brands measure share of voice, the proportion of the conversation they own across advertising and media. In AI mediated commerce, the equivalent metric is Share of Model, the frequency with which a brand appears in AI generated responses for relevant queries. Measuring Share of Model requires querying AI models with the same prompts consumers use, tracking which brands appear in the responses, and comparing results across models and over time.

A brand might appear prominently in ChatGPT responses but be absent from Gemini, or the reverse. Cross model comparison reveals these gaps, and tracking the same prompts over time reveals how each model's perception of a brand evolves as the underlying training data and retrieval systems change.

When a brand discovers that AI models are missing key information or stating something inaccurately, the fix starts on the brand's own website. That can mean adding structured data, enriching product descriptions, or correcting factual errors. Once those changes are live, the next step is to rescan and observe whether the AI model's response has changed. The result is a measurable feedback loop. Fix, then rescan, then verify.

Galderma offers a recent example. In 2025 the company updated its online product descriptions and refocused its public relations strategy so that AI search would surface Cetaphil when users asked about sensitive skin. According to Euromonitor, those GEO efforts contributed to substantial online sales growth across multiple markets. Walmart and Amazon have also reportedly begun embracing GEO focused marketing strategies as the channel matures.

Brands that want to get ahead of this shift should start by understanding what AI models currently say about them. Query ChatGPT, Claude, and Gemini with the same prompts a customer would use. Read the responses. Note where the models are accurate, where they are wrong, and where the brand is absent entirely. From there, work backward. Identify what is missing or misrepresented, then trace it back to the gaps in the brand's website and earned media presence that allowed the model to draw the wrong conclusion.