Ranking within Google's traditional search results provides diminishing returns. Ads, AI Overviews, and other search engine results page (SERP) features push organic links further down the page. As the search landscape changes, how should brands adapt to ensure they're represented in AI-powered responses?
The more you know about how AI engines use your brand's information and when they cite it, the better you can use AI search to your advantage. With that knowledge, you can move beyond whether AI models know your brand and start developing your own AI visibility strategy.
Collapse of the click economy
It's important for most brands to understand AI search and begin developing an AI SEO strategy as quickly as possible. While a full transformation from organic to AI search appears to be years away, AI SEO may eventually replace traditional SEO. Google is already leaning heavily on AI search. As company leadership has noted, search experienced strong growth driven by AI experiences, with queries at an all-time high and significant revenue growth.
At the same time, users are adapting to AI search features. When users encounter an AI-powered summary in search results, they click a blue link just 8% of the time, according to a Pew Research study. When they don't encounter AI summaries, they click blue links 15% of the time. This shift indicates that AI summaries are reducing click-through rates to traditional organic results, making it crucial for brands to be referenced within the AI summary itself.
Although AI search traffic is still limited, it tends to have a higher conversion rate than organic search traffic. One study found AI traffic had a conversion rate of 11.4%, compared to 5.3% for organic search traffic. This suggests that users arriving via AI recommendations are more intent-driven and ready to engage, making AI visibility a high-value goal.
The collapse of the click economy means that even top rankings may not deliver the same traffic as before. Brands must adapt by ensuring their content is used as a source by AI models, whether or not it is explicitly cited. The goal is to be part of the knowledge base that AI engines rely on to generate responses.
Brand presence within AI engines: Usage vs. citation
Brands can exist in AI systems in two distinct ways: usage and citation. AI engines ingest information about your brand and use it when responding to search queries. This is somewhat similar to how Google traditionally indexes pages before ranking and serving them in search results. Usage means the AI model has learned from your content and may incorporate that knowledge into its answers, but it does not necessarily point back to your site.
When AI engines use your content, they may also mention your brand as an unlinked citation. This can drive discovery and may prompt users to search for and engage with your brand. Citation occurs when an AI engine directly references your brand as a source of information. This may be a link to your web page, a link to your social profile, or a clickable phone link that lets users call you. Citation provides direct attribution and a potential traffic path, while usage builds brand awareness without a direct link.
Within specific AI platforms, usage and citation rely on separate technical levers. For example, some AI systems deploy distinct user agents for crawling and for generating responses. One agent may be used to gather information for training the model, while another handles real-time searches. Other AI systems have similar controls and measures that point to the same distinction. Understanding this separation helps brands decide whether to focus on making content crawlable for training or on optimizing for citation-worthy snippets.
The difference also affects how brands measure their presence. Usage is harder to track because it often happens behind the scenes. Citation is more visible because it appears in the AI's output. Both are important, but they require different strategies. Usage might leverage structured data, high-quality authority signals, and broad topical coverage. Citation might require original research, unique data, and clear attribution schema.
Why citations are only part of the AI visibility equation
AI engines often answer questions directly without necessarily citing web sources. This isn't a new phenomenon. Before AI Overviews, Google tried something similar with featured snippets. In many cases, the AI synthesizes information from multiple sources and presents it as a single answer, without listing every source.
Research indicates that AI models retrieve roughly equal numbers of cited and uncited URLs to generate an average response. Yet one platform accounts for a significant majority of uncited URLs. This demonstrates that many AI systems are biased in the uncited information they provide to users. Certain platforms and websites are better than others at helping brands appear in AI answers. Brands that try to force themselves into AI models without understanding where those models source most of their information will be at a distinct disadvantage.
For example, community-driven platforms often dominate uncited references because they provide conversational data that AI models find useful for generating natural language responses. Brands that want to be used uncited need to have a presence on those platforms or produce content that mimics the conversational style that AI models prefer. Meanwhile, citation-focused strategies should target authoritative, well-structured sources that AI models trust enough to link back to.
This bias also means that some industries or niches may find it easier to get cited than others. Tech, health, and news often receive more citations because they produce content that fits AI citation patterns. Brands in less covered areas may need to work harder to generate original content that AI models find worth citing.
How to improve AI usage and citation for your brand
Start by tracking your brand's status and progress over time. Run a representative selection of prompts through an AI visibility platform and examine the citation sources. Where do they land, and what does that tell you? There are many emerging AI citation tracking platforms to choose from. Established analytics platforms have also integrated AI tracking features.
Scale your tracking and research efforts as much as possible. This can be difficult because AI prompt tracking often relies on API calls and is more expensive than traditional search ranking tracking. As long as your sample is broadly representative, most tracking platforms will pull multiple responses and calculate some type of average. Although the volume of data is smaller, it's usually quite rich. You can gain insights into which topics your brand is used for, which competitors appear, and which types of content are most likely to be cited.
Don't forget to read AI and data vendor studies. They're valuable sources of information because they show where AI engines pull information from. These studies often reveal patterns about which domains, content formats, and topics are most prevalent in AI training data. Continual monitoring and adaptation are key. Over time, you can place your brand within the sources AI engines rely on most heavily.
There are also technical steps you can take. Ensure your website is crawlable by AI agents by reviewing your robots.txt and allowing relevant bots. Use clear, semantic HTML and structured data to help AI models understand your content's context. Publish original data, case studies, and in-depth analysis that cannot be easily summarized by scraping other sites. Build authority through high-quality backlinks and mentions from trusted sources, as AI models often weigh authority heavily.
Should you bother with traditional search rankings?
Yes, you should continue to pursue traditional search rankings, but not for the reasons you might think. The connection between organic ranking positions and performance has become much more nebulous. However, research suggests a correlation between AI citations and Google ranking positions, at least for Google AI Overviews. One study found that over three-quarters of pages cited in AI Overviews ranked in Google's top 10 organic search results. For AI Overviews, which may become a dominant force in AI search over the coming years, traditional rankings still seem to matter.
Additionally, AI engines rarely cite generic content that restates what other sources already say. Content that earns citations adds unique value. This aligns with Google's helpful content guidance, which encourages brands to publish original information. Producing content with a unique, trusted, and statistically grounded perspective can also help improve Google rankings. Since many tactics for earning higher organic rankings can also earn AI citations, there's no reason to abandon traditional SEO techniques and content strategies.
Traditional SEO also supports the broader digital ecosystem. Rankings contribute to brand recognition, even if they don't generate clicks. They also feed into the data that AI models use to evaluate authority. A site that ranks well is more likely to be considered trustworthy and thus more likely to be used as a source. Furthermore, traditional search remains a major traffic driver for many industries, especially those where users actively seek out links, such as research, shopping, and professional services.
However, brands should not rely solely on traditional rankings. They need to build a presence that works even when users don't click. That means being the answer, not just the link. This shift requires a change in mindset from top-of-funnel traffic to top-of-mind awareness. Brands that succeed in AI search will be those that become synonymous with expertise in their field.
The growth of AI visibility and the fate of traditional SEO
Both usage and citation require continual tracking and analysis. To increase the likelihood that AI engines use your brand's knowledge and content, get your brand into the sources each AI model relies on. To earn citations, stay crawlable, rank organically, and say something original. Classic SEO still earns its keep because the techniques that win organic rankings often earn AI citations as well. Yet the returns are diminishing, and AI SEO may one day replace traditional SEO altogether. That's still a long way off, so for now, keep ranking, start tracking, and pursue both.
Brands must also consider the ethical and practical implications of AI usage. As AI models become more sophisticated, they may begin to synthesize information from multiple sources in ways that obscure original creation. Brands need to protect their intellectual property while also ensuring they are part of the training data. This balance is delicate, but it is essential for long-term visibility.
Finally, the landscape will continue to evolve. New AI models, new search interfaces, and new regulations will all shape how brands appear in AI-driven environments. Those who invest in understanding the difference between usage and citation today will be better prepared for the search ecosystem of tomorrow. The key is to remain agile, test continuously, and prioritize content that adds genuine value to users, regardless of whether that value is delivered through a link or an AI summary.
Source: Search Engine Land News