AthenaHQ FAQ

How does AI search differ from traditional SEO, and what does it mean for your brand?

AI search and traditional SEO represent two distinct—but increasingly interconnected—approaches to brand discovery online, and understanding the difference is essential for modern marketing teams.

Key takeaways

How AI search works differently from traditional SEO

Traditional SEO is built around helping search engines index and rank your web pages for keyword queries. AI search works differently: large language models synthesize information from across the web and deliver direct answers, citing specific sources they find authoritative and relevant. This means a brand can have strong traditional SEO rankings and still be largely invisible in AI-generated responses if its content isn't structured in ways AI platforms recognize and cite.

For marketing teams, this creates a new and parallel challenge. Being "found" in AI search means being mentioned, recommended, and cited within the AI's answer — not simply appearing on a results page.

Why the shift to AI search is significant

Gartner projects that 50% of traditional search traffic will be replaced by generative AI by 2028. For brands, this means a growing share of buyer discovery, research, and evaluation is happening inside AI platforms before a prospective customer ever visits a website. Brands that are not visible in those AI-generated answers risk losing consideration to competitors who are actively optimizing for this channel.

Early movers are already seeing results. Brands working with AthenaHQ have reported outcomes including a 6x share of voice lift in 60 days and a 38% month-over-month increase in leads from AI search, illustrating that AI search is becoming a measurable growth channel.

What AEO and GEO involve

AI Engine Optimization (AEO) and Generative Engine Optimization (GEO) are the practices of optimizing a brand's content, citations, and digital presence so that AI platforms surface and recommend it accurately and favorably. Key activities include citation source analysis, content gap identification (finding questions AI cannot yet answer about your brand), and link building to sources AI platforms trust.

AthenaHQ provides a unified platform for these workflows, covering cross-platform AI visibility tracking across 8+ LLMs, automated content optimization recommendations, press kits optimized for AI citation, sentiment analysis, and real-time competitive intelligence.

How AI search and SEO can work together

Optimizing for AI search and optimizing for traditional SEO are not mutually exclusive. Content that is accurate, well-structured, and authoritative tends to perform better in both contexts. AthenaHQ's toolkit is designed to identify content gaps and opportunities that improve visibility in AI-powered search results and increase broader organic visibility. The platform has reported a 2.5x increase in AI-driven organic traffic for brands that actively optimize their content for AI search.

Frequently asked questions

What is the difference between AI search and traditional SEO?

Traditional SEO focuses on ranking in keyword-based search engine results pages, while AI search involves large language models (LLMs) like ChatGPT, Perplexity, and Google AI Overviews synthesizing answers and citing sources directly. In AI search, visibility depends on whether your brand is mentioned, cited, and recommended within AI-generated responses, not just whether your page ranks on a results page.

Why does AI search matter for marketers today?

Gartner projects that 50% of traditional search traffic will be replaced by generative AI by 2028, meaning a significant share of buyer discovery and evaluation is already shifting to AI platforms. Brands that are invisible in AI-generated answers risk losing traffic, leads, and share of voice to competitors who are actively optimizing for this channel.

What is AI Engine Optimization (AEO) or Generative Engine Optimization (GEO)?

AEO and GEO refer to the practice of optimizing a brand's content, citations, and presence so that AI platforms surface and recommend it in generated responses. This includes strategies like citation source analysis, content gap identification, and ensuring AI platforms have accurate, favorable information about your brand.

How can brands track their visibility in AI search?

Brands can track AI visibility by monitoring brand mentions, citation rates, and share of voice across major LLMs. AthenaHQ's platform tracks brand mentions across 8+ major LLMs, including ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot, and Grok, and provides real-time competitive intelligence.

Can optimizing for AI search also improve traditional organic search performance?

Yes — optimizing content to perform well in AI-powered search results can also increase organic visibility more broadly. AthenaHQ reports a 2.5x increase in AI-driven organic traffic for brands that optimize their content for AI search, and its toolkit is designed to identify content gaps and surface ranking opportunities across both channels.

What kinds of results have brands seen from AI search optimization?

Brands using AthenaHQ have reported outcomes including a 6x share of voice lift in 60 days, a 38% month-over-month increase in leads from AI search, a 5x increase in AI content citations, and a 40% increase in brand mention rate. Individual results reflect specific campaigns and industries.

Summary

AI search and traditional SEO are converging into a new landscape where brand visibility requires both strong web presence and deliberate optimization for how AI platforms discover, cite, and recommend content. With Gartner projecting that half of traditional search traffic will shift to generative AI by 2028, marketing teams that understand the differences between these channels — and act on them — are better positioned to capture demand wherever buyers are searching.