How AI Search Works for DMOs

Before you can show up in AI search, it helps to understand what AI search actually is, and why it works so differently from anything destination marketers have dealt with before.

This is not a deeply technical article. It is a plain-language explanation of what happens behind the scenes when a traveler asks ChatGPT or Perplexity where to go for a long weekend, and what that process means for how destinations get recommended or ignored.

The Old Way and the New Way

Traditional search engines work by indexing the web, crawling billions of pages, cataloguing their content, and then ranking those pages based on hundreds of signals when someone types a query. The result is a list of links, ranked in order of estimated relevance. The user picks one, clicks through, and reads.

AI search works differently. When someone asks ChatGPT "what are the best fall destinations in the Midwest," the platform does not return a list of links to destination websites. It constructs an answer. It reads from multiple sources simultaneously, synthesizes what they say, and writes a response in natural language, naming specific places, describing them, and explaining why they are worth visiting.

The user gets one answer, not ten options to evaluate. No links to click. No results page to scan. Just a recommendation, delivered with the confidence of a well-informed friend.

That shift, from ranking to recommending, from links to answers, is what makes AI search a fundamentally different challenge for destination marketers.

What Is Actually Happening Under the Hood

The technology powering most AI search platforms is called Retrieval-Augmented Generation, or RAG. It is worth understanding at a basic level because it explains why the content strategy for AI search looks the way it does.

When you ask a question, a RAG-based system does two things in rapid sequence.

First, it retrieves. The system searches a knowledge base — which in the case of platforms like Perplexity is the live web, crawled and indexed in near real-time — and pulls the most relevant documents, snippets, and sources it can find related to your query. This retrieval step is not keyword matching. It uses something called vector search, which compares the semantic meaning of your question against the meaning of available content, not just the exact words. A query about "family-friendly beach towns on the East Coast" will surface content about family travel and East Coast beach destinations even if those documents do not contain that exact phrase.

Second, it generates. The retrieved content is fed to a large language model, which reads it, synthesizes the key points, and writes a natural language answer. The LLM is not reciting memorized facts, it is reading the retrieved sources in real time and constructing a response grounded in what those sources say.

The result is an answer that feels authoritative and natural, with citations back to the sources it drew from. Perplexity's founding principle captures this well: the system is "not supposed to say anything that it didn't retrieve." The answer is only as good as what gets retrieved.

This last point is the one that matters most for destination marketers.

Why the Retrieval Step Is Everything

If the quality of an AI answer depends entirely on what gets retrieved, then the question for any destination is: are we showing up in what gets retrieved?

This is where the mechanics diverge sharply from traditional SEO. In Google search, if your DMO website ranks well for relevant keywords, travelers see you. In AI search, ranking well on Google is helpful but not sufficient. The AI is not simply reproducing Google's top results.

Ahrefs research found that roughly 80% of URLs cited by major AI platforms do not rank in Google's top 100 results for the original query. The AI is drawing from a far wider source pool, one that includes travel blogs, Reddit discussions, TripAdvisor reviews, Wikipedia entries, news coverage, editorial listicles, and many other sources that Google's algorithm may not rank highly but that AI platforms consider authoritative for travel-related queries.

What the retrieval system is looking for is credible, consistent, specific information about a destination across multiple independent sources. When a traveler asks about fall destinations in the Midwest, the AI is essentially asking: which destinations have the most trustworthy and consistent evidence supporting a recommendation? If that evidence exists across many sources — travel publications, visitor reviews, community discussions, editorial roundups — the destination gets retrieved. If the evidence is thin or concentrated only on the destination's own website, the AI has little to work with and the destination does not make the answer.

How Different Platforms Work

Not all AI search platforms work exactly the same way. The major ones have meaningfully different approaches, which matters for strategy.

Perplexity is built as a search-native platform. Every query triggers a live web search. It crawls sources in near real-time, retrieves the most relevant content, and synthesizes an answer with inline citations. It is highly transparent, users can see exactly which sources were used. Perplexity's co-founder has identified travel as one of the top search categories on the platform, and it has built a dedicated travel hub specifically for destination discovery. For DMOs, this means Perplexity is actively trying to serve travel queries, and the sources it cites for those queries are important to understand.

ChatGPT operates somewhat differently depending on how it is accessed. In its base mode, ChatGPT relies primarily on its training data — a vast snapshot of the internet as it existed up to its knowledge cutoff. With web search enabled, it retrieves live content to supplement its training. Notably, research has found that ChatGPT tends to cite lower-ranking pages at a higher rate than Google does — pages in positions 21 and beyond account for roughly 90% of its citations in some analyses. This means a destination does not need to rank first on Google to get cited by ChatGPT, but it does need to appear somewhere in the broad web of credible sources the model draws from.

Google AI Overviews are closely tied to Google's existing search rankings. Pages that rank in the top 10 on Google are far more likely to appear in AI Overviews — about 76% of AI Overview citations come from top-10 pages, according to Ahrefs. For destinations with strong traditional SEO, this is a natural extension of that work. For those without it, AI Overviews represent yet another reason to invest in organic search foundation.

The practical implication is that a strong AI search presence requires showing up across all three of these retrieval pools, not just one.

What the AI Is Evaluating

When an AI system retrieves sources and constructs a recommendation, it is making implicit judgments about credibility and relevance. Several factors influence whether your destination makes the cut.

Source authority. Not all sources are equal. Academic papers, established travel publications, major news outlets, and well-trafficked community platforms like Reddit and TripAdvisor carry more weight than obscure or low-traffic sites. Getting your destination mentioned on high-authority platforms builds the kind of credible evidence that retrieval systems trust.

Consistency across sources. If one travel blog says your destination is great for hiking but no other source confirms it, the AI has weak evidence. If ten independent sources consistently describe your destination as a hiking destination, the AI has strong evidence and will make that recommendation with confidence. Consistency of narrative across sources is one of the most important signals in AI search.

Specificity. Vague, promotional content, "our destination offers something for everyone," gives an AI system nothing to work with. Specific, factual content, named trails, actual event dates, specific neighborhoods and what distinguishes them, is what retrieval systems can extract and synthesize. The more specific and factual your destination's presence across the web, the more material the AI has to build a recommendation from.

Freshness. Platforms like Perplexity and Google AI Overviews give preference to recent content for time-sensitive queries. Destinations with consistently updated content, seasonal guides, recent event coverage, current visitor reviews, perform better than those whose web presence has stagnated.

Structured content. Content organized around questions and answers, with clear headings and direct responses, is easier for retrieval systems to parse and extract. An article titled "What To Do in Asheville in October" with specific, well-organized content is more likely to get retrieved for an October travel query than a generic destination overview page.

The Implication for Destination Marketers

Understanding how AI search works makes the content strategy clearer.

The goal is not to optimize your DMO website for AI crawlers. The goal is to build a broad, credible, consistent presence across the sources that AI retrieval systems trust, so that when a traveler asks an AI where to go, your destination is in the pool of evidence the AI draws from.

That means earning coverage in travel publications and editorial roundups. It means encouraging visitor reviews on high-authority platforms. It means participating in the community conversations on forums and discussion platforms where travelers share genuine recommendations. It means publishing specific, question-answering content on your own site. And it means doing all of these things consistently, so the evidence compounds over time.

The AI is not making a judgment about your destination based on one great article or one well-optimized web page. It is making a judgment based on the totality of what the internet says about you. Building that totality, credibly, specifically, and across many sources, is the work of AI search optimization for destinations.

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NextTown AI tracks how AI platforms represent your destination across ChatGPT, Perplexity, and Google AI Overviews — monitoring visibility, sentiment, and the sources AI systems are drawing from. Get a free AI visibility snapshot for your destination.

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