The next time an artificial intelligence chatbot effortlessly curates a holiday itinerary or recommends a trendy new destination, there is a vital, yet overlooked, question that travelers should ask: “What am I not being shown?”
As generative artificial intelligence (GenAI) rapidly cements its role as the primary assistant for modern travel planning, the technology’s influence is undergoing a profound shift. While early discussions focused on how AI could help travelers compare hotel prices or find cheaper flights, a new study suggests that the technology’s most powerful impact occurs much earlier in the consumer journey.
AI is increasingly acting as an invisible gatekeeper. By narrowing the selection of potential destinations before a traveler even has the chance to consider them, these algorithms are quietly rewriting the global tourism playbook. For destination marketing organizations (DMOs) and hospitality brands worldwide, this algorithmic curation represents both an existential challenge and a paradigm shift in how travel must be marketed.
The New Gatekeepers of Global Tourism
For decades, travel research followed a familiar, open-ended path. A consumer would start with a search engine, browse travel blogs, scroll through social media feeds, and gradually narrow down a list of potential destinations. This process, while time-consuming, allowed for serendipitous discovery.
Generative AI models have collapsed this funnel. Instead of presenting a page of blue links that require manual evaluation, AI chatbots provide a single, synthesized answer. While this offers unprecedented convenience, it also introduces a massive filtering effect.
According to a study conducted by the marketing firm Create Consulting China, the true power of GenAI in travel lies in this early-stage curation. When an AI chatbot recommends a shortlist of destinations, it does not just rank options—it effectively erases the alternatives. If a country, city, or resort does not make the chatbot’s highly condensed recommendation list, it ceases to exist in the mind of the traveler using the tool.
This phenomenon of "algorithmic erasure" means that the battle for tourist attention is no longer fought on search engine results pages (SERPs) or through traditional social media advertising. Instead, it is fought within the training data and parameters of large language models (LLMs).
Inside the Study: How China’s Tech Giants Curate the World
To understand the mechanics of this digital gatekeeping, Create Consulting China conducted a rigorous stress test of the country’s leading artificial intelligence platforms. The research firm tested 240 distinct travel queries across five of China’s most prominent AI chatbots:
- Baidu’s ERNIE Bot: Developed by China’s search giant, ERNIE is deeply integrated with the country’s largest search index and web ecosystem.
- ByteDance’s Doubao: Powered by the parent company of TikTok and Douyin, Doubao leverages ByteDance’s sophisticated recommendation algorithms.
- Alibaba’s Tongyi Qianwen: Built by the e-commerce behemoth, this model is closely linked to transactional data and consumer behavior insights.
- Tencent Yuanbao: Drawing from Tencent’s massive social media and messaging ecosystem, including WeChat.
- DeepSeek: The highly efficient, open-source AI model that has recently disrupted the global tech landscape with its advanced reasoning capabilities.
The Methodology: Simulating the Chinese Traveler
To ensure the findings reflected real-world consumer behavior, the study utilized eight distinct traveler profiles. These profiles represented a broad spectrum of the modern Chinese outbound travel market, ranging from budget-conscious solo backpackers and luxury-seeking families to adventure travelers and culturally focused older tourists.
The researchers compared how these five AI models responded to two different types of searches:
- Broad, open-ended destination searches: Designed to see which countries the AI naturally prioritized when given complete freedom.
- Continent-specific or constrained queries: Designed to see how the AI filtered options when the user applied geographic limitations.
Among the tested queries were highly realistic holiday planning prompts, such as:
- “What are the best countries to visit for the National Day holiday?”
- “I want to travel to Europe for National Day. Which countries should I visit?”
The National Day holiday—often referred to as "Golden Week" in October—is one of China’s most significant annual travel periods. By testing queries centered around this high-stakes travel window, the study highlighted how AI models direct massive waves of potential tourist traffic during peak seasons.
The Mechanics of the Invisible Filter
The study’s findings point to a stark reality: when presented with broad queries like "What are the best countries to visit for the National Day holiday?", the AI models do not offer a diverse, balanced list of global options. Instead, they quickly narrow the field, heavily favoring a select group of destinations while consistently omitting others.
This filtering happens through several distinct algorithmic mechanisms:
1. The Bias of Training Data and Popularity Loops
AI models are trained on historical internet data. If certain destinations have historically dominated Chinese travel blogs, news articles, and social media discussions, the AI is highly likely to recommend them repeatedly. This creates a self-reinforcing popularity loop: because a destination is already popular, the AI recommends it; because the AI recommends it, more travelers visit and write about it, further solidifying its position in future AI training sets.
2. The Illusion of Choice in Constrained Queries
When queries were limited to specific continents—such as asking for European recommendations for the National Day holiday—the study revealed that the AI models still applied a heavy hand in curation. Rather than presenting an objective overview of Europe’s diverse regions, the chatbots tended to cluster their recommendations around traditional, well-trodden tourist hubs. Lesser-known eastern or northern European nations were frequently left out of the conversation entirely, even when they aligned perfectly with the traveler profiles used in the prompts.
3. Ecosystem Integration
In China’s highly integrated digital landscape, AI models do not operate in a vacuum. Alibaba’s Tongyi Qianwen, for instance, is naturally positioned to prioritize destinations and services that align with Alibaba’s travel platform, Fliggy. Similarly, Tencent’s Yuanbao draws heavily from WeChat’s vast ecosystem of official accounts and user-generated posts. This means that a destination’s visibility on a specific AI platform is deeply tied to its broader digital footprint within that tech giant’s proprietary ecosystem.
Implications for Destination Marketers: The Era of GEO
For global tourism boards, hoteliers, and tour operators, the rise of AI-driven travel planning requires a complete overhaul of traditional marketing strategies. The old playbook—relying on Search Engine Optimization (SEO) to rank high on Google or Baidu, or purchasing display ads on travel booking sites—is no longer sufficient.
In the age of AI, marketers must transition from SEO to Generative Engine Optimization (GEO).
To remain visible to travelers, destinations must ensure they are deeply embedded in the datasets that train these LLMs. This involves:
- Diversifying Digital Footprints: Tourism boards can no longer rely solely on their own websites. They must ensure that high-quality, structured information about their destination is present across a wide array of high-authority platforms, social media networks, and online encyclopedias that AI web crawlers frequently scrape.
- Targeting Niche Traveler Profiles: Since AI models customize recommendations based on user profiles, destinations need to create highly targeted, niche content. If a country wants to attract luxury family travelers, it must ensure that the specific digital content describing its luxury family offerings is robust enough for an AI to identify and synthesize.
- Fostering Local Tech Partnerships: For international destinations hoping to attract Chinese tourists, understanding the nuances of Baidu, ByteDance, Alibaba, Tencent, and DeepSeek is crucial. Engaging with these tech ecosystems directly through partnerships, digital campaigns, and structured data feeds will become essential for maintaining market share.
The Future of Travel: Serendipity vs. Algorithm
As travelers hand over more of their decision-making processes to artificial intelligence, the industry faces a broader philosophical question: Is the magic of travel being optimized away?
The beauty of exploration has historically been rooted in the unexpected—the sudden discovery of a small town, an unheralded national park, or a country that wasn’t on the original itinerary. By funneling millions of travelers toward a highly curated, algorithmically approved shortlist of destinations, GenAI risks exacerbating overtourism in already crowded hubs while leaving emerging destinations struggling for visibility.
For consumers, the takeaway from the Create Consulting China study is clear. While AI chatbots are incredibly efficient tools for organizing logistics and sparking initial ideas, travelers must remain conscious of the digital walls built around them. To truly experience the world, one must occasionally ignore the chatbot’s advice, step outside the algorithmic sandbox, and seek out the destinations that the AI chose to leave behind.
