How to use AI in Tourism – And How Your Destination Can Benefit

The tourism industry is undergoing a quiet revolution — one powered by artificial intelligence (AI). From personalized travel recommendations to smarter destination management, AI is helping destinations deliver richer, more engaging experiences for visitors while streamlining operations behind the scenes.

For destination management organizations (DMOs), tourism boards, and local tourism providers, embracing AI isn’t just about keeping up with trends — it’s about staying competitive, increasing visitor satisfaction, and unlocking new ways to showcase what makes your destination unique.

Why AI Matters in Tourism

AI is no longer science fiction. It's already shaping how travelers plan, experience, and remember their journeys. Here’s why it’s becoming essential in modern tourism:

• Personalization at Scale: Travelers today expect experiences tailored to their interests, language, and preferences. AI enables destinations to offer hyper-relevant recommendations, whether it’s a hidden-gem café, a scenic hiking trail, or a cultural festival happening next weekend.
• 24/7 Engagement: With AI-powered chatbots and virtual assistants, visitors can get instant answers to their questions — anytime, anywhere. No more waiting for email replies or office hours.
• Smarter Content Management: AI helps automate content tagging, translation, and even content generation, making it easier to maintain rich, multilingual and multmedial experiences across digital platforms.
• Data-Driven Decisions: By analyzing visitor behavior and preferences, AI provides actionable insights that help DMOs optimize marketing, manage crowds, and improve infrastructure.

At CityBot, we’ve integrated AI directly into our mobile guest app, allowing visitors to request personalized recommendations. The app engages users by presenting a curated selection of highly relevant POIs, each accompanied by a clear explanation of why it matches their interests. This approach helps visitors discover what they’re looking for quickly and accurately, reducing decision fatigue and preventing disengagement caused by overwhelming choice.

Meet The CityBot AI Travel Assistant: Smarter POI Recommendations in Real Time

How It Works: The Power of Retrieval-Augmented Generation (RAG)

Our chatbot is built using a technique called Retrieval-Augmented Generation (RAG) — a hybrid approach that combines the accuracy of semantic data retrieval with the fluency of large language models (LLMs). This ensures responses are not only relevant and factually grounded in your POI data, but also natural and easy to understand.

Here’s how it works in four key steps:

1. Enrich POI Content

Every Point of Interest (POI) in our Content Management System (CMS), from museums to mountain trails, has a detailed description, tags, categories, and metadata. This structured and unstructured text forms the knowledge base the AI draws from.

2. Convert Text to Embeddings

We use an embedding model — a type of machine learning model that translates text into numerical vectors (lists of numbers) to represent each POI’s description in a high-dimensional "meaning space."
In this space, semantically similar texts (e.g., “peaceful forest walk” and “quiet nature trail”) are close together, even if they don’t use the exact same words.

For example, in a simplified 3-dimensional space, a model might represent the following two phrases as:

"peaceful forest walk" → 0.9, 0.8, 0.7
"quiet nature trail" → 0.8, 0.9, 0.6

Normally there are hundreds or even thousends of dimensions depending on the model. The interesting thing is, that none of the numbers can be sematically interpreted.

Instead, the meaning emerges from the vector as a whole — like a fingerprint of semantic content. You can think of each number as analogous to a single neuron in a brain: individually insignificant, but powerful when activated in combination.

You might wonder: How does a machine “understand” that “peaceful forest walk” and “quiet nature trail” mean similar things, even if those exact phrases never appeared together in training?

The answer lies in how embedding models are trained, using massive amounts of text data and a clever learning objective that captures meaning through context.

Training on Context, Not Keywords

Embedding models are typically trained using a technique called self-supervised learning on vast corpora of text, such as books, web pages, Wikipedia, and multilingual content.
Rather than being explicitly taught definitions, the model learns by predicting missing words in sentences — a task that forces it to understand context.

For example, given the partial sentence:

"I went for a relaxing ___ in the forest."

The model learns that words like walk, hike, or stroll fit well and over millions of examples, it begins to group similar concepts together in the vector space.

This is based on the distributional hypothesis in linguistics: Words that appear in similar contexts tend to have similar meanings.

Popular embedding models include OpenAI’s text-embedding-3, Cohere’s embed models, and open-source options like Sentence-BERT or BAAI’s bge models. The space is evolving fast and a ranking of the best performing models based on different metrics can be found here.
These embeddings are stored in a high-performance vector database — such as Pinecone, Weaviate, or Chroma — enabling lightning-fast similarity searches.

3. Semantic Search with User Queries

When a visitor submits a query say, “Where can I take my kids to feed ducks and see flowers?” — their question is also converted into an embedding using the same model.

We then perform a cosine similarity search across the vector database to find the POIs with the most semantically related descriptions. This goes beyond keyword matching: it understands that “feed ducks” might connect to “pond” or “park with wildlife.”

4. Generate Natural Language Responses

The top-matching POIs and their descriptions found in step 3 are passed as context to a large language model (LLM) such as GPT-4, Claude, or an open-source alternative like Llama 3.

The LLM reads this context next to the question and generates a clear, conversational response, summarizing the best options, explaining why they match, and even suggesting nearby amenities or related tours — all while staying grounded in your official content.

This RAG approach ensures the AI never "hallucinates" — it only recommends real POIs from your database, making it both intelligent and trustworthy.

The Future is Personal, Intelligent, and Easy to Manage

You don’t need a tech team or a big budget to bring AI into your destination’s digital strategy. With Citybot, AI is built-in, intuitive, and designed to work the way modern travelers expect it to work.

By combining powerful content management with intelligent visitor engagement, we’re helping destinations around the world create more meaningful connections with travelers — one personalized recommendation at a time.