Top AI use cases in Travel: What online travel platforms should know

Common AI use cases in the travel industry. Discover how AI is transforming online travel agencies through automation, personalization, and better experiences.  

AI is no longer just an add-on but has become a top priority for travel companies, especially online travel agencies (OTAs). IMARC (2025) reported that OTAs have leveraged AI to turn standard browsing into personalized experiences. They track searches, previous trips, and wishlists to recommend flights, hotels, restaurants, and activities that feel hand-picked. Dynamic pricing adjusts ticket or room costs instantly, providing clients with offers that encourage reservations. Automated chatbots handle questions, refunds, or changes anytime. 

AI in travel software development is expanding across various aspects of the travel industry. This guide explores some of the most common AI use cases in online travel platforms and how OTAs are using AI to improve the travel booking experience. It is designed for travel companies and OTA teams looking to integrate AI effectively in their operations.  

Key takeaways 

• OTAs have leveraged AI to track searches, previous trips, and wishlists, recommending flights, hotels, restaurants, and activities tailored to each traveler. 

• Personalization has become more important, with71% of consumers expecting companies to deliver personalized interactions and 76% feel frustrated when they do not receive them. 

• AI travel planning helps travelers plan trips and build personalized itineraries without manually searching for and combining individual travel services. 

• 70% consider real-time travel assistance somewhat or very important, meaning that travelers increasingly expect fast and real-time assistance when planning and managing their trips, 

• While dynamic package recommendations combine multiple travel products into a package, personalized search focuses on which individual products should be shown first and recommended to each user. 

Why AI matters in online travel platforms 

Travelers increasingly expect personalization  

McKinsey found that 71% of consumers expect companies to deliver personalized interactions, while 76% feel frustrated when they do not receive them, highlighting the importance of personalization in overall travel experience.  

For online travel platforms, however, delivering this level of personalization is challenging at scale as each traveler generates a large amount of data. By analyzing large volumes of customer and travel data in real time, AI can identify patterns, understand individual preferences, and deliver relevant recommendations to each traveler. 

Travel booking journeys have become more complex  

Travel booking is becoming increasingly complex. According to Accenture (2024), 68% of travelers use up to 10 websites when planning a trip. Also, they often rely on multi-tabbed browsing sessions, bookmarking websites, offline spreadsheets, and detailed note-taking. 

Instead of making users manage each step separately, AI can support them throughout the journey by automatically extracting travelers’ requirements, searching relevant travel information, and recommending suitable options. This can reduce the time and effort required to organize a trip while creating a more seamless booking experience. 

Online travel platforms handle big data  

Online travel platforms process large volumes of data from multiple sources, including customer searches, booking history, flight and hotel availability, pricing, reviews, destination information, and customer interactions. This data is also constantly changing as prices, availability, demand, and traveler preferences shift. 

AI can process large datasets, identify patterns, and generate insights much faster, allowing travel platforms to better understand customer behavior, optimize recommendations, and respond to changes in demand.  

Increasing competition between travel companies  

Travelers are increasingly using AI generators to research destinations, build itineraries, and discover flights, hotels, restaurants, and activities. Phocuswright reported that 39% of active U.S. travelers were using AI for travel, including tools such as ChatGPT, Google Gemini, and AI-enabled search engines. Also, these AI tools are also moving beyond trip planning toward booking, meaning they could become an alternative starting point for travel discovery.  

However, OTAs have an important advantage: they already have direct access to travel inventory and booking infrastructure. By integrating AI into their existing platforms, OTAs can combine conversational trip planning and personalized recommendations with real-time flights, hotels, activities, and booking capabilities. This can help close the gap between AI-first platforms and traditional travel platforms. 

AI use cases in online travel booking platforms 

01. AI trip planning 

AI trip planning tools help travelers plan trips and build personalized itineraries without manually searching for and combining individual travel services. McKinsey (2025) found that more than half of respondents had used ChatGPT or a similar AI tool for trip planning, while the share using these tools extensively increased by 124% from 2024 to 2025. 

AI travel planning can simplify the process by: 

• Understanding traveler needs through user prompts: extract destinations, travel dates, budget, interests, number of travelers, and other preferences from natural-language input.  

• Building day-by-day itineraries: organize destinations, activities, meals, transportation, and estimated schedules into a coherent itinerary.  

• Recommending relevant travel products: suggest suitable flights, hotels, restaurants, tours, and activities.  

• Adjusting and optimizing the itinerary: users can refine the plan through follow-up prompts.  

For travel booking platforms, this means AI can move the experience from “search and compare everything yourself” to “tell us what you need and get a ready-to-book trip plan”. Using AI trip planning websites can help travelers save time and increase personalization, with 42% of consumers said AI travel planning helps them save time, while 37% use AI to receive highly personalized recommendations (Amadeus, 2025).  

Recommended tech stack: LLM (OpenAI/Claude) + RAG (LlamaIndex/LangChain, Pinecone/pgvector) + recommendation engine (Python, Scikit-learn/XGBoost) + travel/booking APIs (Amadeus/Sabre) + Maps APIs (Google Maps/Mapbox) + backend (Python/Node.js). 

02. AI Chatbot/ AI Voicebot  

AI Chatbot/ Voicebot provides 24/7 support throughout the customer journey, allowing travelers to interact through text or voice and receive continuous assistance from trip planning to booking and post-booking support. Travelers increasingly expect fast and real-time assistance when planning and managing their trips, with 70% considering real-time travel assistance somewhat or very important.  

AI chat travel assistant can provide this support without requiring users to navigate through multiple pages or wait for a human agent:  

• Answer travel-related questions about destinations, travel times, flights, hotels, activities, and other travel information. 

• Search and retrieve relevant information from connected travel databases, CMSs, booking systems, and external APIs. 

• Support booking activities when integrated with the platform’s booking engine, allowing users to book through conversational interactions. 

• Provide continuous support throughout the journey. 

Recommended tech stack: LLM (OpenAI/Claude) + RAG (LangChain/LlamaIndex, Pinecone/pgvector) + Voice AI (STT/TTS) + travel/booking APIs + CRM integration + backend (Python/Node.js). 

03. Conversational AI booking  

Conversational AI allows travelers to search and interact with an AI travel booking platform using natural language. Instead of selecting each filter manually, users can describe what they need in a single request. Then AI can:  

• Understand natural-language requests: Extract key requirements such as destination, travel dates, budget, number of travelers, and preferences. 

• Search relevant travel products: Connect with the booking engine, travel APIs, or inventory systems to find suitable flights, hotels, tours, and other services. 

• Compare available options: Present relevant choices based on price, timing, location, amenities, and booking conditions. 

• Handle follow-up requests: Adjust recommendations when users change their dates, budget, hotel preferences, or other requirements. 

• Support the booking process: When integrated with the booking engine, guide users from search and selection through booking and confirmation. 

Recommended tech stack: LLM + Elasticsearch/OpenSearch + travel/booking APIs + Python/Node.js + Redis. 

04. Dynamic package recommendations 

Traditional travel packages are often pre-designed around fixed combinations of products. AI can make this approach more flexible by dynamically creating and recommending packages based on each traveler’s requirements, preferences, and real-time travel conditions. 

• Analyze traveler preferences: Use search behavior, booking history, budget, travel dates, destinations, and interests to understand what each traveler is looking for. 

• Combine travel products: Dynamically combine flights, hotels, tours, activities, airport transfers, and other services into relevant packages. 

• Match packages to user requirements: Recommend combinations that fit specific constraints such as budget, trip duration, location, or preferred hotel category. 

• Adjust packages: Modify individual components when users change their requirements, such as lowering the budget, changing the hotel, or adding an activity. 

• Consider real-time conditions: Take current availability, pricing, and travel inventory into account when generating recommendations. 

Recommended tech stack: Recommendation engine + LLM + travel/booking APIs + Elasticsearch + Python/Node.js + PostgreSQL/Redis. 

05. Optimized search & personalized recommendation 

Travel platforms can offer thousands of flights, hotels, tours, and activities, but showing the same search results to every user may not reflect their individual preferences. AI can personalize how these products are ranked and recommended:  

• Personalize search rankings: Rank flights, hotels, tours, and activities based on user preferences, previous interactions, and booking behavior. 

• Learn from user behavior: Analyze searches, clicks, bookings, viewed products, and abandoned searches to identify patterns and preferences. 

• Personalize results for returning users: Use previous interactions to make future searches more relevant without requiring users to enter the same preferences again. 

• Recommend relevant products and destinations: Suggest destinations, hotels, tours, or activities that match a user’s interests and travel behavior. 

Unlike dynamic package recommendations, which combine multiple travel products into a package, personalized search focuses on which individual products should be shown first and recommended to each user. 

Recommended tech stack: Recommendation engine + Elasticsearch/OpenSearch + ML ranking + Python + PostgreSQL/BigQuery. 

For example, Adamo Software developed and maintained a travel booking platform designed to help customers search for tours, explore holiday and tour content, view departure points and promotions, and move through the booking journey. 

The platform supported multiple brands and combined legacy code, APIs, CMS, and search engine technologies, making the system relatively complex to maintain. The client also faced challenges related to search filters, Elasticsearch results, CMS content, caching, responsive UI, and user tracking. After analyzing the existing system, Adamo Software focused on maintaining and improving the holiday search and tour page flows. The team also implemented tracking tools for key interactions, including tour searches and departure point selection. Changes were implemented with a minimal-impact approach to avoid disrupting the platform’s existing business logic. 

The project received positive feedback from the client and provided a stronger foundation for improving the travel search and booking experience. 

06. Dynamic pricing and demand forecasting  

Travel demand changes constantly based on seasonality, destination, travel dates, holidays, and market conditions. For online travel booking platforms, understanding these changes can help optimize pricing, promotions, and inventory decisions. 

How AI can support dynamic pricing and demand forecasting:  

• Forecast travel demand: Predict demand for specific destinations, travel dates, hotels, tours, or other products. 

• Predict booking volumes: Estimate how many bookings a product may receive based on historical and current data. 

• Identify demand patterns: Detect periods of high or low demand across destinations and travel seasons. 

• Support pricing decisions: Provide insights that help determine appropriate pricing or promotional strategies as demand changes. 

• Predict booking changes: Identify patterns that may indicate a higher likelihood of cancellations or booking changes. 

Recommended tech stack: ML + time-series models + BigQuery/Snowflake + Kafka + Python. 

Final thoughts  

Advanced algorithms in AI change the face of the tourism industry by bringing actionable insights from large data sets. They optimize price models based on real-time analysis of market trends, competitor pricing, and customer demand, thus dynamizing their price structures for revenue maximization (Markets and Markets, 2025).  

However, implementing AI in travel is not simply about adding an LLM to an existing platform. Real-world travel applications need to connect AI with travel and booking APIs, search and recommendation engines, customer data, maps, and existing booking systems. They also need reliable, up-to-date data to provide accurate recommendations and real-time travel information. 

Unlock the full potential of AI in the travel industry with Adamo Software  

Adamo Software is a Vietnam-based AI and software development partner delivering custom AI solutions for clients across the EU, US, ANZ, and Singapore. Our services include custom software development, dedicated development teams, and AI development. Adamo Software focuses on 2 verticals that many technology providers treat as sidelines: Travel & Hospitality and Healthcare. With experience across multiple travel projects, Adamo’s teams have received positive feedback from clients for their technical capabilities, responsiveness, and commitment throughout delivery. 

For travel software development, Adamo Software collaborates with travel and hospitality suppliers of all types, empowering them to connect with travelers and optimize their operations. Also, we help travel distributors (OTAs and tour operators) deliver smooth bookings, increase sales, and create personalized experiences. 

Adamo has built two-way integrations with major travel platforms and marketplaces, including Expedia (EQC), Airbnb, Viator, GetYourGuide, and Traveloka. For broader hotel inventory aggregation, Adamo works with Hotelbeds, while the Travelgate integration enables unified channel management through a single API.