Generative AI in travel: From trip planning to guest support

These days, AI handles real-time trip planning, saving time and increasing booking rates by up to 35%, provides automated suggestions for optimal next destinations, checking the flight status to baggage assistance; eliminates language barriers in virtually all customer interactions; and scales personalization across millions of users at once. With so many use cases, you can’t avoid integrating Generative AI in the travel business if you want to succeed.

Booking.com, Expedia, Priceline, and Tripadvisor have released their own AI-based trip planners, and there seems to be no stopping this tidal wave of Gen AI in travel due to the great potential of AI to impact tourism in several ways.

For a full guide to starting your AI journey and the key questions to ask vendors, read on! We’ll cover the main use cases, pain points, opportunities, and challenges and list the best tool options to get.

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What is Generative AI in travel?

Generative AI in travel is a category of artificial intelligence that creates original content like personalized itineraries, real-time guest responses, dynamic pricing copy, and translated communications by learning patterns from large datasets and producing outputs that didn't exist before the request was made. Unlike rule-based automation that executes fixed logic, generative AI reasons through context and generates something new each time.

In practice, this means a traveler asking "Plan me five days in Japan for under $2,000, I hate tourist traps," gets a response that wasn't pre-written and isn't a template. The system synthesizes preferences, budget constraints, real inventory data, and behavioral signals into something that actually fits that person.

We saw this directly when building a travel platform for Australian content creators, where the personalization layer had to reflect a specific travel philosophy, not just filter by price and destination. The platform needed to surface experiences that matched a traveler's stated identity, not just their search keywords. That distinction is exactly what generative AI in travel makes possible at scale.

Why is Generative AI different from traditional AI?

Generative AI differs from traditional AI because it creates original outputs rather than executing predefined rules, and that difference determines what's actually possible for travel personalization, content, and guest communication.

Traditional AI in travel is useful for structured, repeatable decisions: flagging a booking anomaly, routing a support ticket, and predicting occupancy based on historical data. It works within boundaries that developers define in advance. When the situation doesn't match the rules, traditional AI fails or falls back to a default.

The travel generative AI model works differently across four dimensions.

• Creativity and output. Traditional AI pattern-matches against existing data and executes known logic. Generative AI produces outputs that didn't exist in its training set, like a custom itinerary for a specific traveler's budget and travel style, a guest email in a brand's voice, or a translated response that reads naturally.
• Architecture. Traditional systems use decision trees, support vector machines, and rule-based logic. Generative models run on deep neural networks (like GANs, VAEs, and Transformer architectures) that process language and data at a structural level that traditional systems can't reach.
• Training methodology. Traditional models are trained on human-labeled datasets with explicit rules for every scenario. Generative models discover relationships autonomously through semi-supervised or unsupervised learning, so they can surface patterns in travel behavior that no analyst thought to look for.
• Flexibility and adaptability. Traditional AI needs a developer to update it when conditions change. The generative AI travel industry applications adapt dynamically: a pricing model that recalibrates when a local event drives unexpected demand, a chatbot that adjusts its tone based on the conversation context, and a proposal generator that accounts for a corporate client's past preferences without being programmed to do so.

"The key difference between traditional automation and generative AI is scope," says Orest Falchuk, Head of Engineering at COAX. "Traditional systems answer the questions you anticipated. Generative AI can respond to questions you didn't know your guests would ask, in a way that actually sounds like your brand."

All these distinctions make generative AI for travel companies so attractive, the ability to create original content flexibly and without extra supervision. 

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How is Generative AI transforming the tourism industry?

Gen AI is changing the industry by automating the generation of personalized itineraries, enhancing the efficiency of resourcing, and improving customer engagement through aspects of the travel journey. Amadeus reports that almost half (46%) of travel organizations consider Generative AI as their leading technology investment.

Research shows that leading AI chatbots, tailored for planning itineraries, were able to achieve over 90% effectiveness in planning a varied type of trip. All this is possible thanks to advanced mechanisms analyzing the experience richness, traveler preferences, and budgets, both for tourist organizations and independent travelers.

What marks the importance of Generative AI for travel development is the complexity of the domain. Travel decisions involve hundreds of interdependent variables like budget, timing, group size, past behavior, real-time inventory, and local context that traditional rule-based systems can't hold simultaneously. Generative AI can. That's why it's moving from experimental to operational across the industry faster than almost any previous technology shift.

Common pain points in travel and how Gen AI can help

The most persistent pain points in travel, such as rising costs, fragmented booking, language barriers, and poor personalization, are exactly where generative AI delivers measurable relief, both for travelers and the businesses serving them.

Here's how Generative AI is addressing these high-priority problems: 

• Increased costs and uncertainty. According to GoingGlobalTV, average international airfare hit $705 in 2025, and average hotel rates rose to $165 a night. Generative AI optimization travel tools solve this problem by offering optimization services that leverage the Maximum Likelihood algorithm to analyze historical price data to project when prices are most likely to go up or down. This allows travelers to book at the best time and receive alerts in real time, and businesses not to lose potential customers because of overly high rates.
• Time-consuming planning. Research shows that the average American spends nearly 18 hours researching and booking a single trip. Travel generative AI chatbots cut through this time by instantly answering complex questions, aggregated from various sources, and generating personalized itineraries within minutes, using preferences for tastier options in future recommendations.
• Barriers of different languages. When you are unable to read important information or converse with locals, traveling abroad becomes difficult. AI translation tools that assist with real-time conversation and camera integration that translate spoken dialogue, menus, and signboards help interact with local cultures and navigate new spaces.
• Insufficient customization. The modern world demands personalization. According to McKinsey, 52% of Gen Z tourists demand special experiences to their own liking. To help with it, Gen AI uses machine learning to analyze user behavior, previous travel experiences, and preferences to provide hyper-personalized recommendations.
• Disconnected booking interface. Travelers struggle to keep up with multiple competing interfaces across their bookings (ultimately leaving itineraries abandoned). Gen AI addresses the problem as it provides a unified booking experience through natural language processing capabilities. It allows for an easy comprehension of multifaceted requests across services. 
• Stress and delays. According to the U.S. Travel Association, 46% of travelers rate their overall travel experiences as inadequate due to delays and congestion. Gen AI helps reduce this uncertainty and stress with tools like travel-relevant predictive navigation offerings that provide predictable traffic and delays. 

In many cases, travel businesses increase their revenues by up to 20% by using Gen AI. The mechanism isn't mysterious: fewer abandoned bookings, higher personalization-driven conversion, and reduced support overhead compound quickly.

"The pain points in travel aren't new: they are typically slow planning, broken handoffs, and generic recommendations. What's changed is that generative AI can actually address all of them through one coherent layer rather than a patchwork of separate tools," says Kostiantyn Lopukh, Senior Developer at COAX.

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Use cases of Generative AI Across the travel journey

Generative AI delivers the most value in travel when applied to specific, high-friction moments in the guest journey (itinerary creation, real-time support, shift handoffs, and cross-language communication) rather than deployed as a generic layer across everything at once. To use AI wisely and profitably, you need to understand the key LLM use cases that call for specific measures taken.

• Creation of custom itineraries.

Traveler inputs, such as budget constraints, personal interests, preferred destinations, and aggregated reviews, are processed by generative AI in travel solutions to produce customized travel plans that optimize daily schedules. The technology takes into account realistic limitations like travel times, venue opening hours, and spending caps. The demand is there: 77% of travelers are open to generating an AI-based itinerary.

On the platform side, this works only when the underlying inventory data is reliable. When we integrated Nezasa's TripBuilder into the ARRIVAL platform, the main challenge was ensuring that availability, pricing, and activity options stayed synced in real time so that what the AI surfaced was bookable. The content layer and the inventory layer have to be connected.

• Answer engines.

AI-driven answer engines handle traveler questions about amenities, cancellation policies, flight delays, and gate changes in natural language, pulling accurate answers from your existing databases without requiring a human agent in the loop for routine queries.

The results are impressive: AI chatbots currently handle 80% of initial customer inquiries in the travel industry and achieve a 90% customer satisfaction rate when it comes to answering travel-related questions. These systems are available around-the-clock to handle their needs when human agents are not available. But from our experience, the ceiling is data quality. An answer engine is only as accurate as the systems it queries. Before any conversational AI layer can perform, the booking engine, CRM, and PMS data need to be consistent and current.

• Summaries for faster guest support.

Helping your customers quickly isn’t easy, and when shift change comes, it gets even more complicated. However, it’s easier with Gen AI in the travel industry. Generative AI condenses the full interaction history: emails, chat messages, calls, and booking notes into a brief that gives the next team member everything relevant without reading through a full thread.

AI-based support systems merge multiple points of contact into briefs that summarize previous preferences, requests, and unresolved issues. This permits housekeeping to view the guest's entire context instantly. With 72% of hotel guests more likely to return when AI has been used to provide personalized services, this technology matters a lot.

• Localization.

Generative AI travel industry applications in translation go well beyond converting text from one language to another. Modern systems handle spoken dialogue in real time, translate camera-captured signage and menus, and adapt phrasing for cultural context rather than producing literal output that reads as foreign.

Research shows that AI-based translation improves communication for international travelers by 50%. For platforms serving international audiences, this is an infrastructure decision as well. When we built DriveIQ for an operator running routes between Ukraine and Poland, the LLM-based communication layer was built to handle English, Ukrainian, and Polish natively, with human-in-the-loop approval for any message touching SLA commitments.

Opportunities and risks

The integration of all these use cases of generative AI in travel creates significant business outcomes through improved guest satisfaction, reduced operational workload, and increased revenue opportunities. 

• Industry data show that 75% of travel companies identify AI as an important influencer of customer experience. Additionally, when travel companies enhance their services with AI, they get their customers to be 83% more likely to reserve any offerings from them compared to companies that don’t.
• The efficiency of your operations is great, too. Research shows that AI is able to lower check-in times by up to 30% in airports, speed up security checks with the help of facial recognition, and automate up to 30% of tasks connected to handling luggage. 
• There’s a chance of revenue enhancement — when dynamic pricing is presented for hotels or air operators, AI capabilities can make the best use of seasonal pricing changes that lead to approximately a 15% increase in the overall income. 
• Personalization with AI-powered techniques also allows for upselling opportunities. With it, you can create tailored offers for guests. Modi's research has shown that 45% of hotel bookings are credited to personalized marketing based on AI use. 

Overall, AI technologies demonstrate a clear return on investment for companies that choose to share the technology with their travelers. AI is a great power, but as you know, with great power comes great responsibility. Generative AI in travel carries several important risks that organisations must manage to protect customers, comply with regulations, and preserve trust.

• Data privacy and security risks arise from handling sensitive PII (passenger names, passport numbers, payment details) and behavioural profiles; weak controls can cause breaches or unlawful data sharing. 
• Bias and unfair outcomes may surface when models reflect skewed training data. This can misrecommend options or discriminate against travellers with particular needs.
• Intellectual property and copyright issues affect generated images, descriptions, or aggregated guides when training or outputs inadvertently reproduce protected content. Fifth, regulatory and liability gaps create uncertainty about who is responsible when AI-driven advice causes losses or safety incidents, complicating insurance.
• Vitally important is a matter of transparency, which relies on citations and explainability. Operators need to cite decisions from original data sources,  like brochures, schedules, or policy documents. By embedding explainable AI techniques like SHAP, the operator becomes more reliable and also more trusted by end customers and regulatory agencies.
• Controlling hallucinations, which continue to be one of the main threats for large language models, is also essential for reliability. As demonstrated in the Air Canada case, PhocusWire emphasizes that when AI produces false or misleading travel information, the repercussions can be both financial and reputational. RAG, prompt refinement to minimize irrelevant output, and fallback options if a confident response cannot be given are necessary to prevent such outcomes.
• Lastly, the use of Gen AI in travel must be framed with compliance with global standards. The GDPR and CCPA continue to edify core principles of privacy and transparency. Also, the EU AI Act limits high-risk applications and discloses content. Other international contexts have begun to build ethical frameworks and engage labor protections found in the UN Global Compact and OECD reports. 

This evolving framework of global standards is taking shape as a normative safeguard that AI will not erode trust but enhance responsible development and implementation.

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Data sources for Generative AI in travel

We mentioned the importance of travel data before, but now, let’s look at this data in more detail. There are several sources from which you can draw data for AI systems.

External data

The sources that go beyond your company’s operations but connect you with outside systems present valuable data.

• GDS/NDC feeds from companies like Amadeus and Sabre give travel planners one location to find travel inventory, which includes flights, hotels, and car rentals by airline APIs. Also, the combination of generative AI and NDC improves customer experience and produces innovations through better distribution methods and better access.
• Online Travel Agencies (OTAs) such as Expedia, Booking.com, or TripAdvisor collate deals and allow consumers to book travel directly online, catering primarily to leisure travelers looking for competitive pricing. 
• Point of interest (POI) databases contain restaurants, hotels, museums, and attractions, and provide the necessary data for location-based services and recommendation engines for travel applications. 
• Weather APIs from vendors such as OpenWeatherMap or AccuWeather provide travelers with real-time weather conditions and forecasts, assisting travel planners in deciding on travel destinations. 
• Review aggregators and social media platforms associated with TripAdvisor, Google Reviews, Instagram, etc., are crowdsourced customer feedback platforms. 

AI systems can analyze all this data to determine sentiment trends, and travelers can use this to research options, while businesses can manage their online reputation for customers.

Internal data

Your internal, or proprietary information, is another valuable source of information for AI to improve travel experiences and operational efficiency. Here are some of the main sources.

• CRM for the travel industry is the place where you record lead information, booking history, customer profiles, and previous interactions. With the help of this data, AI can identify client segments, develop tailored marketing campaigns, and implement chatbots for automated customer support.  
• Individual passenger itineraries, travel information, booking details, and preferences are all contained in the PNR (Passenger Name Record). In order to predict demand, optimize resource allocation, and offer tailored recommendations for upcoming travels while identifying fraud, generative AI travel tools examine these booking patterns. 
• Loyalty programs are another feed for gen AI, as it monitors members' past purchases, redemption trends, levels of engagement, and spending patterns. AI uses demand-driven dynamic pricing, identifies at-risk customers to encourage engagement, provides personalized rewards, and enhances retention with customized offers.
• Market trends, rival pricing, demand signals, real-time feeds, and performance metrics are all examples of operations data from PMS/OPS systems. Analyzing this data, AI increases operational efficiency, speeds up feature launches, forecasts demand to reduce costs, and optimizes pricing strategies.  

Predictive analytics that foresees consumer demands and market changes, automation through chatbots and virtual assistants, and unified omnichannel experiences are all made possible by data integration with these sources. Effective systems combine structured databases (MongoDB, PostgreSQL) with real-time APIs to provide individualized, context-aware travel planning by integrating models with FAISS for semantic search.

Importance of unifying structured + unstructured data for high-quality outputs

In order to produce really high-quality travel GenAI outputs, there must be a combination of structured and unstructured data. Structured data, which includes flight schedules, hotel ratings, and prices, acts as the factual base-level foundation for an itinerary. But it's the unstructured data, including traveler reviews, blog posts, and social media pictures, obtained through two-way LLM API integration, that provides additional richness, context, and nuance. 

By combining these two types of data, an AI can elevate beyond a basic list and provide travelers with a much more personalized and engaging recommendation. For example, by travel planning-connected generative AI use cases in travel, rather than simply booking a ‘hotel with a 3-star rating', it could help travelers find 'the 3-star rated, best mountain views over the Las Cruces with a vegan breakfast menu'. This combines a more 'human' touch with precise itinerary planning in seconds.

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How does Generative AI work in travel?

Generative AI in travel works by combining data pipelines, embedding and retrieval systems, and large language or multimodal models to produce personalised itineraries, dynamic content (descriptions, images), automated customer support, and decision support using user inputs plus up‑to‑date travel data to generate context-aware suggestions, confirmations, and creative content in real time.

Architecture of AI-driven travel solutions

As you see, there’s a great potential for generative AI in the travel market. However, to grasp the momentum, you need to understand the key elements that it consists of. Several main components make gen AI systems work. Let’s examine each of them and see an example.

• Core data layer: travel schedules, pricing, bookings, destination guides, reviews, and policy data flow into a cleaned, privacy‑controlled data pipeline.
• LLM as the reasoning engine. An LLM processes natural language questions and develops responses that weave together various contextual information in a human-like way in response. For example, when a traveler asks the question “The most fitting time to visit the Philippines”, the LLM reasons through its subsequent relevant recall knowledge and returns a human-like helpful suggestion.
• RAG (Retrieval-Augmented Generation) also adds to factual accuracy through the retrieval of documents and enables this retrieval through LLM reasoning. There is a specific type used in generative AI for travel development called generative RAG. In this scenario, PDF documents, perhaps brochure-based travels, are embedded into vector representations so that the most relevant chunks of the documents are recalled and recast to the LLM for answers that are more grounded in factual data. 
• Vector databases for knowledge retrieval. Vector DBs include embeddings of structured and unstructured content, and semantic search can be very fast using this. This way, the agent can find context-relevant travel bundles or itineraries in larger pools of PDF brochures or in other (online) locations. 
• Orchestration and validation: an orchestration service handles prompts, caching, LLMOps, business rules, safety filters, and post-generation validation before returning content to apps or agents.
• Product endpoints: travel planning apps, CRM, and contact centers consume the validated outputs and collect feedback to close the loop and retrain components.

“We design the stack so data and context travel with each request, embeddings surface the right facts, RAG keeps recommendations current, and model outputs are validated by business rules before they hit a customer. That combination is what makes scalable generative experiences in travel reliable and defensible,” says Orest Falchuk.

The workflow of Gen AI in travel settings

The typical workflow that we establish for our clients is simple but nuanced. Following it helps turn complex travel information into simple, useful recommendations for both travelers and businesses.

• AI in travel starts with travel data and destination information, which are collected and sent through a data pipeline. 
• That information is turned into embeddings and stored in a vector database, so the system can quickly find the most relevant facts later. 
• When a traveler asks a question or needs a plan, the orchestration layer sends the request to the right language model, checks the response with cache and validation steps, and then returns a useful result to the travel planning app. 
• Feedback from the user then goes back into the system, helping improve future responses and making generative AI in travel and hospitality more accurate.

For example, a traveler planning a 3-day trip to Lisbon can get hotel options, local activities, and a day-by-day itinerary generated in seconds, with the system adjusting recommendations based on budget, dates, and preferences.

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How to integrate Gen AI in travel business?

Integrating Generative AI travel technology in travel starts with a clear use case, connecting it to trusted travel data, and then scaling it into workflows with human review and validation. It’s not a one-time decision, but a step-by-step, continuous process consisting of exploration, experimentation, and building it into people's workflows. 

A practical example from our case is building advanced integrations for the travel platform so it can pull from destination content, booking rules, and customer preferences to generate itineraries, answer trip questions, and support sales teams.

Generative AI travel solutions will bring real benefits if you follow these steps: 

• The process starts with determining the end goal. For businesses, this involves identifying the parameters for what AI should and should not do, especially when working with sensitive customer data. The more descriptive the input is, the more accurate and meaningful the output becomes, especially regarding travel planning.
• Once the goal is defined, you must then decide on the intermediary tools. 
  
  • Beginners often find free AI platforms like ChatGPT or Gemini are the most accessible way to begin monetizing their ideas or generating itineraries. These platforms are easy to use and can be used for proof of concept. However, there are restrictions (usage limitations, no guarantee of confidentiality, and limited capabilities). 
  • Once confidence is built, many authors leap to using paid subscriptions that promise more reliable security, greater volume, and the ability to fine-tune outputs to the client's or brand's needs.

• After users are familiar with the basic functionality, AI can progress from a recommendation technology to a workflow partner. The emergence of low-code and no-code technology offers professionals the ability to leverage AI with previously established technology with low-to-no effort. In industries like travel and hospitality, it can lead to better management of reservations, rapid educational customer messaging, and enhanced internal coordination, without needing to be a technologist or developer.
• For organizations that are ready to go even deeper, RAG represents a significant advancement in AI. This is especially important in tourism, where only vetted or accurate info is important. Imagine an AI system that only returns accurate info based on a company's catalog of either travel packages, timely advisories, or the best attractions in town. It means close to no hallucinations and natural, human-like language.
• At the most advanced stage, businesses might decide to spend money on specially designed AI solutions. These models can provide distinct competitive advantages because they are frequently driven by numerous APIs and intricately integrated into pre-existing infrastructures. Businesses benefit from hyper-personalized services, predictive insights, and automation at a scale that standard platforms cannot match.

During this journey, some best practices will always apply. Here are some of the tips that COAX engineers have gathered from our experience.

• The quality of your AI outputs relies on the quality of your inputs — a vague request results in a generic outcome, but an accurate prompt will spark personalization. 
• Verification is required. Make sure an AI recommendation is confirmed by the official tourism website, updated reviews online, and a regulatory agency is important in the recommendation's validity. 
• And the most important thing: generative AI works best when it's used in collaboration with a concept, but not as a substitute. Travelers and businesses alike that recognize AI can provide efficiencies and creativity while maintaining human judgment establish trust and accountability.

Orest Falchuk, notes: “Teams get the best results when they begin with one narrow travel workflow, connect AI to reliable sources, and keep a human in the loop for validation. That’s how AI can actually improve something.”

The best use of generative AI in the travel industry ultimately comes from finding the ideal balance between utilizing technology to speed up planning and decision-making and depending on human knowledge to verify, improve, and apply those insights to the actual world.

7 best travel generative AI solutions to choose from

As we mentioned, ready-made tools give the ability to balance a medium-level investment with convenient functionality. Let’s outline some of the best generative AI solutions for travel companies.

• Trip Planner AI offers a robust trip-planning assistant that develops trip itineraries, books flights, and recommends lodging based on travelers' preferences. It excels at the intersection of user-friendly design and trustworthy recommendations based on real reviews, alleviating overwhelming planning. It’s great as a single hub for organizing trips in an easy, customizable manner.

• Roam Around AI replicates a digital concierge service, developing hyper-personalized suggestions that feel authentic and spontaneous. The chat-based interface has intuitive usability, while the GPT-generated suggestions discover off-the-beaten-path gems and neighborhood favorites. This tool is best to give travelers local experiences over a unique itinerary. 

• Wonderplan AI is comparatively fast and free, and presents collaborative features, making it an excellent app for organizing group packages. The streamlined, speedy solution provides tailored itineraries based on interests, quickly saving time. This generative AI travel solution is great for small travel agencies simply seeking to organize a trip for their clients without getting lost in applying complex customization.

• Mindtrip converts inspiration into action. Users can transform a photo, link, or favorite into a full itinerary. Mindtrip's extensive travel database and collaboration planning tools empower travelers to co-create their trips with an agent while they virtually explore their desired destinations before travel. It ensures helpful and engaging AI-assisted planning and all-in-one booking assistance.

• Otto is designed to assist business travel and syncs directly with calendars and preferences to automate bookings. Otto places users on new itineraries when unpredicted delays, cancellations, and disruptions occur. Otto is best for professionals and small to medium businesses needing personalized, trustworthy, and efficient business travel management.

• Travelin.Ai integrates artificial intelligence with human assistance. They connect travelers with personal assistants who take care of check-in, security, and any special needs. They are particularly valuable for seniors, families, or those who need extra accessibility during the airport journey. 

• Seatfrog is changing train travel by leveraging real-time auctions and instant upgrades to First Class, often for a fraction of the cost of an upgrade. Their flexible "Train Swap" option adds value and convenience, and the dynamic pricing works for passengers and operators. Best for train travelers, especially in the UK, who want to enjoy affordable luxury and flexibility.

These tools make generative AI travel industry applications easier and can be a smart starting point, but they often run into limits when a business needs deeper integrations, stricter data control, or more tailored workflows. Some platforms are great for inspiration and simple trip planning, while others can’t connect to your booking engine, supplier network, or internal systems without heavy customization.

That’s where custom travel software solutions make the difference. At COAX, we help travel companies build secure, connected AI solutions that fit real operations, from itinerary generation and booking support to personalization and supplier data syncing. We also keep data protection front and center, so sensitive customer and transaction information is handled with enterprise-grade security and careful access control throughout the project.

Should you build or buy tools for Gen AI in the travel industry?

In the diverse reality of generative AI in travel, companies are faced with a traditional choice of either building their own, customized solutions or purchasing ready-built platforms. So which option should you decide on?

A custom-built solution will provide complete control over the models and how data is treated, and the ability to tie into existing booking engines or loyalty systems, if necessary. This option is best for larger companies with a strong technical team that prioritizes a differentiated solution, such as a proprietary itinerary generator or a one-of-a-kind chatbot that channels their brand voice. The downside is that there are costly resources, long cycles to develop, and protecting compliance with travel-specific regulations and standards, like GDPR or PCI-DSS for payments.

Buying off-the-shelf GenAI tools provides companies speed to market and requires less capital upfront, while still giving immediate access to diverse capabilities. Many vendors likely already have embedded unique domain travel data and compliance checks, so deployment should integrate more smoothly. The downside to off-the-shelf platforms is that they provide less flexibility, if any at all, and could create dependence on external providers.

A hybrid strategy is the most effective for the majority of mid-sized airlines, travel agencies, or OTAs. It presents purchasing a tested GenAI engine for essential tasks while developing some modules internally to gain a competitive edge.

COAX can help you use the best out of the custom or hybrid approach, and we do it with a deep understanding of your business. We always start by a deep research of your pain points, needs of your customers, data sources you have, and the existing solutions in your ecosystem. We align them with short- and long-term goals and provide Generative AI development services that automate your services, create custom offerings for customers, and enable efficient trip planning and 24/7 support that drives customer satisfaction, more bookings, and revenues, and drives sustainable business growth.

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Bonus: RFP checklist

To understand whether the provider of Gen AI in the travel industry suits your needs or not, you need to ask them several important questions.

We compiled a list of the 14 most critical questions for you:

1. What particular Gen AI applications does your platform have for the travel sector? Does it offer itinerary generation, chatbots, summaries of guest support, translation, dynamic pricing, and predictive analytics?
2. How does your AI provide responses and recommendations? Specifically, what models and algorithms exist behind your AI (proprietary, 3rd party LLMs)? Also, how does the AI get trained, and what data sources help with its outputs?
3. What languages and markets does your solution offer support for? How do you address regional variations, dialects, and culture?
4. Is it compatible with our existing solutions for booking, CRM, property management, and communication systems? Which middleware, data connections, or APIs are necessary for connections?
5. How long does it usually take to implement? What time and resources are needed from our team?
6. How adaptable is the solution to our particular requirements? Is it possible to modify the AI's business rules, tone, and brand voice?
7. What metrics can you provide regarding accuracy and performance? Do you have proof of the accuracy rates of he outputs, and what benchmarks can you provide?
8. If your system doesn’t know the answer, what are the algorithms of its next steps? And what is the process to escalate to a human agent?
9. What are your uptime guarantees? If your service does suffer downtime or degraded performance, what happens? If issues do arise, what, if any, support is offered? 
10. How is customer data captured, stored, and protected? What security measures do you have, including encryption? Do you utilize private LLMs, or do you share data with LLM model providers? 
11. What compliance certifications and/or standards do you meet? GDPR, CCPA, PCI-DSS, SOC 2, ISO 27001, etc. How will you assist with maintaining them? 
12. Who holds ownership of the data, and what rights do we have regarding our data? 
13. What model is used, subscription fee, usage fee, per transaction fee, or hybrid fee? What are the total costs of your tool, considering a full setup, training efforts, and any maintenance?
14. What is the model for your solution to generate revenue for my business? Does it support upselling, cross-selling, dynamic pricing, personalized offers, etc.?

To sum up, making the right choice on Generative AI in travel and hospitality is a demanding task. It requires you to get a full overview of the technical capabilities, standards for security and compliance, and an understanding of how it will impact your business.

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FAQ

What barriers in generative AI for travel can you face?

According to Seyfi et al., functional barriers include difficulty of use, perceived low value, and security threats, and psychological barriers consist of brand identity and resistance to change in tradition. Users now have difficulty adopting AI in travel planning because of the problematic use of inaccurate information blended with AI. Customers are predisposed to lean towards human conversations rather than interacting with technology, where traditional services have created substantial hesitation in adoption.

Are there generational differences for generative AI in travel and hospitality?

As Seyfi et al. state, there are significant generational differences in GAI acceptance, with Gen Z emphasizing usability and Gen X concentrating more on security and privacy issues. When it comes to younger generations recommending GAI, trust is especially important. Challenges vary by age group, and adoption patterns are influenced differently by perceived risks, usability, and trust.

Is Gen AI in the travel industry beneficial for small businesses?

Yes, generative AI provides a great advantage for small travel businesses:

• Reduces the need for customer service staff
• Automates booking, check-ins, data analysis, and operational workflows
• Generates personalized travel itineraries based on individual preferences
• Data analytics optimize dynamic pricing
• Predicts demands and utilizes inventory more efficiently
• Projects innovation and differentiation in a crowded marketplace
• Provides personalization and efficiency comparable to larger competitors

What are other generative AI use cases in travel other than travel planning, localization, predictive analytics, and customer support?

Yes, there are numerous other ones:

• Detecting and preventing fraud
• Expense management system automation
• Optimize travel routes and times 
• Virtual tour platforms
• Health and safety recommendations
• Sustainable tourism recommendations
• Best practices of cultural etiquette
• Create marketing content for destinations

How does COAX develop secure generative AI solutions for travel? 

COAX is ISO/IEC 27001:2022 certified for a complete security management framework, risk analysis, and monitoring. We are also certified under ISO 9001 for quality processes. Specific to travel, we provide encrypted protection of booking data, payment processing compliant with PCI-DSS, processing of personal data compliant with GDPR regulations, real-time fraud detection algorithms, and secure API integrations with travel providers.