By: M S Akshaya, Senior Specialist: Consulting: Travel, Tourism & Hospitality

Travel and AI

Gone are the days when travelers relied solely on guidebooks and travel agents to plan their perfect getaways. In today’s fast-paced, digitally driven world, travelers seek more personalized and unique experiences, tailored specifically to their tastes and preferences. Towards this, AI has revolutionized the travel hospitality industry – from personalized destination suggestions to tailored accommodation options, AI is at the forefront of reshaping how travelers plan and experience their journeys. The integration of AI into the travel sector not only enhances customer satisfaction but also propels businesses towards efficiency and innovation.

Industry facts and figures

The travel industry’s adoption of AI is on a steep upward trend. According to a report by McKinsey, companies that leverage AI can increase profitability by up to 25%. The same report indicates that AI-driven personalization can boost customer satisfaction rates by 10% to 30%.¹ Moreover, a survey conducted by Travelport revealed that 77% of travel companies are already using AI to some extent to enhance customer experiences.²

AI’s impact on customer experience is undeniable. Personalized travel recommendations, powered by AI, have transformed mundane travel searches into dynamic and engaging interactions. For instance, AI algorithms can analyze vast datasets, including past travel behavior, preferences, and even social media activity, to predict and suggest destinations, accommodations, and activities that align perfectly with a traveler’s unique profile.

But let us first understand what we mean by AI and its recommendations.

1. AI encompasses a broader set of technologies that simulate human intelligence. It includes machine learning, natural language processing, and other techniques. AI can handle complex tasks beyond recommendations, such as predictive analytics, chatbots, and decision-making. Example: An AI-powered chatbot assists travelers by answering queries, suggesting personalized itineraries, and handling booking changes.
2. On the other end, recommendation engines are a specific subset of AI. They focus on suggesting relevant items based on user preferences and historical data. These engines analyze patterns, user behavior, and content to provide personalized recommendations. Example: A hotel booking website uses a recommendation engine to suggest hotels based on a traveler’s past preferences, location, and budget.

In summary, AI encompasses a broader range of capabilities, while recommendation engines specialize in personalized suggestions.

Placing customer behavior at the forefront

AI recommendation engines excel at understanding and segmenting customer behavior. By analyzing data such as browsing history, booking patterns, and even demographic information, these engines can create detailed user profiles. Here’s how they do it:

1. Behavioral Analysis: AI algorithms track users’ interactions with websites and apps, identifying patterns and preferences. For example, if a user frequently searches for beach destinations, the algorithm will prioritize similar locations in future recommendations.
2. Preference Learning: Using machine learning techniques, AI systems learn a traveler’s likes and dislikes over time. This continuous learning process ensures that recommendations evolve with the user’s changing tastes.
3. Travel History: By examining past trips and bookings, AI models can predict the type of experiences a traveler might enjoy in the future. This historical data is invaluable in delivering contextually relevant suggestions.
4. Segmenting Customers: AI can segment users into different categories based on their behavior, preferences, and demographics. This segmentation allows for more targeted and customized recommendations, enhancing the overall travel experience.

Choosing the right AI model

AI-driven recommendation engines are highly effective due to their ability to process and analyze large volumes of data quickly. They can adapt to changing preferences and provide recommendations that are both personalized and timely. Various AI models are employed within the travel industry to deliver personalized recommendations.

1. Collaborative Filtering: This model leverages data from multiple users to recommend destinations or activities based on shared preferences. For instance, if travelers with similar interests frequently visit a particular destination, the algorithm will suggest it to new users with matching profiles.
2. Content-based Filtering: This model focuses on the traveler’s preferences and history. For example, if a traveler often books luxury hotels, the algorithm will prioritize similar high-end accommodation options in future searches.
3. Hybrid Models: Hybrid models combine the strengths of collaborative and content-based filtering to deliver more accurate and diverse recommendations.
4. Contextual Bandits: These models consider real-time data, such as current location, time of day, and weather conditions, to provide timely and relevant suggestions. They are also called multi-world testing, associative bandits, learning with partial feedback, learning or multi-class classification with bandit feedback, bandits with side information, associative reinforcement learning, and one-step reinforcement learning.

Navigating AI risks

While AI is proving to be a must-have in the travel industry, the landscape is starting to formalize. With legal and ethical guardrails beginning to form, it is imperative to understand the risks of AI in the industry. Here are some areas of risk of AI and ways to navigate them.

1. Data products: For AI to become effective and properly integrated into business, it is imperative to ensure data creation, collection, convergence, and integration into downstream applications that drive intelligence, decision-making, and automation.
2. Data Privacy and Security: AI systems require vast amounts of customer data to function effectively, increasing vulnerability to data breaches and potential misuse of personal information. AI systems are increasingly becoming targets for cyberattacks, which could lead to system-wide failures or manipulated outcomes.
3. Algorithmic Bias: AI systems may inadvertently perpetuate or amplify existing biases, leading to unfair treatment or discrimination against certain traveler groups. AI systems could also make errors or provide inaccurate recommendations.
4. Over-reliance on Technology: Excessive dependence on AI systems for critical operations could lead to significant disruptions, especially when they are exposed to outages, security breaches, or undetectable changes.
5. Job Displacement and Lack of Human Touch: Automating roles traditionally performed by humans could lead to potential job losses in the industry, particularly in customer service. Overusing AI in customer interactions could also lead to losing personalized, empathetic service that many travelers value.
6. Transparency Issues: AI systems become ‘Black Boxes’ with opaque decisions that make it difficult to explain AI-driven decisions to customers or regulators.
7. Ethical Considerations: AI decisions may be legally compliant but ethically questionable, potentially damaging brand reputation and customer trust.
8. Cultural Insensitivity: AI may fail to understand cultural nuances in global travel, offending customers or providing inappropriate recommendations.

We have evolved to understand that customer satisfaction is the core of every business, and AI can drive it effectively. The best way to be successful in this area is to work around the risks involved. Here are some simple yet powerful ways to do so.

1. Prioritize ethical AI development and use
2. Invest in robust data protection and cybersecurity measures
3. Maintain human oversight and intervention capabilities
4. Regularly audit and test AI systems for bias, accuracy, and reliability
5. Stay informed about regulatory developments and ensure compliance
6. Foster a culture of transparency in AI use and decision-making

Conclusion

As the travel industry continues to evolve, the integration of AI offers exciting opportunities to enhance personalized experiences. Today’s travelers are more tech-savvy than ever, seeking unique, tailored journeys that cater to their individual tastes. AI’s ability to analyze vast amounts of data and deliver precise recommendations positions it as a crucial tool in meeting these demands.

But, as AI technology advances, a thought-provoking question arises: Can it continually adapt to meet travelers’ evolving needs and unsaid requirements, ensuring that each recommendation is as personalized and accurate as possible, in a secure and ethical manner? Succinctly put, the future of travel rests on AI’s ability to keep pace with the dynamic preferences of the modern traveler.

References

1. The promise of travel in the age of AI, September 27, 2023: https://www.mckinsey.com/industries/travel/our-insights/the-promise-of-travel-in-the-age-of-ai
2. The state of modern retailing report 2025, Travel’s tipping point: https://www.travelport.com/travel-trends-2025-report
3. DTC, Difficult to Compare, 2024: https://www.travelport.com/difficult-to-compare