However, this convenience raises an important question about how travel algorithms operate: “If AI only learns from what you already like, does it run the risk of creating a ‘filter bubble’ for your vacations?”
In social media platforms, recommendation engines operate on historical engagement data, showing users more of what they have previously clicked, watched, or liked. In machine learning terms, this relies heavily on ‘exploitation’ (leveraging known historical preferences to maximise immediate satisfaction). Computer science literature refers to the outcome of this process as over-specialisation.
A systematic review on recommender systems published in the Journal of Computer Science and Technology (Springer) notes that prioritising pure prediction accuracy inevitably traps users in predictable loops. To counter this, computer scientists measure algorithm quality not just by accuracy, but by “serendipity”, a metric that explicitly balances relevance with unexpectedness.
In practice, algorithms often manage this using what engineers call ε-greedy policies. Instead of basing 100% of an itinerary on a user’s past clicks, the system reserves a calibrated percentage strictly for “exploration”—an intentional injection of randomness that allows the system to test novel choices without completely abandoning core preferences. If travel platforms fail to build in this balance, they risk creating a travel echo chamber where a user who books a quiet beach resort once might be nudged toward identical quiet beaches indefinitely, filtering out unexpected mountain treks or vibrant cultural festivals they never knew they wanted.
The fundamental tension in AI-driven travel comes down to balancing this ‘exploitation’ with ‘exploration’ (injecting controlled randomness or deliberate novelty so the itinerary leaves room for serendipity).
How Travel Platforms Are Engine-ing for Serendipity
Online Travel Agencies (OTAs) argue that travel decision-making requires a different algorithmic framework than social media feed scrolling. Rikant Pittie, CEO & Co-founder of EaseMyTrip, emphasises that personalisation should expand consumer options rather than narrow them.
“Personalisation should never become a filter that limits discovery. While past preferences help make recommendations more relevant, travel is fundamentally about exploration. AI should balance familiarity with inspiration by introducing seasonal destinations, emerging experiences, and alternative itineraries that users may not have actively searched for. The role of AI is not only to predict preferences but also to expand them,” said Pittie.
Pittie notes that diversity and exploration must be explicitly programmed into recommendations: “The most valuable travel experiences often come from discovering places that weren’t part of the original plan. AI should be designed to surface a mix of popular destinations alongside lesser-known alternatives based on factors such as seasonality, traveller interests, and evolving trends. That approach makes recommendations feel more dynamic while encouraging travellers to explore beyond conventional choices.”
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The way consumers interact with AI during travel planning is also shifting. Rather than viewing AI as a static search bar, travellers are using conversational LLMs (Large Language Models) to iterate on plans and adjust mid-trip.
Ahmer Khan, Senior Director of Marketing at Agoda, points out that according to Agoda’s 2026 Travel Outlook Report, 68% of Indian travellers are likely to use AI for travel planning.
“According to Agoda’s 2026 Travel Outlook Report, 68% of Indian travellers said they are likely to use AI for travel planning, and we are seeing this play out earlier in the journey, with travellers using AI not just to book, but to explore where to go, compare options and shape fuller itineraries. At Agoda, personalisation can be informed by what customers have searched for, booked, and engaged with previously, but the goal is not simply to repeat what a traveller has done before,” Khan said.
Khan notes that preventing algorithmic predictability requires striking a balance between efficiency and spontaneous real-time updates: “AI done right makes travel planning more relevant while still leaving room for discovery. This balance is important because travel is not purely a functional decision. Efficiency matters: travellers want help narrowing choices, comparing prices and reducing planning friction. But if recommendations become too narrow, they risk taking some of the excitement out of travel.”
“We are also seeing travellers expect more dynamic support as trips unfold. AI’s value is not limited to what it recommends upfront; it can also adapt to changing context, such as weather, timing, location or disruptions. That might mean suggesting an activity better suited to the day, highlighting a local dining option, or helping a traveller adjust plans quickly. Used well, AI can make travel planning more personalised without making it predictable,” Khan added.
How to Prompt AI for Serendipity: A Guide for Travellers
While travel platforms build diversity into their recommendation systems, travellers using conversational AI tools can actively program room for surprise into their prompts:
Set an Explicit “Variance” Ratio: Instead of asking for a standard itinerary, specify an intentional split. For example: “Build a 4-day Tokyo itinerary. Allocate 70% to exploring cafes and art galleries, but reserve 30% for neighbourhood activities I haven’t mentioned that a local would enjoy.”
Use Real-Time Context Over Static Profiles: Ask AI for mid-trip suggestions based on immediate surroundings rather than past habits. For example: “I am standing in Downtown Florence, and I have 2 hours before dinner. Give me an offbeat spot within walking distance.”
Prompt for Counter-Preferences: Force the algorithm out of its echo chamber. For example: “Based on my preference for nature trips, suggest one high-energy cultural activity or local festival in this city that I wouldn’t normally pick, but might appreciate.”
Ultimately, AI travel tools function best when treated as collaborative sounding boards rather than rigid decision-makers. By combining predictive personalisation with intentional prompt variation, travellers can retain the speed of automation without losing the unplanned moments that make travel memorable.
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