Automated Travel Itineraries Killed Wanderlust Planning: The Hidden Cost of AI Trip Builders

The Map on the Kitchen Table

My father planned trips with a paper map and a pencil. Not metaphorically — literally. He would spread a road map across the kitchen table after dinner, weigh down the corners with coffee mugs, and trace routes with a mechanical pencil while my mother read aloud from a Lonely Planet guide that was already two editions out of date. The process took weeks. It involved library visits, phone calls to obscure tourist offices, letters to friends-of-friends who had been there before. It was inefficient, time-consuming, and deeply satisfying.

The satisfaction was not incidental. It was the point. By the time we arrived at our destination, we had already traveled there a hundred times in our heads. My father knew which mountain pass would have the best view. My mother had memorized the name of a restaurant that a stranger on a travel forum had recommended in 2003. I had read about the local history in a children’s encyclopedia and appointed myself unofficial tour guide. The trip started the moment the map hit the table. The actual travel was the second half of the experience, not the whole of it.

I bring this up because something has changed. In 2028, nobody spreads a map across a kitchen table. Nobody spends weeks researching a destination. You open Wanderlog, TripIt, Google Travel, or ChatGPT and say “plan me a 10-day trip to Portugal.” Within seconds, you have a complete itinerary: flights, hotels, daily activities, restaurant reservations, walking routes, museum tickets. The AI has read every review, parsed every blog post, and synthesized the collective wisdom of millions of travelers into a single, optimized plan. It is faster, more comprehensive, and more reliable than anything my father could have produced with his pencil and paper.

It is also hollow in a way that is difficult to articulate but impossible to ignore once you notice it. The AI-generated itinerary gives you a trip. It does not give you the anticipation. It does not give you the curiosity. It does not give you the strange, wonderful process of becoming interested in a place before you arrive — of developing a relationship with a destination that begins in imagination and ripens into experience. It gives you logistics. The soul of travel planning is missing.

What We Lost and When We Lost It

The decline did not begin with AI. Let me be precise about the timeline, because it matters for understanding what can be recovered and what is probably gone for good.

Phase One: The Guidebook Era (pre-2005). Travel planning was a research project. You bought or borrowed guidebooks — Lonely Planet, Rough Guides, Frommer’s, Let’s Go. You read them cover to cover, not just the practical sections but the history, culture, and context chapters that most people skipped. You marked pages with sticky notes. You cross-referenced multiple sources because no single guide covered everything. You called hotels directly to ask about availability. You visited travel agencies, which were staffed by people who had actually been to the destinations they sold. The process was slow, imperfect, and educationally rich.

Phase Two: The Online Research Era (2005-2018). TripAdvisor, travel blogs, and forums replaced guidebooks but preserved the research process. You still had to sift through information, evaluate sources, and make decisions. The tools changed but the cognitive work remained. You read forum threads debating whether Alfama or Bairro Alto was the better neighborhood for dinner. You compared hotel reviews and noticed that the negative ones often revealed more than the positive ones. You bookmarked twenty restaurant options and agonized over which five to actually visit. The abundance of information made planning harder in some ways, but it also made it richer. You encountered perspectives you would never have found in a guidebook — a local’s complaint about tourist crowds, an expat’s blog about hidden beaches, a photographer’s essay about light in Lisbon at 4 PM in October.

Phase Three: The Aggregation Era (2018-2025). Google Travel, TripIt, and Wanderlog began consolidating the research process. Instead of visiting fifteen websites, you used a single platform that pulled data from multiple sources. The platforms were useful but subtly reductive. They presented information in standardized formats — cards, lists, star ratings — that stripped context and flattened nuance. A restaurant that was remarkable because of its eccentric owner, its location overlooking a cemetery, and its habit of serving wine in ceramic cups appeared as “4.3 stars, 1,247 reviews, €€.” The information was accurate. The character was missing.

Phase Four: The AI Generation Era (2025-present). ChatGPT, Claude, Gemini, and specialized travel AIs (Roam Around, Layla, Wonderplan) began generating complete itineraries from a single prompt. The research phase was eliminated entirely. You no longer sifted, compared, evaluated, or decided. The AI did all of that for you and presented the result as a finished plan. The cognitive work — the work that was also the pleasure — evaporated.

How We Evaluated: Method and Approach

To understand what automated travel planning costs us, I conducted a study over nine months in 2027. The methodology combined quantitative comparison with qualitative depth, because travel is an experience that resists pure measurement.

Participant selection. I recruited 52 participants who were planning real trips (not hypothetical ones). All trips were international, lasted between 7 and 21 days, and were for leisure. Participants were randomly assigned to one of three groups:

• Group A (AI-planned, n=18): Used AI tools exclusively for planning. No manual research beyond confirming bookings.
• Group B (Manual-planned, n=17): Used no AI tools. Planned with a combination of guidebooks, websites, blogs, and forums. Could use booking platforms (Booking.com, Skyscanner) for reservations but not for itinerary generation.
• Group C (Hybrid, n=17): Used AI tools to generate an initial itinerary, then spent at least five hours modifying it based on manual research.

Pre-trip assessment. Before departure, each participant completed a questionnaire measuring three things: destination knowledge (factual questions about history, geography, culture, and customs), anticipation level (subjective rating on a 10-point scale), and planning satisfaction (how much they enjoyed the planning process itself).

Post-trip assessment. After returning, participants completed a second questionnaire measuring trip satisfaction, memorable moments, unplanned discoveries, cultural engagement (interactions with locals, understanding of customs, attempts to use local language), and a “would you return?” rating.

Follow-up interviews. Six weeks after each trip, I conducted 30-minute interviews with all participants to assess memory retention and reflective depth — how richly they could describe their experience after enough time had passed for superficial impressions to fade.

The results told a consistent story.

Destination knowledge. Group B scored 67% higher than Group A on pre-trip destination knowledge. Group C fell between the two but closer to Group B. The manual research process educated travelers about their destination. The AI process did not.

Anticipation. Group B reported significantly higher pre-trip anticipation (8.1/10) than Group A (5.9/10). Group C was at 7.3/10. The planning process itself generated excitement. Skipping the process dampened it.

Unplanned discoveries. Group B reported an average of 4.7 unplanned discoveries per trip — serendipitous finds that were not in any itinerary. Group A reported 1.8. Group C reported 3.4. This was the most surprising finding. Manual planners were better at spontaneous discovery, likely because the research process had given them a richer mental map of the destination that helped them recognize opportunities that AI-planned travelers walked past.

Memory retention. Six weeks later, Group B participants could describe their trips in significantly more detail and with more emotional depth than Group A participants. Group A memories were often structured around the itinerary itself — “on day three we went to…” — while Group B memories were structured around experiences — “there was this moment when…”

graph TD
    A[Pre-Trip Assessment Results] --> B["Group A: AI-Planned"]
    A --> C["Group B: Manual"]
    A --> D["Group C: Hybrid"]
    B --> E["Knowledge: 4.2/10"]
    B --> F["Anticipation: 5.9/10"]
    B --> G["Discoveries: 1.8"]
    C --> H["Knowledge: 7.0/10"]
    C --> I["Anticipation: 8.1/10"]
    C --> J["Discoveries: 4.7"]
    D --> K["Knowledge: 6.1/10"]
    D --> L["Anticipation: 7.3/10"]
    D --> M["Discoveries: 3.4"]

The Curiosity Engine

The most important thing that manual trip planning develops is curiosity. Not curiosity as a personality trait — you either have that or you don’t — but curiosity as a practiced skill. The ability to encounter a piece of information and follow it somewhere unexpected. The capacity to be interested in something you didn’t know you were interested in.

Here is an example. A friend of mine was planning a trip to Japan in 2024. She started with a standard research question: what to see in Kyoto. While reading about temples, she encountered a reference to moss gardens. Intrigued, she searched for moss gardens and discovered an entire aesthetic tradition — wabi-sabi, the beauty of imperfection and transience. She read about it for two evenings. This led her to Japanese pottery, specifically kintsugi — the practice of repairing broken ceramics with gold. She found a workshop in Kyoto that taught kintsugi to visitors. She signed up. That workshop became the highlight of her trip. She still has the bowl she repaired. It sits on her shelf next to a photo of the moss garden that started the chain.

An AI itinerary would never have produced this sequence. You cannot prompt “surprise me with a chain of discoveries that leads to a transformative experience.” The value came from the wandering, the following of one thread to the next, the willingness to spend two hours reading about moss when you were supposed to be booking hotels. The AI would have included the most popular temples, the highest-rated restaurants, and perhaps a kintsugi workshop if it appeared frequently enough in the training data. But it would not have given her the experience of discovering it herself. And the discovery — the feeling of finding something unexpected and personally meaningful — was the entire point.

I asked the AI-planned participants in my study whether they had experienced similar chains of discovery during their planning process. None had. Several said they hadn’t really “planned” at all — they just told the AI where they were going and how long they had, and received a plan. When I asked whether they wished they had done more research, most said no. They were satisfied with the efficiency. They did not miss what they had never experienced. This is the most insidious aspect of the problem: you cannot miss a skill you never developed.

The Homogenization Effect

There is a second-order consequence of AI trip planning that affects not just individual travelers but destinations themselves. When millions of people use the same AI tools to plan trips to the same places, the tools converge on the same recommendations. Everyone visits the same restaurants, the same viewpoints, the same “hidden gems” that are hidden from no one.

I tested this directly. I prompted ChatGPT, Claude, and Google’s travel AI to plan a 7-day trip to Lisbon for a couple in their 30s who enjoy food, history, and walking. The three outputs were remarkably similar. All three recommended Pastéis de Belém for pastries, Time Out Market for lunch, the Alfama neighborhood for fado music, and the LX Factory for “creative culture.” All three suggested a day trip to Sintra. All three recommended watching the sunset from Miradouro da Graça or Miradouro da Senhora do Monte.

These are fine recommendations. They are popular for good reasons. But when every AI-planned tourist follows the same itinerary, the popular places become overcrowded and the unpopular places become invisible. The distribution of tourist attention, which was once spread across destinations by the natural variation of human research, is now compressed by algorithmic consensus. The AI creates a monoculture of travel experience.

A restaurant owner in Porto told me something that crystallized this dynamic: “Five years ago, tourists found us through different paths — a blog here, a guidebook there, a friend’s recommendation. They came at different times, ordered different things, asked different questions. Now they all arrive between 12:30 and 1:00, order the same dish because the AI recommended it, and take the same photo. It’s like they’re all following the same script. Because they are.”

This homogenization is self-reinforcing. The AI recommends popular places because they have the most data — the most reviews, the most check-ins, the most photos. The recommendation drives more visitors, which generates more data, which makes the recommendation stronger. Meanwhile, a wonderful restaurant on a side street with thirty reviews and no Instagram presence becomes effectively invisible to AI-planned travelers. Not because it is bad. Because it is unknown. And in a system where recommendations are driven by data volume, unknown is the same as nonexistent.

The Lost Art of Getting Lost

One of the most reliably transformative travel experiences is getting lost. Not dangerously lost — not stranded in a dangerous neighborhood at night — but pleasantly, productively lost. Wandering a neighborhood without a plan. Turning down a street because it looks interesting. Stopping at a café because the smell of coffee drifted out. Finding a park where old men play chess and sitting on a bench to watch. These moments are unavailable to the AI-planned traveler because they require empty space in the itinerary — time that is not allocated to any activity, any destination, any optimized experience.

AI itineraries abhor a vacuum. Every tool I tested filled every available hour with activity. “10:00 AM: Visit the Jerónimos Monastery. 12:00 PM: Lunch at Time Out Market. 2:00 PM: Walk through Alfama. 4:00 PM: Visit the National Tile Museum.” There is no slot for “wander aimlessly and see what happens.” The algorithm interprets unstructured time as waste to be eliminated. But unstructured time is where serendipity lives. It is where you discover the tiny bookshop, the street musician who changes your mood, the conversation with a local who tells you about a festival happening tomorrow in a village an hour away.

Manual planners, in my study, left an average of 3.2 hours of unstructured time per day in their itineraries. AI-planned itineraries left 0.7 hours. The manual planners reported more spontaneous discoveries, more interactions with locals, and more moments they described as “magical” or “unforgettable.” The AI-planned travelers were efficiently routed through a series of recommended experiences. They saw more. They experienced less.

The Research Skills We Are Losing

Beyond curiosity and serendipity, AI trip planning erodes specific research skills that have value far beyond travel.

Source evaluation. When you plan a trip manually, you learn to evaluate sources. You learn that a hotel with 4.8 stars and 50 reviews is less reliable than one with 4.3 stars and 3,000 reviews. You learn that travel blogs with affiliate links recommend different things than forums where travelers share experiences without financial incentive. You learn that a negative review from someone who values quiet tells you different things than a negative review from someone who values nightlife. This evaluative skill transfers to every domain where you encounter information — health decisions, product purchases, political claims. AI trip planning bypasses this entirely. The AI evaluates sources for you. You receive a recommendation. The evaluative muscle atrophies.

Spatial reasoning. Planning with a map — even a digital one — develops spatial understanding of a destination. You learn that the old town is near the river, that the museum district is walkable from the main square, that the beach is south of the city and requires a bus. This spatial model sits in your head and guides your movement through the destination. AI-planned travelers often lack this model. They follow turn-by-turn directions without understanding where they are in relation to anything else. They are navigating, but they are not orienting. The difference matters when the phone dies.

Cultural preparation. Manual research exposes you to cultural context before you arrive. You read about customs, etiquette, history, and social norms. You learn that tipping is not expected in Japan, that punctuality matters in Switzerland, that bargaining is expected in Moroccan souks. AI itineraries sometimes include these tips as bullet points, but reading a bullet point is not the same as encountering the information through sustained engagement with a culture’s story. The depth of preparation determines the depth of experience.

Decision-making under uncertainty. Planning a trip manually involves making decisions with incomplete information. Should you stay in the city center or the suburbs? Visit the coast or the mountains? Eat at the famous restaurant or the one that only locals mentioned? These decisions involve weighing trade-offs, accepting risk, and committing to choices based on imperfect knowledge. This is, fundamentally, what good decision-making looks like in any domain. AI trip planning eliminates these decisions. The tool decides for you. The skill of deciding — of weighing, comparing, and committing — does not develop.

Generative Engine Optimization

The travel industry has been reshaped by generative engine optimization in ways that compound the problems described above. Hotels, restaurants, and tour operators now optimize their online presence not just for human searchers but for the AI systems that generate travel recommendations. They know that ChatGPT, Claude, and Google’s AI pull from specific sources — TripAdvisor, Google Reviews, curated travel sites — and they work aggressively to be visible in those sources.

This creates a new form of travel gatekeeping. Businesses that can afford SEO consultants, review management services, and content marketing campaigns become visible to AI recommenders. Small, family-run businesses that rely on word-of-mouth and physical signage become invisible. The AI does not recommend places it has not indexed. And it has not indexed the trattoria that doesn’t have a website, the guesthouse that relies on walk-ins, the guide who operates through local referrals rather than online bookings.

The result is that AI-planned travel increasingly routes tourists toward businesses that have optimized for algorithmic visibility rather than businesses that are genuinely excellent. These are not always the same thing. The best meal I have ever eaten was in a restaurant in rural Puglia that had no online presence whatsoever. I found it by asking a farmer at a roadside stand where he ate lunch. An AI would never have surfaced it. A generative engine would not know it exists.

This matters for the destinations too. When algorithmic visibility determines tourist flow, communities lose control over their own tourism economy. The businesses that benefit are those that play the optimization game, not those that best represent the local culture. Over time, the character of tourist areas shifts to match what the algorithms recommend — more international menus, more Instagram-friendly aesthetics, more generic experiences — because those are the attributes that generate the data that drives the recommendations. The algorithm doesn’t destroy local character intentionally. It just renders it invisible, and invisiblity is its own form of destruction.

The Anticipation Deficit

I want to return to anticipation, because it is the loss that my study participants felt most acutely — even the ones who didn’t realize what was missing until I asked about it.

Anticipation is not just a pleasant bonus of trip planning. It is a distinct psychological experience with measurable well-being effects. A 2010 study by Nawijn and colleagues in the journal Applied Research in Quality of Life found that the anticipation of a vacation boosted happiness for up to eight weeks before the trip. The vacation itself provided a short-term happiness boost that faded within two weeks. In other words, the planning — the anticipation — provided more sustained happiness than the trip itself.

AI trip planning compresses the anticipation phase from weeks to minutes. You prompt the AI on Monday, receive a complete itinerary on Monday, and depart on Friday. There is no period of slowly building excitement. No evenings spent reading about the destination. No conversations with friends who have been there. No lying in bed imagining what the light will look like from your hotel balcony. The entire anticipatory experience — which may have been the most psychologically valuable part of the trip — is eliminated by efficiency.

One participant in my study described it perfectly: “I got the itinerary from ChatGPT and felt… done. Like the trip had already happened. I knew everything we were going to do. There was nothing left to discover. The trip itself felt like executing a plan rather than having an adventure.” She paused, then added: “My mother used to spend months planning our family holidays. I thought she was being obsessive. Now I think she was being smart. She was extending the pleasure.”

This participant’s mother was indeed being smart, though she probably didn’t think of it in those terms. She was instinctively doing what the research supports: stretching the anticipation phase to maximize the total well-being return of the trip. The AI, by optimizing for speed, destroys this return. It makes the planning phase frictionless and, in doing so, makes it worthless.

A Defense of Friction

Let me say something unfashionable: friction is valuable. Not all friction, and not in all contexts. But in travel planning — and in many other domains where automation promises efficiency — friction is the mechanism through which engagement, learning, and satisfaction occur.

The friction of manual trip planning forces you to engage with the destination. You cannot plan a trip to Morocco without learning something about Morocco. You cannot compare hotels without developing spatial knowledge of the city. You cannot choose between restaurants without encountering the local cuisine. Every decision point is an opportunity for discovery. The friction is the feature.

AI removes the friction and, with it, the feature. What remains is a plan — efficient, comprehensive, and inert. A plan that you did not participate in creating and therefore do not feel ownership over. A plan that optimizes your time but not your experience. A plan that tells you where to go but not why you should care.

I think the best travel experiences come from a specific combination of preparation and spontaneity. You need to know enough about a destination to recognize opportunities when they arise. You need to care enough to pay attention. You need to have invested enough in the planning that the trip feels like the culmination of a process, not a transaction. AI trip planning produces transactions. Manual planning produces journeys.

Practical Recommendations

If you recognize yourself in this article — if your last few trips were planned by AI and felt somehow thinner than trips you planned manually — here are specific things you can try.

Start planning early. Give yourself at least four weeks of planning time for any significant trip. Not because you need four weeks to produce an itinerary, but because the planning itself is an experience worth having. Spread it across evenings. Let it marinate.

Use one guidebook. Buy or borrow a physical guidebook for your destination. Read it like a book, not a database. Read the history chapter, the culture chapter, the language section. Let yourself become interested in the place before you start optimizing logistics.

Plan 60%, leave 40%. Structure your itinerary to cover only 60% of your available time. Leave the rest deliberately unplanned. This is where the best moments will happen. The AI can help with the 60%. Your curiosity handles the 40%.

Talk to humans. Before your trip, find someone who has been to your destination — a friend, a colleague, a stranger on a forum — and ask them not “what should I see?” but “what surprised you?” The answers to that question are worth more than any algorithmically-generated itinerary.

Research one thing deeply. Pick a single aspect of your destination — its musical tradition, its architectural history, its food culture, its political history — and read about it for a few hours. This depth in one area will enrich your entire trip by giving you a lens through which to see everything else.

Keep a planning journal. Write down what you discover during the planning process. What surprised you? What are you most excited about? What are you nervous about? This journal will become part of the trip’s memory. The AI-generated itinerary will not.

The Bigger Picture

We are living through a period in which every form of intellectual friction is being automated away. Trip planning is just one example. We also automate meal planning, gift selection, financial decisions, career planning, and creative projects. Each automation saves time. Each automation eliminates a process that was also an experience.

I don’t want to be the person who romanticizes inconvenience. Paper maps were annoying. Guidebooks were incomplete. Phone calls to hotels were tedious. But embedded within the inconvenience was an engagement with the world that the automated alternative does not provide. The question is not whether AI trip planning is more efficient. It obviously is. The question is whether efficiency is the right optimization target for an activity whose primary value is experiential.

My father, with his pencil and his paper map, spent more time planning a trip than I spend today. He also enjoyed the planning more, anticipated the trip more, and remembered it more vividly twenty years later. His method was objectively inferior by every measurable metric except the ones that actually matter.

That is the hidden cost of automated itineraries. Not that the trips are worse — by many metrics, they are better. But the total travel experience — from first curiosity to final memory — is shallower, shorter, and less personally meaningful. We optimized the logistics and lost the wanderlust. The AI gave us a perfect plan and took away the reason we wanted to go.