I Spent 20 Hours Planning With AI and Still Ruined My Family's Spain Vacation

The Trip That Should Have Been Perfect

Last summer, my family and I planned a two-week dream vacation to Spain. We'd been talking about it for years—the kids were finally old enough to appreciate European history, we had the budget saved, and I had time off work. This was going to be the trip of a lifetime.

I'm a technical person. I know how to use AI tools. So I did what any engineer would do: I threw ChatGPT, Claude, and Gemini at the problem.

15 hours later, I had a consolidated itinerary that looked beautiful on paper.

Two weeks into the trip, I realized I'd ruined our family vacation.

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What Went Wrong: The AI Planning Failure

Here's what actually happened in Spain:

❌ Alhambra in Granada: Sold Out

The crown jewel of our trip. The reason we were visiting Granada in the first place. Sold out two months in advance. None of the AI tools mentioned this. We skipped Granada entirely and pivoted to Córdoba.

❌ Royal Palace in Madrid: Sold Out

We showed up on our planned day. Tickets sold out days ahead. The kids were disappointed. We spent the afternoon wandering aimlessly instead.

❌ Sagrada Familia Summit in Barcelona: Sold Out

I wanted to show the family Barcelona from above. Summit tickets were gone weeks before. We saw the basilica from ground level, but missed the iconic experience.

❌ Wrong Season Planning

AI told me June was "great weather" for Spain. It didn't tell me that Granada in late June hits 95-102°F—too hot for families with kids to enjoy outdoor sightseeing. Córdoba, where we pivoted? Even hotter. Less interesting. More walking.

❌ 15+ Hours of Manual Consolidation

Three different AI tools gave me three different itineraries. I spent an entire weekend consolidating them, cross-referencing attractions, trying to build something coherent. The AI gave me research. I still had to do all the work.

This wasn't the trip I planned. These weren't the memories I wanted for my family.

And if I—someone who builds AI systems for a living—was struggling this badly, I figured I couldn't be alone.

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Let's Look at a Real Example: Brussels in 2 Days

To show you exactly what I mean, let me share a real AI-generated itinerary. I asked Gemini (Google's AI) to plan a 2-day Brussels trip. Here's what it gave me:

Day 1: "The Historic Heart & Belgian Indulgence"

Morning: Grand Place & Town Hall

"Start at the Grand Place, widely considered one of the most beautiful squares in the world. Admire the gold-leafed guild houses and the towering Gothic Town Hall. If you enjoy surrealism, head nearby to the Magritte Museum."

Afternoon: Chocolate & Shopping

"Stroll through the Royal Galleries of Saint Hubert, a stunning 19th-century glass-roofed arcade. This is the best spot to sample high-end chocolate from houses like Pierre Marcolini or Neuhaus."

Evening: Beer & Mussels

"End your day in the Sablon district for a more upscale vibe, or head to the Delirium Café near the Grand Place, which holds the world record for the most varieties of beer commercially available."

Sounds pretty good, right? Here's what's missing:

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The 8 Critical Failures of Generic AI Itineraries

Let me break down exactly what's wrong with that Brussels itinerary—and why this pattern repeats with ChatGPT, Claude, Gemini, and every other generic AI chatbot:

1. ❌ No Specific Timing

The AI says "Morning" and "Afternoon" and "Evening." But when exactly should I start? How long at each place? The Grand Place is beautiful for 20 minutes—do I really need to spend the entire morning there? When should I leave for the next activity?

Real-world impact: You waste time, miss optimal visiting hours, and either rush through experiences or stand around wondering "what's next?"

2. ❌ No Transport Details

How do I get from Grand Place to Royal Galleries? Walking? Metro? How long does it take? The AI assumes I'll figure it out. On vacation, with tired kids, in a foreign city? Good luck.

Real-world impact: Inefficient routing, wasted time backtracking, frustration trying to navigate unfamiliar transit systems.

3. ❌ No Booking Requirements

Which of these attractions require tickets? Which sell out? Do I need to book the Magritte Museum in advance? What about Delirium Café—do I need a reservation? The AI doesn't know and doesn't tell you.

Real-world impact: Sold-out attractions (like my Alhambra disaster), wasted days, disappointed family members.

4. ❌ Vague, Unusable Suggestions

"Grab a classic Liège waffle from a park van" sounds charming. But which park? What van? Where? When? The AI generates atmosphere, not actionable plans.

Real-world impact: You spend vacation time Googling "where to buy waffles Brussels" instead of enjoying your trip.

5. ❌ No Routing Optimization

The AI lists activities in thematic order, not geographic order. You might zigzag across the city because the AI doesn't understand that Grand Place and Atomium are 30 minutes apart, while Grand Place and Royal Galleries are a 5-minute walk.

Real-world impact: Hours wasted on unnecessary transport, exhausted travelers, fewer actual experiences.

6. ❌ No Budget Information

How much does the Atomium cost? What about Mini-Europe? Is Delirium Café expensive? The AI has no idea whether this day costs €50 or €200.

Real-world impact: Budget shock, overspending, or missing experiences because you thought they were too expensive (when they weren't).

7. ❌ No Weather/Season Awareness

The AI doesn't know that the Atomium has limited indoor space (miserable in rain), or that outdoor markets are best in morning (before afternoon heat). It generates the same itinerary for January and July.

Real-world impact: Uncomfortable experiences, wrong clothing choices, activities that don't match weather reality.

8. ❌ Options Without Guidance

"Option A: Visit the Parlamentarium. Option B: Visit the Horta Museum." Great. But which one should I choose? What if I like both politics AND architecture? How do I decide? The AI gives you choices but no decision framework.

Real-world impact: Decision paralysis, FOMO, or random choices that don't match your actual interests.

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Why Generic AI Chatbots Can't Plan Travel

Here's the uncomfortable truth: ChatGPT, Claude, and Gemini are excellent at answering questions. They're terrible at execution.

These AI tools were built to generate text, not to manage real-world logistics. They don't understand travel the way a human travel agent does. They don't know that:

• Timing matters: The Sagrada Familia at 9 AM is peaceful. At 2 PM it's a zoo.
• Booking windows matter: Some attractions sell out months ahead, others are walk-in.
• Geography matters: Backtracking across a city wastes hours you'll never get back.
• Constraints matter: Your trip has a budget, kids get tired, restaurants close, weather changes.
• Context persists: Yesterday's late night means today should start slower.

Generic AI gives you a snapshot. Travel requires a system.

What Generic AI Cannot Do:

❌ Maintain persistent context — Chat history isn't trip state. You lose continuity between conversations.

❌ Monitor real-world constraints — AI can't tell you attractions are sold out or weather will be terrible.

❌ Optimize routing — AI doesn't minimize backtracking or travel time between locations.

❌ Provide structured execution — Hour-by-hour timing, transport details, booking deadlines.

❌ Adapt to reality — When plans change (and they always do), you're back to prompting.

❌ Track budget — No running total, no cost breakdowns, no budget impact awareness.

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The "Collation Burden" Problem

Even when generic AI chatbots give good research, they put the entire burden of execution on you:

• You cross-reference three different AI outputs.
• You Google each attraction for opening hours and ticket prices.
• You build a geographic route that doesn't zigzag.
• You figure out transport between each location.
• You check booking requirements and weather.
• You consolidate everything into a usable plan.

The AI did the easy part (generating ideas). You're stuck with the hard part (making it work).

And when something goes wrong—like missing the Alhambra—you have no one to blame but yourself.

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What Execution-Aware Planning Looks Like

After my Spain disaster, I knew there had to be a better way. Not just for me, but for everyone who's struggled with the gap between "AI-generated itinerary" and "successful trip."

That's why I built TravWiz—an execution-aware travel planning platform that goes beyond answering questions to actually delivering trips that work.

Here's what execution-aware planning includes:

✅ Specific Hour-by-Hour Timing Not "morning" or "afternoon"—actual times: "9:00 AM - St. Mark's Basilica (2 hours), 11:30 AM - St. Mark's Campanile (45 min)."

✅ Transport Details Between Every Activity "Walk 5 minutes to Rialto Bridge" or "Take Vaporetto Line 1 to Murano (15 min ride)." You never wonder how to get there.

✅ Proactive Booking Intelligence The system flags attractions requiring advance booking, tells you the booking window ("reserve 2-4 weeks ahead"), and provides direct booking links with timing/pricing context.

✅ Routing Optimization Activities are geographically sequenced to minimize backtracking. See everything without zigzagging across the city.

✅ Real-Time Budget Tracking Every activity shows cost. Running total updates as you customize. You know exactly what your trip will cost before booking anything.

✅ Weather & Season Awareness The system recommends best months to visit, flags seasonal considerations, and adapts suggestions based on when you're traveling.

✅ Structured Trip State Your trip is a living object—time, location, budget, routing, bookings all bound together. Change one thing, everything adapts.

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The Difference in Practice

Let me show you what this looks like in reality. Here's the same Brussels Day 1 activity, but execution-aware:

Generic AI Version:

"Start at the Grand Place, widely considered one of the most beautiful squares in the world. Admire the gold-leafed guild houses and the towering Gothic Town Hall."

Execution-Aware Version:

9:00 AM - Grand Place & Town Hall (1 hour) Address: Grote Markt, 1000 Brussels Cost: Free Transport: Walk from hotel (12 min) or Metro lines 1/5 to De Brouckère station Booking: None required Insider Tip: Arrive before 10 AM to photograph without crowds. Town Hall interior tours require advance booking (€8, book 1 week ahead at brussels.be). Next: Walk 4 minutes to Manneken Pis

See the difference? One is atmospheric text. The other is an actionable plan.

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Real Travelers, Real Results

After testing with early users, here's what we're hearing:

"This would've saved my trip. I missed the Vatican Museums because I didn't know they sold out—TravWiz flags this stuff automatically." — Sarah, Rome traveler

"The booking alerts are exactly what I needed. I would've shown up to the Colosseum without tickets and ruined a whole day." — Mike, first-time Europe visitor

"Finally, realistic itineraries. ChatGPT told me to do 8 things in one afternoon. TravWiz actually maps timing and shows me it's impossible." — Jennifer, Paris planner

These aren't edge cases. This is what happens when you build for execution, not just conversation.

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Why This Matters for Your Next Trip

Travel failures are irreversible. You can't retry a missed attraction. You can't redo wasted vacation days. You can't get back the money spent on a trip that didn't deliver the experiences you planned.

My Spain trip was supposed to be the vacation my kids remember forever. Instead, they remember that we skipped Granada, missed the Royal Palace, and spent too much time walking in the heat.

Generic AI chatbots are great for research. They'll help you brainstorm destinations, learn about attractions, and get inspired. But when it comes to execution—the booking windows, the routing, the timing, the budget, the real-world logistics that make or break a trip—they fall short.

Because answers aren't execution.

If you're planning a trip using ChatGPT, Claude, or Gemini, you're going to spend hours consolidating outputs, cross-referencing details, and building the execution plan yourself. And even then, you're likely to miss something critical.

Or you can use a platform built specifically for travel execution, where the system does the hard work and you focus on enjoying your trip.

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See Execution-Aware Planning in Action

Want to see what execution-aware planning actually looks like? Check out these real itineraries with complete timing, transport, booking requirements, and budget details:

• Venice 2-Day Luxury Escape — Byzantine splendor with Murano/Burano island hopping, complete with vaporetto routes and booking windows
• Amsterdam 3-Day Local Experience — Neighborhood exploration with bike routes, market schedules, and brown cafe recommendations
• How TravWiz Works: Your Travel Operating System — See the full platform vision and how we're building travel planning for the AI era

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Try TravWiz for Your Next Trip

Don't let poor planning tools ruin your next vacation. TravWiz provides execution-aware itineraries with:

• ✅ Hour-by-hour timing for every day
• ✅ Transport details between all activities
• ✅ Proactive booking requirement alerts
• ✅ Routing optimization to minimize backtracking
• ✅ Real-time budget tracking
• ✅ Theme-based personalization (Bucket List, Foodie, Family-Friendly, Local Experience, Romantic, Nightlife)

Ready to plan your next trip properly?

👉 Start Planning with TravWiz — Choose your destination and see execution-aware planning in action

👉 Explore Sample Trips — Browse pre-built itineraries with complete execution details

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For the Technically Curious: Why Execution Requires More Than LLMs

This section is for readers interested in the technical limitations of large language models (LLMs) for travel planning.

Generic AI chatbots like ChatGPT, Claude, and Gemini are built on Large Language Models (LLMs)—trained to predict the next word in a sequence based on patterns in training data. They're phenomenal at generating human-like text, answering questions, and synthesizing information.

But travel execution isn't a text generation problem. It's a constraint satisfaction and optimization problem.

Why LLMs Struggle With Travel Execution:

1. No Persistent State LLMs are stateless—each response is generated independently based on context windows. Your "trip" doesn't exist as a structured object with properties like budget, timing, location constraints. Chat history is not trip state.

2. No Real-Time Data Access LLMs don't know what's sold out, what the weather will be, or what's closed for renovation. They generate plausible responses based on training data, not real-world constraints. When Alhambra sells out, ChatGPT doesn't know and can't warn you.

3. No Optimization Capabilities Routing optimization (minimizing travel time between locations) requires graph algorithms and geographic computation. LLMs generate sequences based on semantic similarity, not geographic proximity. They'll suggest visiting attractions in thematic order, not optimal geographic order.

4. No Structured Output Guarantees LLMs produce natural language text, not structured data. You can't reliably extract "start_time," "end_time," "location," "transport_mode" from free-form text. Execution requires structure.

5. Hallucination Risk LLMs confidently generate plausible-sounding information that may be wrong: incorrect opening hours, non-existent restaurants, wrong booking requirements. For travel execution, wrong information is worse than no information.

What Execution-Aware Systems Do Differently:

Structured Trip State Trips are database objects with properties: activities, timing, location, budget, theme. Changes propagate through the system (move one activity, subsequent timing auto-adjusts).

Real-World Data Integration Live data on booking requirements, seasonal considerations, operating hours, pricing. The system knows what requires advance booking and when.

Constraint Satisfaction Geographic routing optimization, timing buffer calculations, budget constraint satisfaction. Algorithms, not language model predictions.

Deterministic Execution Given the same inputs, the system produces the same output. No hallucinations, no plausible-but-wrong suggestions.

LLMs are still valuable—they power the conversational interface, help with itinerary customization, and provide destination insights. But they're one component of the system, not the entire system.

Execution requires architecture, not just a better prompt.

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Building something better takes time, but it's worth it so no one else has to miss the Alhambra.