The Problem: Generic AI responses
Have you ever asked ChatGPT “Create a direct-booking strategy for my hotel”?
You’ll get the standard response: create compelling website, offer rate parity, use email marketing, implement retargeting campaigns.
Ask about increasing revenue and you’ll get another checklist: improve online reviews, optimize pricing, implement loyalty programs, enhance social media presence.
Doesn’t matter if you’re running a luxury mountain resort or a budget city hotel. Same checklist.
The AI doesn’t care that your mountain resort guests book 6 months in advance while your city hotel gets same-day bookings.
But most importantly, AI doesn’t care whether you provide all the necessary information and context for it to give you a solid response.
In other words: whatever you ask, AI will reply, no matter what.
If you ask “How do I increase direct bookings for my hotel?” without mentioning your hotel type, location, current booking mix, target guests, or budget, AI will still give you a confident, detailed answer.
That’s how AI works. Built to please. So if you ask, AI won’t complain about the fact that it doesn’t have the necessary information and context to give you a detailed answer.
Instead, it’ll give you a one-size-fits-all solution that’s not really a solution, but the most generic outcome it could come up with.
When no detailed information is provided, AI will do what it does best: it assumes.
It assumes you have a marketing budget. Assumes you have a dedicated website. Assumes your guests behave like average travelers. Assumes your market conditions match some generic hotel scenario.
When these assumptions are wrong, the advice becomes useless. Or worse, expensive mistakes.
You implement email marketing for guests who never check email.
You focus on rate parity when your guests don’t even compare prices.
You build loyalty programs for travelers who never return to the same destination.
But what if AI actually refused to help until it understood your specific situation?
That’s exactly what just changed, at least in the context of ChatGPT, with its new mode “Study & learn”.
How Study & Learn work
The name “Study and Learn” is pretty self-explanatory.
But you might be wondering: what does Study & Learn have to do with the problem I just described – AI giving instant answers no matter what?
It has everything to do with it.
When you select Study & Learn mode, the approach to your questions changes radically.
Instead of giving instant answers, it actually studies your situation first. More importantly, it helps YOU understand the situation first. Like a tutor, instead of providing answers, it develops your reasoning capabilities so that you can answer your own questions.
Let me show you the difference.
Here’s what happens when I ask regular ChatGPT: “How can my hotel get more direct bookings?”
Same question, but this time in Study & Learn mode.
“Before we develop a direct booking strategy, let’s explore your current situation together. What type of property do you operate? What percentage of your bookings currently come through direct channels versus OTAs? Who makes up your primary guest demographic? What’s driving guests to book through third parties instead of directly with you?”
Notice the difference. Regular ChatGPT assumes it knows your situation.
The biggest problem in such a scenario is that you are required to know and provide with whatever contextual information AI needs.
If you fail at it, the output you’ll get not only will be useless, but harmful because you are at risk of listening and implementing baseless suggestions.
Consider instead Study & Learn mode as your tutoring buddy.
This creates a completely different dynamic. Instead of receiving generic advice, you’re guided through a thought process that’s specific to your hotel’s reality.
Not just better advice, but advice you actually understand because you helped create it.
Direct Booking Strategy Case Study
So let’s move on with our case study.
I’m going to walk you through a complete direct booking strategy session using Study & Learn mode, so you can see exactly how this plays out in practice.
I start with the same question I used before: “How can my hotel get more direct bookings?”
But this time, instead of getting a generic list, ChatGPT begins investigating my specific situation.
“Let’s work through this together. First, tell me about your property – what type of hotel are you operating, where is it located, and what’s your typical guest profile?”
I respond: “It’s a 45-room boutique hotel in downtown Portland, Oregon. We primarily serve business travelers during weekdays and leisure couples on weekends.”
“Excellent. Now, what’s your current booking distribution? What percentage comes through your website versus OTAs like Booking.com or Expedia?”
“About 25% direct, 75% through OTAs.”
“That’s helpful context. What do you think is driving guests to book through OTAs instead of directly with you? Have you identified any specific barriers?”
Notice what’s happening here. Instead of jumping to solutions, it’s helping me analyze my actual situation. Each question builds understanding that will inform the strategy.
This conversation continues for several exchanges, with ChatGPT guiding me to examine my rate parity, website user experience, marketing channels, and guest feedback.
Only after this thorough exploration does it provide recommendations – and they’re completely different from the generic advice regular ChatGPT would have given.
Other Uses for Hotels
The direct booking strategy is just one example of how Study & Learn mode transforms AI from generic advisor to personalized consultant.
This approach works for virtually every area of hotel management where context matters, which is basically everything.
For example, Revenue Management: Instead of generic pricing advice, Study & Learn mode digs into your specific situation by investigating your specific market dynamics first.
It wants to understand your competitive set, your seasonal patterns, your guest segments, and how price sensitivity varies across your different markets.
The resulting strategy is likely not going to be some generic pricing theory, but rather a nuanced approach built on your actual market position and guest behavior.
Distribution Strategy Development: Study & Learn can examine which channels drive your highest-value guests, where you’re seeing costly acquisition, and how guest behavior differs across platforms.
You end up with a distribution strategy that’s actually executable for your property, not textbook theory.
Performance Analysis: Study & Learn mode turns data interpretation into collaborative investigation by guiding you through understanding what’s happening in your numbers and why it’s happening.
You develop analytical thinking that helps you spot patterns and connect performance to real operational factors.
Also Guest Journey Optimization which is something that unfortunately too often is overlooked. The mode walks you through mapping your actual guest experience and helps you identify where friction exists in your real operations, from first touchpoint to checkout.
This creates improvements based on your actual guest flow, not generic hospitality best practices.
The pattern is always the same: understand first, advise second. Context before recommendations. Your reality before generic solutions.
This is what AI assistance should look like across every aspect of hotel management.
Conclusion
And that’s it. AI blew our minds from day one, but every day that goes by, we realize how basic yesterday’s version really was.
