Key AI concepts every hotelier should know
Booking Engine 12/12/2025
How to distinguish the real value of smoke
Artificial intelligence is advancing so rapidly that it sometimes seems as if a new concept, a new tool, or a supposed revolution promising to transform the tourism sector appears every week. And yet, when we scratch the surface, we discover an uncomfortable truth: many companies talk about AI, but few are actually applying it in a useful, safe, and operational way in the day-to-day running of hotels .
In this context, it is essential that hoteliers have a clear framework. Not to become computer engineers, but to be able to make informed decisions, demand real value from their suppliers, and avoid being misled .
This article has a simple objective: to present the AI concepts that really matter in tourism today , explaining what they mean, how they can affect hotel management, and why they will be key in the coming years.
1. MCP: the standard that allows AIs to connect with the hotel
The Model Context Protocol (MCP) is one of the most relevant concepts in the current landscape. It is a standard that allows AI models to connect to external tools—such as PMS, CRS, channel managers, or booking engines—in a secure and structured way.
Why is it so important for hotels?
Because AI, however powerful, is useless if it can't interact with the hotel . MCP makes it possible for AI to work with real, day-to-day data.
- Availability
- Rates
- Policies
- Restrictions
- Contracts
- Inventory
- Demand history
Now, let's be realistic. This privilege, as of today, is reserved for only a few partners, and primarily in the US. What you really need right now is for your supplier, your engine, to be ready when the barrier is lifted. A date that is still undefined.
Key message
AI without tools is just smoke and mirrors. MCP is what allows AI to bring real value to the hotel.2. AI Agents: When AI stops talking and starts working
Until now, most generative AI applications have focused on text. But the future, and already the present, lies in agents : systems capable of planning, acting, verifying, and repeating until a goal is achieved.
An agent can:
- Check your systems.
- Make a decision.
- Perform an action.
- Evaluate whether it has worked.
- Iterate until you achieve the desired result.
Real-world applications in tourism
- Automatic tariff adjustments based on rules and demand.
- Parity monitoring with corrective actions.
- Intelligent rating and management of reviews.
- Automation of repetitive front desk tasks.
- Support for the revenue manager in complex analyses.
Key message
If it doesn't perform tasks connected to the hotel ecosystem, it's not an agent: it's marketing.3. A2A: Agents talking to agents to solve complex problems
The Agent-to-Agent (A2A) model allows different specialized agents to communicate with each other and collaborate to achieve a goal.
Let's imagine a revenue automation:
- An agent analyzes the demand.
- Another checks availability and restrictions.
- Another calculates the risks of overbooking.
- One last thing applies the update in the CRS.
Why is this so relevant for hotels?
Because many operational processes in the sector are not linear. They require validation, cross-checking, negotiation between systems, and sometimes human intervention. A2A allows you to automate complexity without losing control.
Key message
True automation in the sector will come from coordinated work between agents, not from isolated chatbots.4. RAG: the key to preventing AI from inventing answers
RAG (Retrieval-Augmented Generation) is the mechanism that combines generative AI with real hotel data .
- Without RAG:
AI can invent answers . - With RAG:
Respond only based on verified information from the hotel.
Examples:- Chatbots that answer questions about rates and policies without errors.
- Internal assistants who consult contracts, agreements, inventory, or technical documentation.
- Generating content from real engine data.
Key message
RAG is the barrier between a useful AI and a dangerous AI.5. Specialized models vs. LLMs
Many suppliers boast about using LLMs (GPT-4, GPT-5, etc.), but in tourism many tasks are better solved with small, specific and fast models .
Examples:
- Proprietary models for detecting parity.
- Models for classifying sentiment in reviews.
- Models that predict cancellations.
- Models that optimize descriptions based on conversion.
The current state: which LLMs can connect to real systems today
AI models already exist that can securely connect to external systems , query real-time data, and execute actions within a hotel ecosystem. Today, solutions like OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini allow integrations via APIs, native tools, or open standards like MCP (Model Context Protocol) , enabling models to interact with PMS, CRS, booking engines, channel managers, and internal databases. However, as we mentioned earlier, access to these models is very limited.
Other environments, such as Microsoft Copilot , function as AI layers connected to the company's operating software, and unified platforms like OpenRouter allow combining different models as needed.
In short, technology now allows AI not only to generate content, but also to directly control the systems that support hotel operations , always with human oversight and security guarantees. This marks a turning point in how the sector must evaluate its suppliers: not by what they promise, but by what real-world integrations they are capable of delivering today .
For informational purposes, it's worth considering the evolution of LLM usage when deciding between different options. Although preliminary battles currently favor OpenAI (Chat GPT), the trend graphs suggest that Google (Gemini) will ultimately win the race ( nextword.substack.com ).
What should the hotelier understand?
The value is at:
- The data with which those models are trained.
- How well they integrate with the hotel's ecosystem.
- What they automate.
Key message
The size of the model doesn't matter. What matters is that it solves a real problem for the hotel.6. The forgotten factor: real integrations (not promises)
Much “AI for hotels” fails at the most critical point: it is not integrated with the hotel's actual systems.
Without controlled access to data, AI is useless.
A hotelier should always ask:
- What systems does it integrate with?
- What can they read and what can they write?
- Who validates the actions?
- What happens if something goes wrong?
- Is there complete traceability?
Key message
AI should be an ally, not an operational risk.Conclusion: Understanding AI to make better choices (and avoid the hype)
AI is poised to transform tourism, and the hotel sector in particular. But its value depends on something as simple as it is essential: that it solves real hotel problems.
As an industry, we must demand clarity, transparency, and tangible results. MCP, agents, A2A, RAG, Guardrails… These aren't technical terms: they're concepts that will determine which solutions deliver value and which only make empty promises. Let's not forget:
In a time when everyone is talking about AI, only the one that understands the hotel's context and improves its daily operations will survive.
