14 ways AI is impacting hotels
Artificial intelligence has been a transformational force across industries.
For hotels, AI has become a catalyst for smarter, faster, and more profitable hotel operations. From personalized guest communication and predictive maintenance to data-driven marketing and revenue forecasting, AI is enabling hotels to make better decisions across every department.
At its core, AI is about more than automation or convenience. It’s about unlocking the full potential of your data, identifying patterns, understanding cause-and-effect relationships, and surfacing insights that would be impossible to find manually. For hotels, this means the ability to anticipate demand shifts, engage guests more effectively, and run leaner, more connected operations.
In this guide, we’ll break down the different types of AI used in hospitality today and explore how these technologies are helping hotels across every area of operations.
Artificial intelligence, or AI, refers to the ability of computer systems to perform tasks that require human intelligence, such as analyzing data, recognizing patterns, making predictions, solving problems, and understanding human language. Depending on the task, AI employs different models and algorithms, often categorized hierarchically. Below, we’ve broken down some of the most popular types used in hospitality. What is artificial intelligence?
Generative AI
If the goal is also to interact with humans using natural language and to create content (texts, code, images, etc.), this ability is known as generative AI, and it’s a typical feature of tools such as ChatGPT and Gemini. Generative AI models are trained on vast amounts of data to understand and replicate patterns to generate new, similar content.
Generative AI enables hotels to craft creative social media or email copy, respond to hotel guest reviews, generate targeted upsell messages and package descriptions, and much more.
Machine learning
Machine learning (ML) analyzes a broad range of data to identify patterns and correlations in data. When the data involves human language, machine learning uses a series of techniques and algorithms called natural language processing (NLP) to create models that can make predictions or decisions without being explicitly programmed.
In hotels, ML powers tools like dynamic pricing engines, demand forecasting, and guest segmentation.
Causal AI
Causal AI aims to understand and model cause-and-effect relationships. By understanding the root cause of issues, causal AI offers even deeper insights for strategic decision-making, enabling more accurate predictions and suggestions.
For example, if occupancy drops on certain dates, causal AI can help determine whether it’s due to pricing, seasonality, local events, weather, etc. This means more accurate forecasting, smarter rate recommendations, and optimized marketing spend.
Multimodal AI
Multimodal AI refers to systems that can understand and generate content across multiple outputs, like text, images, video, and voice, in a unified way.
Multi-modal AI can be used in a number of different ways. For example, a multimodal assistant can process a guest’s voice message asking for a late checkout, check reservation details from the PMS, and send a personalized confirmation. Or, if a traveler asks about room types, it could provide a visual room preview.
14 ways AI is impacting hotels
The industry is starting to see exciting advancements in which AI is being incorporated into hotel tech. Here we’ve broken down 14 applications of AI technology that are having the greatest impact on hotel operations.
Guest experience
Managing and personalizing the guest experience was once a time-consuming and tedious process. With AI, hotels can reduce manual tasks, focusing their efforts on strategic initiatives.
1. Sentiment analysis
It’s important for hotels to have a bird’s-eye view of guest feedback, including what words they use most often to describe their stay and common pain points or areas of praise.
This activity is called a sentiment analysis. To do this manually, you would have to go through all new reviews regularly, trying to find patterns and keep track of changes in sentiment over time. AI can do that for you through NLP AI algorithms, going beyond star ratings to gauge the sentiment spectrum from extremely positive to negative.
The data pulled from a sentiment analysis can help owners and operators make changes to hotel operations and guest services, and invest in initiatives that will have the greatest impact on guest satisfaction.
2. Guest messaging
Unified inbox tools aggregate messages across different channels (SMS, emails, WhatsApp, the hotel’s in-app chat, etc.), allowing you to engage with guests before arrival and during their stay. Like with online reviews, generative AI functions as a virtual assistant for front desk staff, automatically responding to frequent queries, solving issues before arrival, and making personalized recommendations for activities. This not only enhances the guest experience but also creates opportunities for upselling and cross-selling.
Here, Pormer Sarram, Co-Founder & CEO of Cloudbeds’ partner Visito AI, shares some best practices for getting started with AI messaging.
3. Personalized experience
Once you have collected enough data from guests in your CRM system, AI can use machine learning to help you come up with ideas for personalized services. A recent research study found a strong link between personalization and overall customer satisfaction, showing that 61% of consumers are willing to spend more with companies that offer a customized experience.
Marketing
Marketing is one of the departments that AI has had the greatest effect on, as it has enabled more personalized, efficient, and data-driven marketing strategies for hotels.
4. Reputation management
Responding to online reviews attracts higher ratings and improves ranking in search engines. AI-driven reputation management tools simplify this process by aggregating reviews from different channels (like Google and Tripadvisor) into one spot. However, responding to each review can be quite time-consuming, especially if you want to personalize replies and avoid boilerplate formulas.
To speed up the process further, reputation management tools have started to incorporate generative AI to write personalized responses following simple prompts such as “reply in a professional and constructive manner.”
For example, if a guest leaves this negative review:
The service at the hotel’s restaurant is terrible. The servers act as if they are doing you a favor. For such a nice hotel, I would expect better service and not have to wait 10 minutes to get a server’s attention. Do better.
AI will quickly generate a response:
“Dear [guest name], Thank you for your feedback. We apologize for the poor service you experienced at our restaurant. This is not the standard we strive for, and we are addressing this with our team to ensure it doesn’t happen again. We hope you’ll give us another chance to provide the excellent service you deserve. Please contact me directly if you have any further comments or concerns.”
The aggregation of reviews saves hoteliers time jumping between platforms, and the ability to generate responses allows hotels to speed up the process while still being able to edit and personalize each reply.
During Compass, Cloudbeds’ quarterly product webinar, Ben Lloyd, Director of Digital Marketing Services, showcased how Cloudbeds’ Reputation Management tool works.
5. AI voice concierge
AI-powered concierges transform how hotels handle inbound communication. These virtual concierge systems can answer calls instantly, handle routine tasks, complete bookings, and even upsell services, all without involving human resources.
Cloudbeds’ Engage is a leading example. With sub-100ms responses, agentic intelligence, and voice-native design, Engage delivers real-time, human-like conversations that book, serve, and upsell.
6. Website chatbots
The latest generation of AI-powered chatbots, integrated with booking engines, can respond to common visitor queries 24/7 in multiple languages. These AI chatbots provide specific information about hotel room rates and availability, and they even allow guests to complete bookings directly in the chat window, capturing personal and payment data.
7. Segmentation and targeted campaigns
Through machine learning algorithms, AI can analyze CRM data to identify segments beyond classic business and leisure travelers, families, or couples. By digging deep into booking history, room preferences, dining choices, preferred spa treatments, and spending habits, AI helps hotels create highly personalized marketing campaigns.
With generative AI, content, including email copy, social media posts, and digital ads, can then be generated for these campaigns with the click of a button. Amit Popat, Head of Machine Learning at Cloudbeds, showcased how generative AI has been implemented into Cloudbeds Guest Marketing during Compass.
Revenue management
The use of AI systems has made it possible for revenue managers to be more dynamic in their revenue management strategies.
8. Demand forecasting
Traditionally, revenue management systems optimized rates by analyzing historical data and relying on predefined rules and manual adjustments. The introduction of AI and machine learning has given RMS the ability to consider many more factors, such as special events, weather patterns, and competitor pricing, allowing them to:
- Optimize rates in real-time and forecast demand, occupancy rates, and pricing strategies
- Suggest profitable package deals based on guest preferences and seasonal trends
- Identify new revenue opportunities by offering additional products and services
- Learn and adapt to new information, continuously improving performance over time
9. Competitive intelligence
Instead of scouring the web to check competitors’ rates, revenue managers can leverage AI tools to continuously monitor competitor pricing and market trends. RMS’ can suggest rate updates based on gathered intelligence and prompt hotels to make changes as soon as they become applicable.
10. A shift to revenue marketing
AI is ushering in a more integrated approach to commercial strategy where revenue and marketing teams work together, using the same data. Rather than optimizing rates and promotions in isolation, hotels can identify the most valuable segments and deploy targeted campaigns using causal AI.
Cloudbeds Intelligence, for example, harnesses billions of data points and uses causal AI to help hotels proactively identify high-converting audience segments and suggest strategic marketing actions to help improve profitability.
Operational efficiency
AI has helped hoteliers automate repetitive tasks and provide valuable insights to help increase productivity and profitability.
11. Predictive maintenance
In industries such as automotive and aerospace, predictive maintenance monitors equipment usage and performance to detect anomalies and predict potential breakdowns. Traditional rule-based systems trigger preventive maintenance actions based on predefined thresholds.
Now, these systems are used alongside AI-powered machine learning algorithms that analyze real-time equipment data, such as temperature, vibration, and pressure readings, collected by IoT (Internet of Things) devices. AI-based predictive analytics is starting to be used by hotels, too, to prevent issues with key systems such as kitchen, laundry, and spa equipment, air conditioning, and in-room technology. Not only are these tools good for the bottom line, but they make a significant impact on sustainability.
12. Housekeeping
Modern cloud-based apps used in housekeeping offer scheduling functions that eliminate the need for spreadsheets and printouts. However, when prioritizing room service, you may have to look not only at guest check-in and check-out times, but also at staff availability and skills, room types and location.
With 72% of hotels unable to fill open positions (half of which note housekeeping as a top priority), hotels need to leverage AI to streamline operations. With machine learning, it’s possible to automate scheduling considering all of the above factors together, making sure each room is serviced by the right housekeeper in the right order. AI can also automate inventory management by analyzing guest preferences and usage patterns to determine the right time to order new supplies of toiletries, linens, and other amenities.
13. Facial recognition
A standard feature on many smartphones, facial recognition can be a way for hotels to automate check-in completely. All guests need to do is upload their documents and a selfie prior to arrival. The system will then analyze it and compare it with the features from the ID photo using deep learning algorithms, a subset of machine learning designed for large datasets and complex patterns.
Facial recognition can also be used instead of digital keys for room access and to verify guest identity for charges to their room or at hotel outlets.
Reporting & analytics
14. Real-time insights & suggestions
AI should be incorporated into every aspect of hotel reporting and data analytics to bring departments together with real-time insights and suggestions to improve operations and boost profitability by:
- Identifying emerging trends and implementing campaigns based on booking behavior, guest profiles, and guest preferences
- Forecasting peak periods using historical data and external factors like local events or seasonal trends, allowing hotels to adjust pricing, staffing, and inventory management proactively
- Generating and distributing custom reports to stakeholders with interactive maps, charts, and other visual representations of key metrics
Establishing a strong technology foundation
The adoption of AI-powered tools in the hospitality sector is not a future possibility: it’s already happening, though at varying levels of maturity, as new advancements become more accessible.
Some AI solutions, such as generative AI and AI-driven revenue management systems, are already widely available and integrated into daily operations. Meanwhile, other applications, like predictive maintenance and facial recognition, are still emerging, with an increasing number of hospitality businesses starting to adopt these technologies.
To successfully embrace AI, your starting point should always be a problem you’re trying to solve in your business, whether it’s analyzing guest data, personalizing communications with guests, or increasing automation. It’s crucial to partner with technology providers that can demonstrate real-world use cases and have thoughtfully incorporated AI into their systems based on these actual challenges.
Another key consideration is that AI is only as effective as the quality and consistency of the data it has access to. It’s, therefore, important for hotels to use applications that collect accurate data consistently and use the property management system as the single source of truth for data-driven decisions.
Cloudbeds’ investment in AI
At Cloudbeds, we recognize the power of AI and have made significant investments in developing unparalleled applications for the hotel industry.
Cloudbeds Intelligence, our AI and machine learning layer, is built to supercharge all functionalities of the hospitality management system and break down departmental silos, delivering unmatched decision-making intelligence for revenue managers, marketers, GMs, operations staff, and more.
The innovative platform layer implements causal AI using rich datasets within the Cloudbeds platform and from partner data, which will allow hoteliers to better understand and forecast property performance and take actionable steps to boost revenue, optimize time and costs, and improve the guest experience
