Machine Learning and AI in Travel: 5 Essential Industry Use Cases

machine learning ai travel
Illustration: © IoT For All

Imagine that you are planning a trip. A few decades ago, it would take you a lot of time and effort to research destination and accommodation options, book a flight, make a hotel reservation, rent a car, and do a bunch of other trip-related activities. Today, with the help of machine learning and AI, you can use a one-stop travel platform to plan and book everything you need. And the best thing is, you don’t have to leave your home or even your bed. 

This convenience wouldn’t be possible without machine learning and artificial intelligence technologies actively adopted by the travel, tourism, and hospitality industries in recent years. Here, you will learn about the uses of ML and AI in travel and the changes they bring to domain businesses. 


Digital assistants or chatbots are one of the most prominent examples of AI applications in the travel industry. According to statistics provided by Google, one out of three international travelers are interested in using chatbots to plan and book their trips. But why?

Chatbots are computer programs that reproduce a natural human-like conversation online. They provide real-time responses to user queries via text or voice-based messages, relying on predefined scripts. AI chatbots rely on natural language processing (NLP) to convert text into a format understandable to a machine. They capture patterns within incoming messages, single out words and phrases, and use them to identify a customer’s intent and provide an answer. 

The services of virtual travel assistants range from simply advising on a travel destination to providing a local weather forecast to even booking a room/flight or renting a car for you. Travel chatbots commonly integrate with instant messaging platforms such as Skype, Facebook Messenger, Telegram, and Slack, to name a few. 

For example, Expedia, one of the world’s leading online travel agencies, has launched a bot for Facebook Messenger to help travelers pick a suitable hotel option and move forward with a booking. By simply typing @Expedia in the field for conversations, you can get started with the bot and use its guidance to pick a suitable hotel option for a particular city and date. Not that it works flawlessly — you may need to answer the same question a few times in a row — but at the end of the day, the bot helps with trip booking and management. 

Eddy Travels is another example of an AI-powered travel chatbot that helps search for flight deals, find accommodations, and get travel inspiration 24/7. With over 200 million active users, the bot is available on a dedicated website and Telegram. 

Edwardian Hotels London offers its virtual host called Edward. This artificially intelligent chatbot application is designed specifically for text messaging; this artificially intelligent chatbot application presents hotel guests with personalized information and assistance. It can answer queries on over 1200 topics ranging from information about the nearest restaurants to the towel supply.

Travel companies keep improving their services by incorporating various intelligent assistants. Some travel chatbots can even recognize and respond to vague queries such as “romantic winter vacation in Europe.” Moreover, their functionality can go far beyond research and booking. Some chatbots can be used as mobile travel guides or companions, solving problems or providing info during a trip. 

With all the benefits brought to the table, it’s worth noting that chatbots can’t replace human interaction entirely yet.

Voice-Enabled Virtual Assistants 

AI solutions bring the concept of a seamless hotel stay experience to a whole new level. New technologies known as voice-enabled virtual assistants have already made their way to many hotels across the world. These assistants fall under the category of speech recognition software. Such software uses natural language processing and deep learning neural networks to extract meaning from human speech. For this purpose, the speech is broken down into separate audio pieces, which the software then converts, analyzes, and responds to accordingly.  

Guests can control various amenities of a hotel room with the help of tools like Amazon Alexa — the AI system behind the company’s Echo speakers. The idea is as follows: The room is equipped with various IoT devices connected to a central hub. The devices are controlled by a voice assistant. As such, a guest can manage many hotel room services like adjusting bedroom lights or turning the television on simply by giving voice commands. 

Wynn Las Vegas was the pioneer in equipping all hotel rooms with Amazon’s Alexa voice command system. A few more examples of hotels that use virtual hotel concierges are Safeco Field Suites in Seattle and Clarion Hotel Amaranten in Stockholm, Sweden. 

The hospitality industry is getting more IoT-friendly and digitally advanced. A recent report in which Oracle gathered perspectives from 150 hotel operators states that 78 percent of responders believe in the mass adoption of voice assistants to control room devices, lights, and air conditioning. 

Facial Recognition 

Another AI technology that is gaining a ton of popularity in travel is facial recognition. 

Facial recognition software can identify or verify a person’s identity by capturing, analyzing, and comparing patterns on their face. It uses artificial neural networks to process biometrics data and generate filters that transform facial details from an image into numerical features. The system then compares these features with a database to determine similarities.

For example, many airports worldwide have started using facial recognition technologies to enable tourists to pass through check-ins and document scrutinization faster and more conveniently. JetBlue Airways makes use of facial recognition for a paperless boarding experience. In cooperation with US Customs and Border Protection (CBP), the carrier placed fully-integrated biometric, self-boarding gates in some airports across the US, including New York’s John F. Kennedy International Airport (JFK).

A leading travel technology company, Amadeus, collaborated with Ljubljana Airport, Adria Airways, and LOT Polish Airlines to launch a biometric boarding pilot program. During the trial, passengers who enrolled in the program used the Amadeus smartphone app to take a selfie and photos of their boarding pass and passport. This data was sent to a secure remote server. Then, the IoT-powered cameras on the boarding gate also took pictures of each passenger and sent them to the same server. With the successful matching of photos and data, the app sent a message to the departure control system that passengers’ identity and flight status had been validated and they could be allowed to get on board. As a result, boarding times were reduced by 75 percent. 

Engines and Personalization 

Arguably, the most valuable application of AI in travel and hospitality so far is generating personalized recommendations, and for a good reason. 

Getting back to the Oracle report, “47% of consumers said AI-based promotions based on past purchases would improve their experience, 26% would visit more often if hotels offered this service.”

Just like all-too-familiar recommendations on Amazon or Netflix, many online travel agencies, airlines, and hotels apply machine learning algorithms to analyze customer data, build sophisticated recommender engines, and provide tailored suggestions automatically. 

For example, when searching for flights from New York to Los Angeles on Skyscanner, the platform recommends a few hotel options in LA where you can stay during a trip. 

The AI-empowered recommender engine generates suggestions automatically based on the search queries you’re making — but not exclusively. The engine learns from both historical data containing all digital footprints of users and real-time data. It can single out typical searches and provide the right recommendations to the right users.

In simple terms, if any of the travelers visiting New York search for Times Square and the Statue of Liberty together, the system sees this pattern and will recommend Times Square to people interested in the sculpture on Liberty Island in New York Harbor.

Sentiment Analysis 

Social media and travel review platforms have become immensely influential in recent years. A 2019 report showed that 86 percent of people (the percent grows up to 96 for Gen Z) get interested in a particular travel destination after they have seen other users’ posts online. Around 60 percent of millennials go to Facebook or Instagram for ideas.

As you can see, since customers tend to leave a trail about their travel experience, brands can use this valuable data to improve their services and make better offers. TripAdvisor alone had 884 million user opinions and reviews as of 2020. Processing this volume of data manually would be impossible. This is where machine learning techniques, namely sentiment analysis and modern, powerful computers, can be leveraged to analyze brand-related reviews quickly and efficiently. 

Sentiment analysis is the process of mining text to detect positive, negative, or neutral sentiment. Sometimes referred to as emotion AI, it uses natural language processing and supervised machine learning to detect, extract, and study what customers think of a product or service. Hotels, airlines, and other travel businesses can use customer feedback analysis to personalize and enhance their services. 

For example, Google Natural Language API enables users to analyze text with their off-the-shelf ML capabilities. 

Many travel-related companies have already used sentiment analysis to track social media reactions to their products and services. For example, Dorchester Collection, a luxury hotel operator, leveraged an AI platform to perform sentiment analysis of 7,454 reviews from 28 different hotels in different regions for its brand study. 

What the Future Holds for AI in Travel

In 2018, the International Air Transport Association (IATA) predicted that air passengers would hit the 8.2 billion mark by 2037. While passenger numbers are again on the rise, the global pandemic has undoubtedly changed that forecast. This is just one example proving that predicting the future accurately is simply impossible. At the same time, attempting to do this can draw a picture of what to expect. Let’s see what the three main AI trends are in the travel industry. 

More Personalized Travel Planning 

In addition to the level of personalization already available in travel planning, it’s expected to become even more tailored to individual needs. Powered by AI and ML capabilities and integrated with wearable health measurement devices, mobile applications may track passenger health conditions and suggest safer in-destination activities and less crowded paths on the fly.

Artificial Intelligence Systems for Baggage Handling

Airports deal with thousands of bags daily, so it was only a matter of time before baggage handling would be automated. There has already been a successful pilot of an AI-powered luggage handling system without baggage labels at Eindhoven Airport. The system tracks bags from check-in and through their journey both off and on airplanes, so passengers know exactly where their luggage is. The forecast is that more airports will follow the lead. 

Robots and Virtual Assistants for Self-Service 

COVID-19 hit the travel industry with a vengeance, so it makes sense that businesses will be more interested in smart, contactless mechanisms for self-service processes to avoid the need for human interaction. For that reason alone, expect that both robots and virtual assistants will see greater demand in the future. 

AltexSoft is a software development and technology consulting provider focusing on travel tech and data science. The company is on a mission to help organizations use their data potential with predictive analytics and machine learning and accelera...
AltexSoft is a software development and technology consulting provider focusing on travel tech and data science. The company is on a mission to help organizations use their data potential with predictive analytics and machine learning and accelera...