How AI is shaping the future of the travel sector

10 AI use cases in the travel sector

Here are 10 use cases for AI in the travel sector that demonstrate the technology's benefits.

1. Dynamic pricing

Businesses in the travel sector were some of the earliest to use AI to adjust prices based on market conditions, said Suseel Menon, Everest Group's practice director. This approach, known as dynamic pricing, considers factors such as demand fluctuations, competitor prices, weather patterns and major events that could affect travel.

Although the travel industry has used AI for dynamic pricing for years, Menon said its use is becoming more sophisticated and personalized.

"Dynamic pricing has been done with machine learning for quite some time," he said. "But what's changing is the nuance, the hyperpersonalization of it.... If you look for [a travel option] three or four times, [businesses] potentially know if you're comparing prices or whether you're on the fence, and they're able to tweak their prices."

2. Automation of office tasks

Travel and tourism organizations are using AI to make business operations more efficient and automated, a trend mirrored across industry sectors, said Erika Richter, vice president of communications for the American Society of Travel Advisors.

"They're using it to enhance their business and how they conduct business," she said.

For example, travel agents are using generative AI tools such as ChatGPT to write emails and marketing material, she said. AI tools can also assist with onboarding new customers, as well as routing customer calls and emails to the right specialists to accelerate response times and service fulfillment.

3. Operational efficiency and asset management

Travel companies are also using AI to automate back-office functions and improve efficiency, Menon said.

For example, transportation companies are using AI for asset management, fleet maintenance, predictive maintenance, route selection and route optimization. These applications help businesses reduce downtime and repair costs, minimize scheduling delays and improve overall service efficiency.

4. Summarization of information

Travel companies can use AI to summarize and personalize information for travelers, Menon said. For example, an online booking site could use generative AI to summarize reviews of destinations or activity options, based on a customer's questions or interests.

5. Additional automated services

AI is being used to automate many tasks that once required human effort, said Alex Cosmas, a McKinsey partner specializing in advanced analytics for the travel and logistics sectors.

Travel firms today use many types of intelligence to serve customers and assist human workers in providing support, Cosmas said, from classical machine learning to large language models.

6. More customer empowerment and self-service options

Along the same lines, AI enables customers to handle more travel-related tasks themselves, Richter said, whether via internet searches, travel company websites or self-service tools created by travel companies.

Richter cited prospective travelers use generative AI to more thoroughly investigate travel options and design itineraries tailored to their interests. Travelers can more easily book those AI-generated trip plans themselves, she said, often using AI-enabled chatbots and other AI-powered self-service tools.

In addition, Richter said, travelers are bringing those AI-generated plans to travel agents, using them as a starting point for conversations. Travel agents can then refine the plans to better match what the customer has in mind.

7. Smoother experiences

The travel industry uses AI to minimize friction for customers and "to make the actual 'day of' magical," Cosmas said. Travelers can see this application of AI at work when they use hotel kiosks or mobile apps to check in and out of their hotel rooms, he explained.

Similarly, hotel staff -- aided by AI algorithms embedded in their company's IT systems -- can instantly offer customers dining, theater and other recommendations tailored to their tastes. And travel apps use AI to make similar real-time recommendations, including links to reservations so customers can immediately lock in their plans, Cosmas said.

8. Measuring customer sentiment

To ensure customers are having a good time, some businesses in the travel sector are using AI to measure customer sentiment more accurately. "One of the biggest applications of AI is uncovering customer satisfaction without asking directly how they're feeling," Cosmas said.

A range of AI capabilities are used to do this, Cosmas explained. For example, voice sentiment analysis uses natural language processing and machine learning to understand human speech and categorize its tone as positive, negative or neutral. In some cases, AI can even automate responses to reinforce positive sentiments or remedy neutral or negative ones.

9. Improved customer experience

Travel companies are using the data they gather from customer sentiment analysis and other sources to enhance both operational efficiency and customer experiences, Kaushal said. This data can help companies refine services and deliver better value throughout the entire travel journey.

In fact, improving the customer experience is a primary driver of AI adoption for technology buyers in the travel industry, according to the Everest Group's research, he said.

10. Enhancing problem-solving with AI agents

The travel industry is exploring how agentic AI can enhance travelers' experiences, Menon said. Agentic AI systems can autonomously make decisions and take actions, enabling them to work independently and without direct human intervention.

As an example, Menon described the scenario of a canceled flight. An AI agent would first identify the problem and then take action to address it: booking a new flight, ensuring bags are routed to the right plane, notifying relevant hotels or car rental companies about the updated plans, and sending the traveler the revised itinerary.

Challenges and obstacles to AI adoption in the travel industry

As companies in the travel sector advance their use of AI, they face multiple challenges. Some are common across all verticals, Cosmas said, while others are unique to the travel sector.

1. Ensuring data privacy and governance

In addition to collecting and being able to access the right data at the right time, industry players must manage, protect and secure that data.

"The travel industry deals with a lot of customer data," Kaushal said. "The concern is, how will it ensure data privacy and compliance to data best practices and regulations?"

2. Dealing with legacy systems

As in other industries, IT environments in the travel sector often involve legacy technology that can limit the flow of data needed for AI applications. As such, maximizing AI's potential might mean investing in other modern technologies, Cosmas said.

3. Navigating industry fragmentation

The travel industry's fragmentation and scale also complicates AI adoption, Cosmas added. For example, a hotel that wants to improve the customer experience might also need information about travelers' transportation and planned activities -- data often stored in external systems outside the hotel's control.

4. Alienating customers

Travel is a high-touch industry where some customers might prefer traditional person-to-person interactions over their AI-assisted counterparts.

"[Companies] can't launch a digital experiment without the risk of alienating some of customers who enjoy things the way they are," Cosmas said. Therefore, travel firms must devise risk mitigation strategies when designing, piloting and scaling their AI initiatives.

5. Getting customer segmentation right

Many companies segment customers into groups based on factors such as shared characteristics and preferences. This customer segmentation lets companies tailor services, such as marketing campaigns, to each group.

But customer segmentation is more difficult in the travel sector, Cosmas said. Travel is highly individualized, with many variables on any given trip: start and end locations, time, price points and group size.

"No two people will have the same experience, so it's very hard to segment customers into one of 10 labels, for example, or statistical clusters," he said. "[Travel] is a customer segment of one."

Cosmas also emphasized the importance of personalizing services without overreaching. Travel companies that use data to highly tailor the experience to a single specific traveler risk being seen as "creepy."

"Getting it right in that sweet spot is unique to travel," he said.

6. Assigning responsibility for AI processes

AI systems that are predictive in nature can produce errors. Generative AI tools, for example, sometimes give false or misleading answers, known as hallucinations.

A lawsuit involving Air Canada demonstrates the risk here: In Moffatt v. Air Canada, Air Canada was found liable by the British Columbia Civil Resolution Tribunal for misinformation given to a consumer by an AI chatbot on its website.

It's not enough for travel companies to just understand those risks, Kaushal said. It's also essential to assign ownership of the relevant AI processes and the outcomes themselves.

7. Losing human creativity and personal touch

Although companies tout the use of AI to personalize services, experiences and products, machines can't always match what a human worker can do, Richter said.

She pointed out that human agents bring creativity and empathy to the travel experience, solving customers' travel problems and crafting novel adventures. These strengths don't come from analyzing known data sets, but rather from getting to know clients personally.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.