How AI and machine learning are changing corporate travel

Artificial intelligence (AI) and machine learning (ML) tools, like ChatGPT, have become household names, attracting users of all ages and backgrounds. These innovations promise to enhance productivity and personalization and where corporate travel is concerned, AI and ML are beginning to change the way we book and experience travel.

Before diving into how technological changes are impacting human behaviors, it’s important to acknowledge enduring human traits. Over the past 40 years, human nature hasn’t transformed significantly, even as technology has rapidly advanced. People still seek efficiency, speed, affordability, value, and a sense of safety and satisfaction. AI and ML can help to satisfy all these traits in improved ways.

At CTM, we focus on 5 innovation pillars that underpin our technology strategy to deliver benefits to our customers:

• Simplicity: Making things easier for customers.
• Speed: Improving process efficiency or eliminating the need altogether.
• Savings: Delivering financial savings and return on investment (ROI).
• Safety: Ensuring traveler well-being is considered at every step.
• Sustainability: Promoting sustainable travel choices.

Incorporating AI and ML into the technology strategy not only ensures that we continue to deliver key benefits for our customers in more effective, efficient and personalized ways to meet the fast-changing needs and expectations of travelers and the travel environment.

Navigating the future of travel

While we often hear a lot about AI in corporate travel and its influence on personalization, its real impact is much larger in how developers and software engineers create, design, test, and deploy the code that powers the apps and services we use daily, making them work seamlessly behind the scenes.

But what happens when everyone is using advanced tools and methods, creating more sophisticated AI, such as chatbots, personalized trip search algorithms, continuous pricing models for airlines, hotel rate predictors, and everything in between? Adam Hunger, CTM’s VP of Engineering, explains, “We are approaching a time when suppliers in the business travel value chain will have access to the capabilities and knowledge to deploy sophisticated recommendation engines, dynamic offers, repricing thresholds and so much more. These advancements will pull or push travelers to make travel purchases based on competing incentives; getting the best fare or travel experience for the traveler, versus reaching strategic shifts in market share and profitability for the supplier.

“Imagine you work with preferred hotel partners to receive negotiated rates and value-adds, and then all of a sudden a new hotel targets your frequent travelers with unique dining experiences, spa treatments or a personal training session.

“Travel policies, the booking process, visibility of rates, and the process from booking through to travel will all undergo substantial transformations. Trying to use an outdated travel policy or an obsolete online booking tool (OBT) in this era of advanced technologies won’t stand a chance.

“Keeping things fast and simple with all this underlying complexity is no small feat, but we’re excited about the opportunities AI and ML offer in this space, and the progress we’re making with recommendations and smarter search results in Lightning (CTM’s proprietary OBT) that deliver benefits for both travelers and your corporate travel program.”

How can AI and ML support a more sustainable future?

Addressing whether AI and ML can contribute to a more sustainable future for travel first requires an exploration of factors influencing the carbon emissions associated with the process of travel.

Aircraft specifics

Aging aircraft, accumulating weight over time, and larger planes that retain dirt and moisture all contribute to increased fuel burn during takeoff, landing and flight. Newer engines, with higher fuel efficiency, present a more environmentally friendly alternative. Seating configuration, including the distribution between first, business, and economy class seats, also plays a role in influencing the weight and efficiency of a plane.

Furthermore, the passenger and cargo load factors are critical considerations. While fuller planes may weigh more, they contribute to reduced emissions on a per-passenger basis. Airlines with consistently high load factors demonstrate greater sustainability compared to those operating numerous half-empty planes.

So how are AI and ML used to support things like aircraft maintenance, passenger loads and fuel efficiency?

For maintenance, ML is used to predict issues by analyzing data from sources like flight data records and logbooks. Utilizing big data from condition monitoring and predictive solutions, such as image-based AI, these models efficiently detect anomalies that might pose challenges for human detection, enhancing maintenance practices.

In operational management, AI ensures optimal flight bookings through revenue management and dynamic pricing. ML algorithms leverage historical data, flight distance, and willingness to pay, maximizing sales revenue while maintaining flight capacity. While it’s important for airlines to fill as many seats as possible, they also need to manage weight carefully. Carrying extra weight on a flight, specifically unnecessary items, increases fuel consumption. This extra fuel burn due to additional weight is usually around 2.5 to 4.5 percent per hour of flight. AI and ML can assist airlines in optimizing seat capacity, making room for essential cargo, enforcing luggage weight limits, and reducing unnecessary items onboard. This helps airlines save fuel and operate more efficiently for a more sustainable environment.

Flight route and altitude

Exploring the impact of the flight path and altitude on emissions is crucial. Even if a flight takes the same amount of time and burns the same fuel, factors like condensation trails (contrails) come into play. Contrails, formed when jet engines release soot and heat into moist air, contribute significantly to global aviation’s heating impact, estimated at around 35% by a recent Intergovernmental Panel on Climate Change (IPCC) report.

The team at Google, leveraging AI and ML capabilities, partnered with American Airlines and Breakthrough Energy to address this issue. By analyzing contrail data and adjusting flight paths to avoid areas prone to contrail formation, they achieved a remarkable 54% reduction in contrails on 70 test flights compared to non-Google predictions. This collaboration represents a positive step forward for aviation’s environmental impact.

Sustainable aviation fuel (SAF) usage and investment

We know that today there is only enough sustainable aviation fuel for 0.1-0.2% of the total aviation industry, but some airlines are adopting this quicker than others. Over the past few years, CTM has partnered with Delta to purchase SAF in a multi-year agreement that aims to reduce lifecycle emissions by 209 metric tons, and invested in United Airlines’ EcoSkies Program to allow customers to contribute to SAF production.

SAF offers an eco-friendly alternative to traditional jet fuel, using renewable resources, organic materials, or waste. While SAF holds promise for reducing carbon emissions, challenges like cost and accessibility exist. This is where AI comes in. AI can enhance production efficiency by analyzing data, identifying areas for improvement, and ultimately lowering production costs. AI-driven predictive models can also assist in selecting the best raw materials and refining processes. Taking into account factors like climate data, crop yields, and waste availability, AI can discover innovative SAF solutions that are both environmentally friendly and economically viable.

It’s the beginning of a new chapter in technology innovation

The integration of AI and ML in corporate travel is not just a trend but a transformative force reshaping the landscape of efficiency, sustainability, and personalization. The evolving travel landscape introduces challenges for travel policies, booking processes, and fare visibility, necessitating a departure from outdated tools. The future demands agility and adaptability, which is embraced by CTM’s in-house product development teams and innovation pillars. With a focus on simplicity amid underlying complexities, CTM’s Lightning OBT is already leveraging AI and ML technology to deliver innovative recommendations and smarter search results, ensuring a seamless travel experience that aligns with both company-level objectives and traveler preferences and needs.

The potential for AI and ML to contribute to a more sustainable future in aviation is particularly promising, from optimizing flight operations to addressing environmental concerns like contrail formation and promoting the use of sustainable aviation fuel. As we embrace these technological advancements, CTM remains committed to delivering a travel experience that is efficient, sustainable, and personalized for every traveler and corporate travel programme. The future of travel is indeed being shaped by the seamless integration of artificial intelligence. We understand that we are at the beginning of an exciting new chapter in technology innovation, and we are excited about the opportunities that lie ahead for our customers and supplier partners.