Brandon Strauss of CapTrav and KesselRun Corporate Travel Solutions explains why he’s in “watch and learn” mode when it comes to generative artificial intelligence. 


Claude Shannon, a computer scientist and founder of information theory, published “A Mathematical Theory of Communication” in 1948, which defined how messages are carried from sender to receiver. This theory became critical to understanding communication flow, the meaning and intention of words, and, in practice, how language must be assimilated for artificial intelligence applications to work. Among other things, the theory states that any information transmitted from sender to receiver can be mathematically sorted between empirical information and semantic content. In other words, there is a scientific distinction between the information exchanged and the meaning of that information. One of the conclusions drawn is that while the two can be viewed as exclusive, in practice, a message would not really carry any meaningful information without the shared semantics. To a large degree, Shannon illustrates the key hurdles that generative artificial intelligence must overcome to be successful for corporate travel applications. 

Industry suppliers and new technology providers today offer next-level machine learning capabilities that some inaccurately label generative AI. Applications that pull data out of reservations or apply historical travel patterns to support new bookings seem to be the next and better iteration of common functions, such as online booking, chat, reporting platforms and mobile applications. These examples improve on pre-existing machine learning capabilities but don’t reach the level of traditional artificial intelligence — much less the humanistic, semantics-level decision-making ability that defines generative AI. When we hear travel technologists reference the ability to “contextualize” intent with their products, it dramatically oversimplifies the semantics required under Shannon, does not paint an accurate description of services available and is not close to market-ready.

Brandon Strauss
KesselRun Corporate Travel Solutions partner and CapTrav co-founder and president Brandon Strauss

The problem of semantics in travel gets even more complex as you dig deeper. Not only is there a requirement to marry information to semantics, but the language itself must match to succeed. For a travel application, the language concerned here can be thought of as text, chat, phone or email. An email picked up by an agent and answered with a phone call potentially violates the Shannon theory, which means neither the information nor the semantics of the message is accurately transferred. Understanding how language has intention and conveys meaning is a hard problem to solve for a computer. Yet, intention, meaning and raw data are all real-world aspects that must be considered. Have you ever heard the phrase, “a difference that makes a difference”? It came from the idea that language and communication are well-defined and measurable, even if we don’t always understand how meaning and intention fit into a particular application. 

Successful generative AI applications will understand the semantics of both business and leisure travel for a particular travel policy and individual. It will consider personal aspects of a traveler’s life, including every variable that a traveler may consider when making a travel decision. Generative AI will assimilate predictive analytics with semantics to provide the traveler with granular, sensible choices. Effective generative AI will proactively consider the multitude of variables that a traveler would consider as they anxiously watch a gathering storm through a conference room window 1,000 miles from home, for example. 

Will my flight be canceled? Can I keep my hotel one more night? Will I make it to Billy’s softball game? I have that dinner tomorrow that I’d need to cancel. I need to be in Baltimore in three days. Perhaps wait a day and then just go to Maryland? I have a conference call at 3:00 p.m. tomorrow. I can’t sit in a middle seat because of my back issues.

Imagine the power of harnessing all this information using predictive analytics along with the interpretation of personal data, such as calendars and historical travel data, to provide proactive, semantic-driven choices while the worried traveler gazes from their desk chair. 

Whereas most spend categories within organizations can probably utilize generative AI with readily available data points and fewer semantics, I think business travel will be challenging. Corporate travel has always been a stick-and-carrot type of spend category, where the company takes away liberties with policy while acquiescing for dozens of reasons using exception codes. Travel policy, traveler choice, a myriad of travel options and data privacy all play a role. The recent European Union Artificial Intelligence Act, which will put substantial limitations on the use of AI in Europe, may act as a world model for laws surrounding AI applications. Will these applications even be legal in Europe? What will California’s Consumer Privacy Act say about all of this? Successful applications of generative AI must overcome enormous data challenges while requiring access to very personal information. 

Given the industry buzz and excitement around generative AI, it’s easy to envision how travel applications can be transformative. Every sea change must start with a ripple before it becomes a wave. Until then, I’ll continue to watch and try to learn. I get it. I just don’t see it, and I’m skeptical.  


This Op Ed was created in collaboration with The Company Dime‘s Editorial Board of travel managers.

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