SkyLink CEO and co-founder Atyab Bhatti is back with another slightly high-tech posts about artificial intelligence.
Artificial Intelligence is changing the way we approach personalization across industry verticals, including corporate travel. By leveraging generative AI, organizations can provide personal, delightful experiences for their travelers, enhancing satisfaction while meaningfully reducing costs.
Machine learning is an AI concept that has been in the press for longer than generative AI. It’s important to understand the differences between them and how they can complement one another to create distinctive, personalized experiences.
Machine learning is a subset of AI that focuses on computers learning from data without being explicitly programmed to that specific data set. ML algorithms analyze swaths of data to find patterns and make predictions based on those patterns. You can think of ML as a more traditional form of AI. A good example is how major social media sites show you ads; they analyze your browsing patterns to map what they think you’d like to see.
Generative AI, on the other hand, is a branch of AI that involves creating new content based on learned patterns in human language.
Combining ML and GenAI can deliver highly personalized experiences for corporate travelers. Here’s how it works:
Data collection: GenAI-powered assistants can engage in natural conversations with travelers, collecting valuable information about their preferences, needs and past experiences. This data can come from preferred airlines and hotels, and general behaviors like preferring airports nearby to work sites.
Preference persistence: ML algorithms can analyze the data and create detailed profiles. These profiles capture preferences and patterns, allowing the system to make recommendations based on historical behaviors.
Continuous learning: As travelers interact with the AI-powered system over time, it continues to learn and refine its understanding. Each conversation contributes to a more accurate and comprehensive profile, leading to increasingly personalized experiences over time.
To illustrate the potential of AI-driven personalization, consider the analogy of a dedicated VIP agent at a TMC. This agent remembers every conversation, building an understanding of the traveler’s needs over time. As the relationship grows, the agent becomes more adept at anticipating the traveler’s requirements without the need for explicit direction.
Now, imagine scaling this level of personalization across an entire enterprise for every traveler. The result is a traveler journey that is customized for each individual, anticipating preferences at every step.
A great example of this is sentiment-driven personalization. Traditional machine learning allows for the surfacing of relevant content to travelers but not necessarily based on how they like to travel — for instance, preferring to fly United on certain routes or favoring specific airport lounges, which can influence their choice of airports. Generative AI enables us to understand both how and what people prefer, unlike traditional ML, which really only addresses the what.
Achieving this level of personalization requires expertise in both GenAI and ML technologies. Investing in these capabilities will differentiate good from great. Personalized experiences don’t just lead to happier travelers but also better experiences, employee satisfaction and operational efficiency.
In the next part of our series, we’ll discuss the inner workings of transformers.
This Op Ed was created in collaboration with The Company Dime‘s Editorial Board of travel managers.
Fascinating! And I guess if the attention window includes travel policy, HR policy, environmental policy, risk feed, reason for travel info and rules, plus, plus plus, and if you play around with the temperature, which is perhaps a setting open to company policy decision, you’ve got one hellava powerful booking system. Wow. Who needs a pre-trip booking process? Who needs a stand-alone duty-of-care system? I want it!