From Personalisation to Prediction: What's Next for AI in Business Travel?

 

Business travel is going digital, but the next wave of change could be more than about personalizing travel. AI is now helping companies understand what travelers want, make the booking process easier, and suggest things based on what they’ve booked in the past. The next level is prediction, where AI could predict what a traveler or travel manager might need, before they even ask, by using patterns, real-time data and historical travel information.

Such a change could affect how business trips are booked and managed. Instead of waiting for travelers to look for flights, respond to disruptions or make arrangements manually, intelligent systems are increasingly able to predict potential needs and provide appropriate options in advance. When a company has a lot of people traveling on business, it can make for a more responsive and efficient travel experience.

How AI is making business travel personal

Digital travel is already highly personalized. Business travelers may have their favorite airlines, seating preferences, hotel categories, loyalty programs, budgets and special requirements. This information can then be used by artificial intelligence to tailor the recommendations rather than offering the same options to each traveler.

An AI-powered travel platform, for instance, can evaluate an employee’s past booking history and suggest similar morning flights and hotels near meeting locations for future trips. The traveler can receive recommendations that already take into account their known preferences and company travel policies, and they don’t have to begin each booking from scratch.

This will save time and make the booking experience more bespoke. Personalization, in contrast, generally reacts to data that already exists. Predict takes this a step further.

From Personalization to Prediction

Predictive AI looks at what it can see, and tries to predict what might happen next. In business travel, this may include past trips, future meetings, travel plans, information on your destination and current travel conditions.

For example, an employee is scheduled to attend a series of meetings in a number of cities over a period of several weeks. An intelligent system will know where and when those meetings are taking place and will flag any potential travel requirements before the employee even begins planning each journey. It may also highlight potential scheduling conflicts or suggest making arrangements earlier, when availability is likely to be limited.

The advantage of prediction is that it means travelers do not have to start every step themselves all the time. The system can help answer the question of What is likely to be needed next? rather than What do I need to book?

Outlook for Travel Disruption

One of the most practical use cases of AI is predictive, to know possible disruptions and solve them before they impact a traveler. Business travel plans can be badly upset by flight delays, extreme weather, congestion at airports and other operational problems.

A smart system can look at relevant travel data and identify itineraries that are potentially at risk. If the first flight is delayed and the traveler has a connecting flight that has a tight transfer window, the system may be able to detect the potential problem and suggest alternatives.

Similarly, if severe weather is forecast at a destination, travel managers may be given a list of travelers who could be impacted. This allows for potential problems to be addressed proactively, rather than waiting for the disruption to occur.

Prediction does not mean every prediction will be right. Instead, it gives companies better visibility to make travel decisions faster and smarter.

Guessing what travelers desire

AI might also become better at predicting practical needs when traveling on a business trip. The system could also detect a traveler arriving at a new destination and provide relevant information regarding airport transfer, local transportation or meeting point.

If the user is a frequent traveler, the system may use his previous travel patterns to give more relevant suggestions. Future recommendations may include preferences for booking airport transfers or hotels at a specific distance from meeting venues.

This could mean fewer repeat decisions when organizing corporate travel. Travel managers would also gain as systems can handle the simple requests, leaving human teams to take care of the more complex arrangements.

Company policies are a factor

You can't know what a traveler needs. You have to know about corporate travel. Business travel is also governed by company policies on budgets, approved suppliers, travel classes, sustainability requirements and other organizational considerations.

The AI system has to balance business rules and individual preferences. For example, a traveler may have a favorite hotel but the system still must determine whether that option fits within the approved company budget or travel program.

This balance is important because the most useful prediction is not necessarily the choice a traveler is most likely to make. This is an option that satisfies the traveller’s needs and the company’s policies and objectives

AI and the travel manager.

AI’s predictive power could change the role of the travel manager. Travel professionals may find they can spend less time on routine bookings and itinerary changes, and more time on exceptions, complex trips and strategic travel management.

Computers can read a lot of information very fast, but we still need humans to monitor computers. Travel managers can review recommendations, deal with unusual circumstances and make decisions where business priorities or traveler needs cannot be easily reduced to rules.

This human-AI relationship could be particularly helpful for MICE programs, which involve the coordination of multiple travelers, event schedules, accommodation, transportation and destination arrangements.

What Comes After Prediction?

One day, the shift from personalization to prediction could make travel management more proactive. In future systems, rather than just offering a choice based on past behavior, systems could anticipate possible needs, identify risks and prepare possible solutions before the traveler asks for them.

For example, an AI system might identify a potential scheduling conflict in a forthcoming itinerary, flag a likely connection problem and suggest alternative arrangements for approval. Much of the research and preparation could already have been done but the traveler would still be in charge.

It’s about the transition from reactive travel technology to systems that actively help the journey.

The Future of AI in Business Travel

The transition from personalization to prediction is likely to move slowly. Good data, clear travel policies and the right safeguards are needed before companies can hand off more duties to intelligent systems. travelers need to feel comfortable that the recommendations are relevant and that they can maintain control of important decisions.

The potential for AI to make business travel more proactive is its capacity to understand patterns, anticipate needs and identify potential disruptions earlier. But prediction should be an adjunct, not a total substitute, for human judgment.

So the future of business travel may be systems that know where a traveler has been and where they’re going to and what they may need along the way. Personalization will make travel more relevant. Prediction could make it more proactive. They have the power to change the way companies plan, manage and benefit from travel for business.