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How AI is Revolutionizing The Travel Industry.


A storm is coming. One that might possibly change the world for good; the same way we started using electricity for anything and everything back in 1882. That storm is called Artificial Intelligence (AI); and  every single working industry, in one way or another, is doing its best to make good use of it, including the travel industry.

While it’s true that the ideology and Science behind AI has been around for many years, it’s only now that we are really starting to see companies actually diffusing it into their regular work-flow.

In short, how a particular AI generally works is that it studies past data about a particular subject and tries to learn on its own the hidden patterns and trends. For example, a hotel’s dataset might show that Brazilians like to book expensive rooms in the month of June. A good AI would take notice of that, along with countless other patterns, that might not make logical sense to us, humans, but are essential to make predictions of how future customers would act.


The main reason for the sudden popularity is that AI is more ‘usable’ now. Reasons for that include:

  • Data is now in abundance and continue to increase exponentially.
  • The cost of storing data is decreasing exponentially.
  • Data gathered from IOT (Internet of Things) is available now in almost every electrical device.
  • The spread of computer hardwares which can run complicated algorithms quickly.

See the below chart as an example of the exponential growth of data:

Zooming in on the travel industry, we can find several examples on how AI has been particularly disruptive. Some of these examples are dynamic pricing, railway booking service, disruptive management and time series forecast.

The first example is PROS ; a company that utilizes an idea called dynamic pricing. PROS provides technology for popular airlines around the world, where the AI would first study consumer behavior on which flights they like to book, when and where. Then, when a future consumer wants to book a flight, it would automatically provide prices that he would be most comfortable with. For example, consumers on low budgets might be provided with lower priced flights, while consumers who were previously unhappy on cheap flights, might be provided with more expensive, but much more comfortable flights.

Using this, PROS have reported 50% less response time, 50% less mainframe usage, 100% increase availability in requests and almost 0% rejected traffic. In other words, consumers don’t need to spend as much time and are always able to find the best offers.

Another company is called Trainline, where they say you can “Save an average of 43% when you book tickets in advance.” This UK based company provides an online rail booking service, after gathering all kinds of consumer behavior data. Future train passengers would then be provided with tickets they are most likely to choose, while informing them what the cheapest price would be, how many tickets are left, and also notify them in advance when new tickets are available.


Disruption Management is another great example. A quick look on Flight Aware might give you an idea of how many flights are canceled or disrupted each day. The Australian Airlines, Qantas, decided to try to use AI to combat these issues. The AI would collect data from airports, including weather and current delays, and predict in the future if a disruption will happen and how. Furthermore, the AI would then automatically make a back-up plan that would be most convenient for the passenger to take.

Qantas reported that using this, “15 out of 436 Qantas flights (about 3.4 percent) were canceled, as compared to 70 out of 320 (22 percent) flights by Virgin Australia [another airline]”.

One further example is flight price prediction using time series analysis. AltexSoft developed an AI tool that would analyze changes, trends, seasonality of prices over time. Then, it would use that to give recommendations to the consumer of when to book a flight, whether he should wait for the price to go down, or if waiting is actually too risky. An example is shown below:

How dlc. uses AI

Here at dlc. we have a data science team dedicated to provide technology to travel agencies that make use of AI wonders. Examples of how we use AI include:

  • Dynamic Reports – Automatically providing ease-of-use charts that automatically calculates averages for numerical data (such as prices), as well as counts of categorical data (such as the total number of people for top nations). 
  • Leads Ranking – After receiving data containing consumer requests, this aspect would predict which are most likely to confirm a reservation and which requests are not. 
  • Time Series Forecasting – Given historical data, we can provide predicted values to travel agencies that would forecast future Revenue or total number of customers over time. 
  • Chatbots – Automated chat machines that can understand typed up words and sentences and communicate with customers to provide sales services.

…and much more in the future.