Airports are increasingly adopting new technologies and relying on artificial intelligence (AI) to improve customer experience.
Fremont, CA: Technology is radically altering the way businesses interact with their customers, and thus the aviation industry is no exception. Data and its use transform airline operations from pre-flight to post-flight, comprising ticket purchase, seat selection, luggage, boarding, and ground transportation. The critical data is collected along with the many components of a passenger's trip, allowing organizations to make informed decisions toward operational productivity and a good user experience.
While the rise of artificial intelligence in travel can get linked to various variables, one of the primary motivators has been a shift in client expectations. Because of the experiences provided by major technology companies such as Google, Amazon, Apple, and Facebook, travelers nowadays are anticipating increasingly seamless and personalized experiences.
Another significant driver of AI in travel is the aim to boost efficiency by automating mundane chores. While chatbots are the most visible manifestation of AI-driven efficiency, machine learning is also being implemented behind the scenes, resulting in gains that may be felt rather than seen. Internet of Things (IoT) devices* in aeroplanes or airport halls generate massive amounts of data, and organizations are using predictive analytics to optimize anything from security wait times to maintenance activities.
The potential of AI in travel
It's easy to think of AI in terms of business, but it's crucial to remember that the core of AI's potential in travel is to improve the travel experience. For example, artificial intelligence may be an excellent tool for identifying and solving traveler pain points and creating seamless experiences for travelers and suppliers, from booking sites through boarding gates. In other words, AI has the potential to make travel more intelligent.
AI in airports
Hundreds of millions of planes, passengers, and workers pass through airports every year. One of the critical purposes of AI deployment in airports has been to ensure the smooth movement of people, equipment, and baggage:
Passenger flow is at the heart of airport operations, and passenger analytics employs video and IoT to measure passenger volume and movement through a terminal. In addition, airports may use machine learning models to optimize queue management and checkpoint resource management in real-time and predict peak footfall intervals such that airport businesses can tailor their retail outlets.
Computer vision, a technique that utilizes cameras and machine learning algorithms to monitor complicated ground servicing activities, detect safety hazards in real-time, or sound alerts when a service takes longer than intended, is one of the emerging AI applications at airports.