The article titled “This airport robot will take your bags at the curb, check you in, and send you on your way” seems to indicate that we are now at the beginning of the “Full Service Restaurant” model at airports. In a typical full service restaurant, the waiter/ waitress shuttles between the table and the kitchen to pick up the order and deliver food; while the customer is static. However, in a Quick Service Restaurant, the customer is shuttling between the cash counter, kitchen (delivery counter) and the table. Traditionally, all airline counters have worked in the QSR mode, with the airline agents remaining static, and customers going from one station to another completing the check-in process, handing over their luggage, etc. With Leo and Kate, are we moving towards the full-service model of a restaurant, where the customer is at any location of his/ her choice, and these automated agents come to him/ her doing tasks for him/ her?
And how is this enabled? Some of the technologies at play that are making this a reality clearly fall in the domain of Artificial Intelligence. For example, let us analyze how Kate, the automated check-in agent, discovers where to set itself up:
a) Take a picture of the crowd standing in front of the counter
b) Recognize distinct faces in the image and count them up
c) If this number is above a threshold, then navigate to that area using robotics including Obstacle avoidance technology
d) Set itself up by coming to a halt, announcing that it is now available for helping passengers, and then initiating the process of checking-in
Image Processing and Robotics are two key AI technology enablers that are brought to play in the above and the business outcomes are: (a) faster turnaround time, (b) enhanced customer satisfaction. Human agents can be freed up to do tasks that are not routine, which truly require human intervention, which is usually for a minority of the passengers.
Let us analyze how this automated service could be further enhanced by including another AI technology enabler, i.e. contextual search.
Different people wanting to board different flights would all be standing in the same line. However, there would always be some late comers who would desperately want to jump ahead in the queue, so that they do not miss their flight due to the long lines. When a passenger joins the line in front of the check-in counter, let us assume that there is a Queue Management System (QMS) application, where he has to enter some minimal detail such as the flight number that he is planning to board. Based on this, the QMS system can issue a serial number (or token) to the passenger. Internally, the QMS system should keep track of the approximate number of persons in the queue by taking a difference between the most recently issued serial number and the one that was most recently serviced by the agent at the check-in counter. Based on this estimate of the number of persons in the queue, the system can now figure out if the person who has just joined the queue will complete the check-in formalities in a timely manner before his flight time. In case the system discovers that the time available for the passenger is less, it can alert the automated agent (Kate) to roll up to this passenger to get his formalities completed. This reduces the anxiety for the passenger and the check-in agents, thereby dramatically enhancing customer satisfaction. Thus, Kate’s usefulness will be significantly enhanced as it will not only help in automating some routine tasks, but will also specifically help those passengers who need its assistance the most. Thus, the context or the situation of the passenger is also used to determine who deserves such undivided attention from an automated agent.
Image recognition, Robotics, Contextual Search are all AI technologies that are maturing at a faster pace than one can imagine, and their applications in different customer situations are just waiting to explode.