In 2016, the global aviation analytics market was valued at $2.35 billion. By 2023, that figure is projected to reach $7.18 billion—underscoring how analytics and AI are transforming the aviation industry.
Whether it’s ticket purchase, seat selection, boarding, or ground transportation, every component of the passenger journey generates data. Mining this information allows airlines and other stakeholders to enhance the customer experience and improve operational efficiency, reaping significant financial rewards in the process. In fact, McKinsey states that the global travel industry is poised to gain over $400 billion per year in efficiency gains alone through the adoption of AI.
Below are some other examples of how AI and analytics stand to create a significant positive impact within the aviation sector:
Drawing on IoT sensors and video footage, passenger analytics tracks passenger volume and movement through an airport. Applying machine learning (ML) models to this data enables airports to optimize line management at security checkpoints and predict peak traffic periods so that airport retailers can plan accordingly, among other benefits.
Computer vision is an emerging AI application within airports. The technology uses cameras and ML algorithms to monitor complex ground servicing activities, identify safety concerns in real-time, or notify the appropriate resources when a service is taking longer than expected. Other benefits of computer vision include increased aircraft turnaround times and enhanced safety conditions for ground crews.
The global airline industry has numerous challenges to consider from a risk management perspective. One of these is addressing the perennial risk of fatigue facing pilots and crew due to the constant change of time zones, scheduling delays, and other unforeseen factors. Data analytics is increasingly being utilized to mitigate these risks and help schedulers better plan for these variables.
Another benefit of AI and analytics is the development of a forecasting model that can help airlines assess whether and when to take various actions such as adjusting fares, increasing available seats, or introducing new routes.
Unexpected delays and cancellations due to unplanned maintenance can be a significant cost center for airlines, with some estimates stating the latter is behind 30 percent of aircraft delay time. Applying predictive analytics to fleet technical support can mitigate this concern, allowing airlines to proactively address issues before they become big enough to cause delays. Analytics can also help optimize runway bandwidth, aircraft type, flight routes, and other fleet management considerations.
For more on what analytics and AI are doing in the aviation sector, check out this video of Emiliano Sorrenti, CIO of Italy’s Aeroporti Di Roma, discussing how visual analytics has transformed operations at his airport.