Nathalia Vega, head of research at innovation consultancy Designit, says there is no limit to what airports can do if they learn from the successes and failures of AI.
The global market for AI in aviation is forecast to grow to $4.04 billion by 2032.
Implementing AI in airports will inevitably bring its own successes and challenges – and some will fare better than others.
What matters is how AI is being integrated into airports around the world: its impact on operational efficiency, passenger experience and safety, and the challenges faced in its implementation.
So which airports are ahead, who is making costly mistakes and who is simply doing something wrong?
The good: better efficiency and customer experience
Let us first look at London Heathrow And Amsterdam-Schipholtwo of the busiest airports in Europe.
AI supports air traffic control in both locations, especially in difficult weather conditions such as fog or heavy cloud cover.
Normally, these conditions would cause significant delays and risks, but thanks to AI-driven systems, these airports can now monitor and control air traffic with absolute precision, ensuring safe and efficient operations, even in poor visibility.
This advancement not only improves operational efficiency but also sets a new standard for safety in airport management.
Singapore Changi Airport has also committed to AI to improve runway management.
Its AI system continuously scans the runway for hazards such as debris or wildlife, a task that was previously done manually and was prone to errors.
Using high-resolution cameras and machine learning, the system detects even the smallest anomalies, reducing the risk of accidents and disruptions.
Changi’s proactive use of AI in this area sets a new standard that is likely to be adopted by airports around the world.
In the area of customer service, AI is also helping airlines and airports improve interactions with passengers.
United AirlinesFor example, uses AI to send personalized text messages with real-time updates on potential weather disruptions, including live radar links. This approach reduces uncertainty and has led to increased passenger satisfaction.
Similar, Lyon Airport has integrated AI into its customer service, with a chatbot that helps passengers with general queries such as flight status and directions to the terminal.
While the chatbot still faces challenges when dealing with more complex questions, its implementation underscores the growing demand for automated services, particularly during off-peak hours when human availability may be limited.
Lyon Airport’s use of AI in this function reflects a broader trend towards automation and meets the changing expectations of modern travelers.
The bad: Things are still not running smoothly
Although AI has proven to be useful, there are areas where AI implementation faces challenges, highlighting the need for continuous refinement and adaptation.
Air Canada has deployed an AI chatbot to handle bereavement policy inquiries.
Unfortunately, the chatbot failed to grasp the sensitive nature of these requests, resulting in frustration and misinformation regarding ticket refunds.
This case has highlighted that while AI is useful in many contexts, it is not yet able to respond to the emotional and nuanced needs of passengers faced with sensitive issues such as the death of a loved one.
In such scenarios, human interaction remains irreplaceable. AI should be used to complement human empathy and understanding, not replace it.
The Ugly: Ethical Concerns
However, the problems can go even deeper. One AI implementation that has raised ethical concerns is facial recognition.
San Francisco International Airport This was met with backlash, as there were fears that the inherent biases of AI could lead to discrimination against members of ethnic minorities.
There were also concerns about data and privacy, culminating in a citywide ban on the technology by public agencies.
Although the airport was exempt from the ban and still uses facial recognition technology today, the controversy highlighted the unease surrounding the use of artificial intelligence in airport security.
To successfully integrate AI without facing major backlash, airports need to learn from past mistakes and ensure that human interaction is retained where it is needed. AI is not the great replacement, as it is simply not designed to understand the intricacies of human nuance.
The flight route forward
We have now reached a point where AI has passed its infancy and there are already concrete use cases at airports and airlines.
However, to make the most of it, thoughtful implementation, continuous improvement and a balance between technology and the human touch are essential.
Before implementing AI technology, for example, the US TSA (Transportation Security Administration) faced several challenges, including inefficiencies in security screening processes, long wait times, and human error in detecting threats.
Because these procedures were performed manually, inconsistent results occurred, with security personnel sometimes missing prohibited items or incorrectly identifying harmless objects as threats.
In addition, relying solely on human judgement often resulted in bottlenecks, which in turn contributed to passenger frustration and delays.
The goal of introducing artificial intelligence was to solve these problems by improving the accuracy of threat detection, speeding up the screening process and reducing the number of false positives.
AI has served to improve these capabilities – and has not overtaken anyone in airport security.
The bottom line is that AI is a tool to augment human functions, not a replacement.
Successful integration requires keeping people in control, using AI to support and optimize processes, while ensuring that a human is always involved to manage exceptions and handle complex situations.
By learning from successes and failures, airports and airlines can master the complexities of AI implementation and create a more efficient and customer-friendly experience in the years to come.
This way, there are no limits to what you can do.
