Running an airport is its own special form of choreography. Every minute, thousands of people, systems, and processes must align to keep passengers moving safely and on time. Yet even as passenger numbers hit record highs, much of this orchestration still depends on human interpretation of fragmented data across systems.
For decades, airport technology has focused on automation – the collection and display of information via dashboards, databases, and platforms such as the Airport Operational Database (AODB) and Airport Management System (AMS). These tools remain essential, but they largely describe what’s happening right now rather than why, or what, should happen next.
Integrating AI safely into legacy airport systems
That’s where AI can make a difference. Airports are beginning to integrate large language models (LLMs) into existing systems, adding an intelligent layer that can understand context, interact naturally, and recommend actions. These reasoning systems connect with tools like AMS or AODB, allowing operators to communicate through simple text – even WhatsApp messages – as if messaging a colleague in the field. A team member might ask, “What impact will the delay on Ocean Flight 815 have on baggage?” and get a fast, data-backed answer drawn from multiple systems.
In a disruption scenario, this capability is transformative. A reasoning model can generate operational plans in seconds by comparing data from flight schedules, weather feeds, and IoT sensors. As long as its context window – in other words, its ability to remember and interpret relevant details – is managed properly, results are reliable and decisions can be made in moments instead of minutes or hours.
From dashboards to decision-making
Dashboards have historically displayed data on arrivals, departures, and resource allocations, giving airport management teams a shared overview of what is happening. But that picture is static. When an unexpected delay is reported – due to bad weather or a late flight – human operators hunt across systems to understand what went wrong and coordinate responses manually.
AI is changing that. By integrating data from IoT sensors, AODB systems, flight schedules, and weather feeds, airports can now build reasoning layers that understand context. These systems see the whole operation, not just its parts. They can orchestrate the workforce dynamically by sending instructions over text, answering follow-up questions, and logging every interaction automatically as part of the operation record. If a worker shares a photo or reports a missing bag’s location, the systems can store the data in the right database and alert the relevant teams according to established procedures.
Smarter AI airport systems
This new generation of AI reasoning systems learn relationships, infers intent, and proposes solutions. This means that AI can spot issues like rising passenger density and suggest how to redeploy staff or shuffle gate allocations before these problems escalate.
Research teams and technology partners are exploring how these new systems can reshape the airport backbone. Early prototypes have shown how AI can connect once-siloed platforms into a single, context-aware system linking data, logic, and human decision-making. The goal is not to replace people but to amplify their capacity to respond quickly, confidently, and safely when conditions change.
Responsible intelligence
With new capabilities come new responsibilities. In mission-critical environments such as airports, trust and transparency are paramount. Every AI-driven recommendation must be explainable. Operators need to see why a system has reached its conclusion, what data it has used, and how confident it is in its decision. Traceability and human-centred design are essential safeguards for truth.
Explainability and auditability must be embedded from the start. Human oversight should remain part of every loop, ensuring AI supports rather than replaces operational judgement. Collaboration between engineers and algorithms should be based on visibility, not blind trust. Only then can AI earn a lasting role in airport operations.
Airports are becoming testbeds for intelligent infrastructure. Early adopters of explainable AI are proving how reasoning systems can scale safely, reducing coordination time, strengthening resilience, and ensuring accountability. As these systems mature, AI will become the connective tissue helping every system communicate, learn, and adapt. The question will no longer be whether airports can keep pace with global demand – but how intelligently they can adapt when every second counts.
Jordi Valls is the director of SITA’s Innovation Lab.
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