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The Impact of Agentic AI on Airport Operations

The Impact of Agentic AI on Airport Operations
As airports around the globe pursue modernization, agentic artificial intelligence (AI) is emerging as a pivotal technology with the potential to transform fragmented operational tools into integrated, goal-oriented systems. In anticipation of the International Airport Review’s breakfast briefing in London during PTE on 18 March, Jordi Valls, Global Director at SITA Labs, outlined how agentic AI could fundamentally reshape airport operations, contingent upon the establishment of robust data infrastructures, governance frameworks, and sustained human oversight.
Advancing Beyond Traditional AI
Traditional AI applications in airports have predominantly centered on predictive analytics, dashboards, and alert systems. According to Valls, agentic AI represents a significant evolution beyond these capabilities. Unlike conventional AI, which primarily forecasts delays or assists with isolated tasks, agentic AI is designed to reason across multiple objectives, constraints, and operational roles. It can propose or autonomously execute coordinated actions by evaluating options and balancing trade-offs across various systems and teams. This transition from isolated, assistive tools to systems exhibiting goal-driven behavior is particularly consequential in the complex and time-sensitive environment of airport operations.
Partial Autonomy Within Operational Domains
While the concept of fully autonomous airports remains a distant prospect—complicated by regulatory, labor union, and safety considerations—agentic AI is expected to introduce partial autonomy in targeted operational areas. Functions such as disruption management, resource reallocation, and interdepartmental coordination stand to benefit from streamlined processes, with agentic AI serving as the integrative mechanism linking disparate systems. Valls emphasizes that progress over the coming decade will be measured not by fully unmanned operations but by reductions in operational friction and accelerated recovery times.
The Imperative of Unified Data Architecture
A critical prerequisite for the effective deployment of agentic AI is the establishment of a unified operational data architecture. Consistent and shared data regarding flight statuses, resource availability, and passenger flows are essential to prevent decision-making based on incomplete or conflicting information—a risk that could have severe consequences in safety-critical airport environments. Valls clarifies that a common operational data layer is not about centralizing data but about achieving alignment across systems. Neglecting this foundational step could lead to fragile automation, undetected errors, and erosion of trust among operational personnel, thereby exacerbating fragmentation rather than resolving it.
Immediate Applications and Benefits
In the short term, agentic AI is poised to deliver significant improvements in disruption recovery, turnaround coordination, and baggage exception handling. These operational areas are data-rich but often suffer from delayed or suboptimal decision-making under pressure. Agentic AI can integrate inputs from airlines, ground handlers, and airport operations to generate prioritized, real-time action plans, thereby reducing coordination delays and enabling more informed decisions even when data is incomplete.
For instance, during severe weather disruptions, agentic AI can continuously reassess priorities related to aircraft scheduling, crew legality, gate assignments, and passenger connections. Rather than relying on static contingency plans, the system can dynamically adjust operations by reallocating gates, resequencing services, alerting relevant teams, and flagging critical issues as they emerge.
Emerging Challenges and Industry Implications
The adoption of agentic AI also introduces new challenges for airports and airlines. Increased competition from AI-enabled rivals may compel industry players to reevaluate their strategic approaches and accelerate the development of advanced AI capabilities. Additionally, the growing reliance on AI heightens exposure to data scraping and cybersecurity threats, raising concerns about operational vulnerabilities and potential liabilities, particularly when third-party AI solutions are involved. As agentic AI becomes more deeply embedded in airport operations, the sector must address these risks through comprehensive governance, enhanced cybersecurity protocols, and clearly defined accountability mechanisms.
Agentic AI holds considerable promise for enabling smarter, faster, and more resilient airport operations. However, realizing these benefits will depend on meticulous attention to data integrity, sustained human oversight, and proactive management of emerging risks within an increasingly competitive and complex operational landscape.

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