AI Enters the Airport: From Information Desks to the Apron-The Global Aviation Industry's Diverging Paths Toward Intelligence
Airport Frangible Flange,
Airport Runway End Light,
High A obstruction light,
High B obstruction light,
In September, South Korea's Incheon International Airport announced the official launch of an AI-powered baggage anomaly detection system, co-developed with the industrial AI firm POSCO DX. The system automatically identifies irregularities-such as two bags stacked together or luggage with extended handles-and diverts affected items to a manual processing area; Incheon has already filed a patent application for this technology. Meanwhile, on August 21, Beijing Daxing International Airport held a presentation on application scenarios for embodied AI, unveiling 19 robot-enabled use cases at once. These span a wide range of operations, including bird control patrols, runway inspections, terminal cleaning, passenger guidance, baggage handling assistance, and facility maintenance. According to various domestic media reports, Daxing Airport's intelligent customer service system has recently been made available to all passengers; since its trial launch, it has served 15,000 passengers, effectively bridging the gap in service coverage at manual information desks during peak hours.
These two airport cases illustrate two primary trajectories for AI implementation in the global civil aviation industry by 2026: Incheon has chosen to refine and deepen AI application within the highly standardized, clearly defined scope of baggage handling, whereas Daxing has deployed robots across diverse scenarios-from the airside to the terminal-effectively transforming physical space into a data-gathering network. These distinct paths reflect strategic choices shaped by differing market environments, regulatory frameworks, and industrial foundations, while offering valuable, mutually instructive examples for the industry.
Domestic Landscape: Large Models and Embodied AI-Moving from Front-End Service to Back-End Operations
The launch of the initial 19 robot application scenarios at Daxing Airport represents far more than the simple introduction of robots into the workforce. According to airport officials, these robots perform their assigned tasks while simultaneously collecting data-transmitting real-time information on passenger density, common inquiries, and congestion hotspots to optimize airport operational scheduling. The robots are no longer merely tools to replace human labor; instead, they function as mobile sensor nodes integrated into the airport's perception system, providing data-driven support for operational decision-making.
This approach is currently being adopted and expanded across multiple major hub airports in China. For instance, Shenzhen Airport has launched an AI travel assistant in its international arrivals area; powered by large model technology, it enables cross-language interaction and provides real-time answers to passenger inquiries regarding baggage tracking, entry procedures, transfer guidance, and connecting transportation. Some dining and retail outlets have introduced robot store managers and food delivery robots to serve passengers in innovative, intelligent ways, while devices such as mobility-assist robots and contraband collection robots have also been deployed to comprehensively facilitate the travel experience.
China Eastern Airlines has taken its pilot program at Pudong Airport a step further; in June 2026, it introduced service robots equipped with its proprietary "Huiyan" large model, covering functions such as self-service check-in, ticket rebooking, baggage inquiries, and transfer guidance. The "Little Camel" baggage transport robot boasts a maximum load capacity of 150 kilograms, and intelligent wheelchair robots have also entered field trials; these devices feature multimodal interaction capabilities and are helping to integrate various service stages of the passenger journey.
Explorations by small and medium-sized airports emphasize a more lightweight approach. On August 31, Huai'an Lianshui Airport in Jiangsu Province unveiled "Luanwa," its first digital employee. Aligned with the airport's "Xiangyu Pioneer" service brand, Luanwa is being gradually integrated into various operational aspects-ranging from passenger guidance and service inquiries to process optimization and intelligent interaction. For smaller airports with limited budgets and technical teams, digital humans offer a more practical entry point than large-scale robot deployments.
AI is also extending into core operational areas such as security and energy management. In early 2026, Kunming Airport piloted an AI-powered security screening image analysis system. By utilizing a database of tens of thousands of contraband images, the AI identifies dangerous items in real-time and provides reference information to security personnel, thereby shifting security operations from a primarily human-reliant model to a collaborative human-machine approach that combines manual oversight with technological safeguards. In July 2026, as part of its development into a "zero-carbon airport," Ordos Ejin Horo Airport launched an AI-powered, holographic 3D smart energy management platform. Integrating digital twin technology, the airport established a proprietary zero-carbon energy system that combines heating and cooling-replacing traditional gas boilers and Freon-based refrigeration systems. This transition reduced annual energy consumption for heating and cooling by 60% and cut operating costs by 50%, setting a benchmark for AI applications in green, low-carbon operations.
Overall, AI applications in domestic airports are moving beyond the initial stage of mere demonstrations and extending into operational areas such as apron monitoring, airspace clearance verification, energy dispatching, and AI-assisted security image screening. The convergence of large models and embodied AI is currently a major trend; implementation focuses primarily on human-machine collaboration and integration with existing operational systems.
International: Biometrics and Deep-Dive Applications, Advancing Steadily Within Regulatory Frameworks
Unlike the systematic, broad-scale rollout seen in domestic airports, AI implementation at overseas airports is characterized by a "deep-dive" approach into specific use cases, with biometrics being the most prominent area of focus.
Singapore Changi Airport is advancing passport-free clearance. Leveraging a unified biometric platform, travelers can complete the entire process-check-in, security screening, immigration, and boarding-simply by scanning their faces. The goal is to automate 95% of entry and exit processes by early 2026 and reduce the clearance time for a single passenger to under 10 seconds. AI analyzes passenger flow and dynamically allocates security resources, thereby shortening queues while encouraging longer stays in commercial areas, effectively converting transit efficiency into commercial revenue. Dubai International Airport has introduced a "Red Carpet" seamless biometric clearance tunnel; travelers can complete verification simply by walking through without presenting documents. The process takes 6–14 seconds per person and supports simultaneous passage for multiple travelers.
While accelerating the adoption of biometric technology, European airports are ensuring strict compliance with local regulatory requirements. Rome Fiumicino Airport has deployed Outsight's 3D LiDAR and "Physical AI" solution at scale to capture real-time passenger flow data, detect queues and congestion, anticipate passenger volume surges, and coordinate on-site resources; the system is designed with privacy-preserving anonymization. Frankfurt Airport has implemented the APIDS automated prohibited-item detection system, integrating AI algorithms with CT scanners; this allows passengers to leave laptops and liquids in their bags while the system automatically identifies high-risk items, triggering manual secondary screening only for suspicious objects. Security staff retain final decision-making authority, enabling them to focus on handling high-risk items, resulting in improved throughput and reduced false-alarm rates during morning peak hours.
Baggage handling represents another key area for AI implementation abroad. South Korea's Incheon Airport is set to launch the world's first International Remote Baggage Screening (IRBS) system in January 2026, enabling automated security screening for checked baggage and eliminating the need for transit passengers to reclaim their luggage. SITA's *Baggage IT Insights 2025* report reveals that the global rate of mishandled baggage dropped to 6.3 bags per 1,000 passengers in 2024-an 8.7% improvement over the 6.9 bags recorded in 2023-driven by AI-powered sorting, automated tracking, and cross-stakeholder data sharing. A survey covering 155 airlines and 94 airports found that 44% of carriers have fully deployed baggage tracking systems, while 41% are currently implementing them.
Smart technology adoption at Japanese airports is directly addressing ground-handling labor shortages. In May 2026, JAL Ground Service, in partnership with GMO AI & Robotics, will begin testing humanoid robots-including models from Unitree and UBTECH-at Haneda Airport to handle baggage sorting and cargo loading. The trial will run until 2028, with the primary goal of validating the feasibility of using robots to supplement the workforce within a safety-compliant framework. North American hubs are deploying AI with a focus on biometrics and operational efficiency. For instance, the TSA's "PreCheck Touchless ID" facial verification program is being piloted at major hubs-such as JFK, Atlanta Hartsfield-Jackson, and Washington Reagan National Airport-in partnership with CLEAR's biometric systems to implement seamless identity verification and conduct technical testing for managing passenger flows during major events.
**The Logic Behind Divergent Paths and Opportunities for Integration**
A comparison of domestic and international cases reveals that AI deployment in Chinese airports is characterized by a systematic approach: large-scale models, embodied AI robots, digital twins, AI-driven energy management, and AI-assisted security screening are being advanced almost simultaneously, covering the entire value chain from passenger services and ground operations to energy management and security. This pattern offers two key advantages: first, the domestic civil aviation industry is actively implementing "AI + Civil Aviation" policies, giving hub airports both the incentive and the resources for systematic deployment; second, the cost of locally deploying domestic large-scale models is relatively manageable, facilitating large-scale application at the airport level.
Overseas airports have primarily focused their AI implementation on three key areas: biometrics, automated baggage handling, and AI-assisted image analysis for security screening. These use cases feature clearly defined boundaries, specific objectives, and quantifiable outcomes-biometrics reduce processing times; baggage AI minimizes jams and misrouting; and AI security screening boosts throughput-all of which translate directly into improved passenger satisfaction or reduced operational costs. Overseas airports tend to be more cautious regarding the development of large-scale systems like holistic scheduling and digital twins, primarily due to privacy compliance concerns. Regulations such as the EU's GDPR and relevant US privacy laws impose strict limits on the collection, storage, and use of facial and passenger behavioral data, and cross-system data integration must meet these compliance standards. Consequently, overseas airports prefer to refine and excel in specific, isolated use cases where compliance boundaries are clearly defined.
Each approach has its own advantages, and there is significant scope for mutual learning. The advantage of a systematic domestic deployment lies in the diversity of application scenarios and strong policy support, creating favorable conditions for pioneering cross-system integration. Conversely, the advantage of deep, focused deployment in overseas markets lies in thoroughly validated commercial viability and a relatively clear path from pilot programs to standardized replication. Future development will likely see a convergence of these two approaches: systematic deployments must continue to solidify foundations regarding data interoperability and cost-benefit ratios, while focused deployments will gradually expand across the entire value chain as regulatory frameworks mature.
Industry Development Recommendations
Whether domestically or abroad, the implementation of AI in airports is fostering a consensus that points the way for future efforts.
First, uphold the bottom line of aviation safety and insist on human-machine collaboration. In civil aviation scenarios, AI is positioned to assist decision-making rather than replace human personnel; for critical safety processes-such as security screening image analysis, apron monitoring, and operational scheduling-AI provides references, but the final decision-making authority must remain with humans. For instance, the APIDS system at Frankfurt Airport reserves final decision-making power for security officers, and the AI image analysis at Kunming Airport serves as a reference for staff; this principle of human oversight should be consistently applied across all AI projects. The value of AI lies in mitigating human fatigue and errors associated with repetitive, high-cognitive-load tasks, thereby freeing up personnel to focus on high-risk assessments and the handling of anomalies.
Second, place high importance on privacy compliance and mitigate data risks through technical solutions. The boundaries regarding the use of facial data and passenger behavioral data are a shared concern for regulators worldwide. A key factor enabling the rapid rollout of biometric clearance systems at airports like Changi, Dubai, and Heathrow is the widespread adoption of tokenization schemes-facial data is compared locally without centralized storage, or only one-time digital tokens are generated, effectively reducing the risk of data breaches. Domestic regulations, such as the Personal Information Protection Law, also impose strict requirements on biometric data. It is recommended that airports incorporate privacy protection into the design phase of AI projects, utilizing technical measures such as local processing, data desensitization, and data minimization to unlock the value of data while ensuring compliance. Third, start with specific, high-pain-point scenarios and steadily expand the scope of application. Industry experience shows that applications with clearly defined boundaries and measurable outcomes are more likely to yield viable business models and gain acceptance from operational teams. Airports are advised to prioritize AI initiatives targeting areas with significant pain points, robust data foundations, and quantifiable returns. By solidifying single-point applications before gradually expanding into adjacent processes, airports can avoid the pitfalls of overambitious, all-encompassing projects. The initiative at Daxing Airport to have robots simultaneously perform data collection tasks represents a noteworthy trend; if the data collected by these robots can continuously feed back into operational optimization, their value extends beyond merely replacing human labor-they become vital data entry points for the entire operational system.
Fourth, accelerate the establishment of mechanisms for cross-entity data sharing. Airport operations involve multiple stakeholders-including airlines, air traffic control, ground services, security screening, and border control-and AI models require high-quality, real-time data inputs to deliver maximum utility. It is recommended that industry regulators and hub airports take the lead in exploring standardized interfaces and collaborative mechanisms for cross-entity data sharing, while ensuring data security and the protection of commercial privacy. Such measures will pave the way for the deep integration of AI into areas like operational scheduling and collaborative support services.
Fifth, establish a clear cost-benefit assessment framework to facilitate the transition from pilot projects to routine operations. Many airport AI initiatives are currently in the pilot phase; scaling these up requires careful consideration of the return on investment. Airports should establish a quantifiable assessment metric system at the project initiation stage, factoring in full-lifecycle costs-such as procurement, maintenance, scheduling system development, staff training, and contingency measures for exceptions. Simultaneously, they should objectively evaluate comprehensive benefits-including efficiency gains, error reduction, and improved passenger satisfaction-to provide a clear financial rationale that supports the long-term sustainability of AI investments.
