How Agentic AI Can Improve Airline Baggage Handling ?

Agentic AI holds immense potential to revolutionize airline baggage handling, transforming a traditionally labor-intensive and error-prone process into a highly efficient, accurate, and transparent operation. Here’s how it can help:

1. Enhanced Tracking and Real-time Visibility

Intelligent Tagging and Scanning: Agentic AI can leverage advanced vision systems (like computer vision with deep learning) to accurately read and interpret baggage tags, including subtle blemishes and dents for unique bag identification (baggage biometrics). It can also classify bag types (e.g., duffle, wheeled, irregular shapes) to prevent jams.

Autonomous Monitoring and Alerts: AI agents can continuously monitor bags as they move through the baggage handling system (BHS) using RFID, Bluetooth, and advanced sensors. If a bag deviates from its intended path, is delayed, or appears lost, the agent can autonomously trigger alerts to relevant staff, preventing misrouting and minimizing mishandling incidents.

Proactive Information for Passengers: Agentic AI can seamlessly integrate with airline mobile apps to provide passengers with real-time updates on their bag’s location, estimated arrival at the carousel, and even proactive notifications if there’s a delay or issue, enhancing transparency and reducing passenger anxiety.

2. Automated Sorting and Routing

Robotic Sorting and Loading: Agentic AI can control and coordinate autonomous robots and baggage handling BOTs that can identify, pick up, sort, and transport bags to designated areas or Unit Load Devices (ULDs). These robots can operate 24/7, reducing physical strain on human workers, minimizing errors, and significantly increasing throughput.

Dynamic Routing Optimization: AI agents can analyze real-time data on flight schedules, gate changes, aircraft types, and baggage flow patterns to dynamically optimize bag routing within the BHS. This ensures bags reach the correct aircraft or transfer point efficiently, even amidst disruptions.

Non-Conveyable Item Detection: AI-trained edge devices can examine baggage flow and automatically detect non-conveyable items (e.g., oversized bags, bags with long straps that cause jams), flagging them for manual intervention or specialized handling to prevent system disruptions.

3. Predictive Maintenance of Baggage Systems

Anticipating Failures: Agentic AI can continuously monitor the health of critical BHS components (conveyor belts, sorters, scanners, motors) by analyzing sensor data (vibration, temperature, energy consumption). It can predict potential equipment failures before they occur, scheduling proactive maintenance to prevent costly breakdowns and system downtime.

Optimized Maintenance Scheduling: By accurately predicting maintenance needs, AI agents can help schedule repairs at optimal times, minimizing operational disruptions and ensuring the continuous flow of baggage.

4. Enhanced Security Screening

AI-powered Threat Detection: Agentic AI can enhance the accuracy and speed of baggage screening. Machine learning algorithms can analyze 3D images from CT scanners to quickly identify prohibited items or suspicious anomalies, reducing the need for manual inspections and expediting the screening process.

Automated Anomaly Flagging: AI agents can be trained on vast datasets of security incidents to recognize patterns associated with threats, automatically flagging suspicious bags for further human review, thereby bolstering security measures.

5. Improved Resource Allocation and Efficiency

Staff Optimization: By predicting peak baggage loads and identifying potential bottlenecks, AI can help airports and airlines optimally allocate human staff to areas where they are most needed, ensuring efficient operations and reducing waiting times.

Streamlined Processes: By automating repetitive tasks, agentic AI allows human staff to focus on more complex issues, problem-solving, and direct customer interaction, improving overall operational efficiency and staff satisfaction.

Reduced Costs: While initial investment in AI systems can be high, the long-term benefits include significant cost savings from reduced mishandling claims, optimized resource utilization, lower labor costs due to automation, and minimized downtime from predictive maintenance.

Challenges to Consider

Data Integration: Effective agentic AI requires seamless integration of data from disparate systems (check-in, flight schedules, BHS, security, customer databases).

Legacy Infrastructure: Many airports have older baggage handling systems that may require significant upgrades to integrate advanced AI technologies.

Security and Trust: Ensuring the cybersecurity of autonomous AI systems and building trust in their decision-making is paramount.

Ethical Considerations: Defining accountability when an autonomous AI system makes an error or a decision with significant consequences.

In essence, agentic AI will transform baggage handling from a reactive process to a proactive, intelligent, and highly automated system, leading to fewer lost bags, faster processing times, lower operational costs, and a significantly improved passenger experience.

Author Details

Sajin Somarajan

Sajin is a Solution Architect at Infosys Digital Experience. He architects microservices, UI/Mobile applications, and Enterprise cloud solutions. He helps deliver digital transformation programs for enterprises, by leveraging cloud services, designing cloud-native applications and providing leadership, strategy, and technical consultation.

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