AI-Ready Airport Cargo Operations: A 10-Point Checklist for Visibility, Efficiency, and Resilience

The air cargo industry stands at an inflexion point. According to the Federal Aviation Administration, U.S. air cargo operations generated $106.5 billion in economic output in 2022, supporting over 1 million jobs. Yet despite this critical importance, airport cargo communities continue to grapple with fragmented systems, paper-dependent processes, and limited real-time visibility across their supply chains.

For airport executives responsible for cargo operations—whether in dedicated cargo roles, concessions, commercial development, or IT—the question isn’t whether to embrace digital transformation. It’s whether current operations are truly ready to leverage the next wave of AI-powered capabilities. As Rajan Subramanian, Chief Product Officer and Chief AI Officer at Kale Logistics Solutions, puts it: “We are in exciting times where literally I can prototype five different product ideas in five different weeks and choose two of them to go forward with. We’re living in that age and time where that’s never—I’ve never experienced this.”

Drawing from operational experience across 140 airports in 40+ countries, a consistent pattern emerges: airports that successfully implement AI capabilities don’t start by replacing existing systems—they start by building the digital foundation that allows AI to work with what they already have. This checklist provides airport cargo decision-makers with a practical framework for assessing AI readiness. The good news: airports don’t need to implement everything at once. Many are finding success by starting with high-impact modules that address immediate pain points, then expanding their digital ecosystem over time.

1. Establish a Unified Data Foundation
Before AI can deliver meaningful results, cargo communities need integrated data flowing between stakeholders. This means connecting airports, airlines, ground handlers, trucking companies, freight forwarders, government regulators, and customs brokers on a common platform. The goal is to eliminate information silos that force manual data re-entry and create delays.

2. Digitize Documentation End-to-End
According to IATA, each air cargo shipment carries an average of 30 document types and over 100 copies. This paper burden creates bottlenecks, errors, and opacity. AI readiness requires transitioning to electronic airway bills, digital delivery orders, and, depending upon the trade lane, automated manifest and security pre-departure and pre-arrival filings. The payoff is substantial: airports implementing comprehensive e-AWB systems have reduced processing time by up to 50 percent, while achieving data accuracy rates exceeding 98 percent.

3. Implement Intelligent Truck Slot Management
For many airports, truck slot management represents the ideal starting point for digital transformation. This module addresses one of the most visible and costly pain points in cargo operations: landside congestion. AI-based truck slot management systems use predictive analytics to optimize dock scheduling, reduce congestion, and improve resource planning.
Before implementing such systems, some airports experienced truck wait times exceeding four hours. After deployment, wait times dropped to under 45 minutes—a transformation that allows drivers to do more within their hours-of-service-constrained workday, reduces wasted fuel from idling, and enables ground handlers to align staff with peak-demand periods. Because the benefits are immediate and measurable, truck slot management often serves as the gateway to broader community system adoption.

4. Enable Real-Time Track and Trace
True visibility means knowing where cargo is at every moment—not just at checkpoint scans. AI-ready operations incorporate GPS coordinates, geo-fencing, and predictive arrival notifications that keep all stakeholders informed. “The scope of availability of accurate and complete information before the truck arrival has been leading to clarity, cooperation, and understanding amongst the stakeholders,” noted Elliott Paige, Airport Director, Air Service Development at Hartsfield-Jackson Atlanta International Airport, at the time Kale’s solution was deployed. This advance shipment information transforms reactive operations into proactive ones.

5. Build Regulatory Compliance Intelligence
The regulatory landscape grows more complex by the day. Tariffs, HS codes, security screening requirements, and customs procedures vary by jurisdiction and change frequently. This complexity multiplies across international operations—what works in Europe doesn’t translate directly to Asia or North America, each with distinct data regulations and compliance frameworks.
Subramanian identifies this as a key opportunity: “One of the things we keep hearing from customers is it’s hard to keep up with all these regulations. Can AI help us? The reduction in errors is critical; otherwise, it’s going to cost them if the codes are entered wrongly.” AI-ready systems maintain current regulatory databases and flag compliance issues before they become costly problems.

6. Deploy AI Agents for Proactive Disruption Management
The next frontier involves AI agents that monitor operations 24/7 and take autonomous action when disruptions occur. Subramanian describes the vision: “How do you essentially have an agent, literally like a partner to a human, whose job is to run 24/7 and monitor different things around the world? And feed it either to a human in the loop or to feed it to another AI agent and take actions based on that.”
Consider a practical scenario: a cargo plane is delayed due to weather. An AI agent detects this, automatically adjusts truck scheduling, reallocates ground handling resources, and notifies affected parties—all before human intervention is required. The key is keeping humans in the loop for exceptions while AI handles the routine monitoring and responses.

7. Integrate IoT and Sensor Technology
AI becomes exponentially more powerful when fed rich, real-time data from the physical world. This means implementing RFID tracking for assets, temperature monitoring for pharmaceutical and perishable cargo, and geo-fencing for queue management. A multitude of private sector connected devices are now being deployed and are a ready source of shipment information. These technologies provide the granular operational data that enables AI systems to optimize warehouse planning, equipment utilization, and resource deployment.

8. Create Collaborative Stakeholder Platforms
AI readiness isn’t just about technology—it’s about fostering community collaboration. Airports that successfully implement community systems report that shared data platforms shift perceptions from siloed responsibility to community-wide benefit. When airlines, ground handlers, forwarders, and regulators share a common operational picture, the entire ecosystem becomes more resilient and responsive.

9. Build on Existing Infrastructure with API-Ready Architecture
Here’s where many AI initiatives stumble: the assumption that digital transformation requires wholesale system replacement. It doesn’t.
Legacy systems, often a decade or more old, can support AI capabilities when paired with modern API connectivity and cloud-based integration layers. The goal is to create an architecture that allows AI modules to bolt onto existing platforms without disrupting operations. This approach makes it possible to add capabilities like intelligent document processing, chatbots for system navigation, or predictive analytics without ripping out systems that already work.
Importantly, cloud-based integration solutions can be deployed without significant upfront capital investment, shifting costs to operational expenses that scale with usage.

10. Measure and Benchmark Continuously
AI readiness is not a destination but a journey. Airports should establish baseline metrics for key performance indicators: truck wait times, processing times, data accuracy rates, documentation volumes, and emissions. These benchmarks enable continuous improvement and demonstrate ROI to stakeholders—critical for securing continued investment and expanding digital capabilities. Successful implementations have documented specific gains: a 66 percent reduction in truck wait time, a 90 percent reduction in documentation, and a 94 percent improvement in operational efficiency.

The Path Forward: Start Where It Hurts Most

The logistics industry, as Subramanian observes, “is ripe for some amount of exciting revolutionary changes.” But realizing this potential requires moving beyond AI as a buzzword toward practical implementation grounded in operational reality.

“It’s not AI for the sake of AI,” Subramanian emphasizes. “It’s how does it improve? If someone’s doing something and it took them three hours, and magically, because of AI, we can reduce it—if not dramatically, at least to an extent where it makes life easier for them. That’s something that we want to do.”

For airport cargo leaders evaluating where to begin, the answer often lies in starting with a single high-impact module—truck slot management, for instance—that delivers quick, measurable wins. Success with one component builds stakeholder confidence and creates momentum for expanding digital capabilities across the cargo community. The airports that will thrive are those building these digital foundations today, one module at a time, creating the infrastructure that enables AI capabilities tomorrow.