The Loadstar has identified the fragmented state of air cargo data as an "ideal" application for artificial intelligence, framing machine learning and large language models as practical tools for closing long-standing visibility gaps between shippers, airlines, freight forwarders and ground handlers.

The argument lands in an industry that has grappled with inconsistent electronic data interchange since the International Air Transport Association (IATA) launched its e-freight initiative targeting paperless operations. More than a decade later, booking confirmations, air waybills, house waybills and delivery messages still circulate across competing formats and legacy EDI feeds, requiring manual reconciliation by operations staff.

What gaps does AI aim to close?

Air cargo carries roughly 35 percent of global trade by value but a far smaller share by volume, and the unit-level economics make clean data especially valuable. Mismatched master data — wrong airport codes, inconsistent equipment identifiers, abbreviated consignee names — can propagate across booking, customs and delivery systems, generating rework charges and customs holds that the carrier ultimately absorbs.

AI fits the problem, the publication argues, because the technology can absorb unstructured inputs: photographed airway bills, scanned customs entries, email booking instructions and legacy teletype feeds. Those sources are typically incompatible with the structured XML and EDI messages that modern warehouse management systems expect.

Why is the industry revisiting the question now?

Large language models, which became widely accessible to commercial users after 2022, have demonstrated capability on language-heavy logistics tasks: entity extraction from waybills, classification of commodity codes, summarization of shipment status and translation of multilingual cargo documents. Forwarders and ground handling agents have begun embedding those capabilities directly into customer-facing tracking portals and back-office reconciliation tools.

The publication frames the AI angle as a successor to earlier standardization pushes, including IATA's Cargo iQ performance monitoring program and the wider push toward One Record, an interoperable data-sharing model built around JSON-LD rather than the message-passing EDI architecture that still dominates carrier-to-forwarder traffic.

What does the technology actually have to prove?

Shippers and regulators will judge results against operational benchmarks rather than vendor demonstrations. Dispatch reliability, on-time performance and claims rates are the metrics that determine whether a tool is adopted at scale, and the publication treats them as the appropriate yardsticks for any AI deployment that touches the booking or customs pipeline.

Data governance presents the harder problem. Air cargo messages contain commercially sensitive pricing and capacity information, and carriers have historically resisted sharing granular operational data with intermediaries — a posture that complicates training data access for any model intended to learn from industry-wide flows.

What happens next

The Loadstar's framing positions AI as a complement to existing standards work rather than a replacement for it. Whether carriers, forwarders and regulators adopt the technology broadly will depend on whether early deployments can demonstrate measurable improvements in data quality and shipment visibility without raising competitive concerns about the underlying information flows — a question that the publication suggests the industry has only begun to answer.