Air cargo’s next digital challenge is not simply digitising more processes, but connecting fragmented data across the supply chain, delegates at Aviation Connect in Athens heard today.
Fragmented Data and Shifting Trade Flows
According to Accenture Cargo’s product growth lead, Shanmugam Thangavelu, changing trade patterns, growing capacity, and the emergence of AI are increasing the need for cargo stakeholders to share and reconcile information. He exemplified China-US ecommerce trade, which has fallen significantly over the past year following changes to America’s de minimis rules.
“The volume is actually shifting, but it’s not disappearing,”
said Mr Thangavelu, adding that, overall, freight trade was showing positive growth, while capacity had also increased year on year.
The challenge, he argued, was ensuring the data accompanying that freight movement could be shared quickly enough for operational and commercial decisions. Mr Thangavelu highlighted ground handlers as an example, saying they could receive multiple versions of shipment information from carriers, export agents, customs declarations, and other parties —
“a problem we are seeing right now – even some of the major ground handling agents face this,”
he said.
IATA ONE Record: Awareness ≠ Adoption
IATA’s ONE Record standard was intended to create a common digital record for shipments, but Mr Thangavelu said its adoption remained incomplete. While a large proportion of stakeholders are aware of it, he stressed that “being ready” was different from actually operating the system. Recent implementations, including Lufthansa’s work with multiple technology partners, showed adoption was progressing, he added, but achieving “a single source of truth” across the entire shipment lifecycle would still take time, he warned.
This gap leaves an opportunity for AI to help bridge the disconnect. Mr Thangavelu illustrated how AI agents could connect different systems, reconcile competing sources of shipment information, and help cargo participants make and execute decisions. For a ground handler, this could mean knowing the correct number of pieces arriving, identifying a last-minute manifested shipment, and ensuring the appropriate labour and resources were available.
Operational and Strategic AI Applications
The potential applications also extend beyond operations, to pricing, capacity allocation, flight planning, and revenue management, said Mr Thangavelu. He argued that connecting operational systems with global trade intelligence could give airlines faster insight into changing commodity flows, helping them adjust sales strategies, capacity allocation, and network planning. That insight relies on reconciling real-time data across carriers, customs, agents, and handlers — a task increasingly impractical without AI-driven interoperability.
Source: The Loadstar
Compiled from international media by the SCI.AI editorial team.
