The buzz surrounding AI in the civil aviation sector is now spreading from passenger transport to cargo transport. According to an official press release issued by U.S. logistics technology company SmartKargo on October 26, the company has launched the first batch of products in its AI application suite: AIRCAM, AINOC, AIULD, and AICTX, four AI applications for air cargo. These applications cover four key areas: revenue and capacity management, operations control, container asset tracking, and system interconnection. STAT Times and Air Cargo News subsequently reported on this development. The main selling point of these applications is that they can be integrated “without replacing existing systems.” They are designed to be embedded into airlines’ existing cargo management systems via APIs, and the company claims they can be deployed within weeks.
According to STAT Times on October 27, AIRCAM (AI Revenue and Capacity Management) is responsible for forecasting capacity and demand, and providing real-time dynamic spot rate recommendations to help freight teams manage pricing, loading, and route capacity utilization. AINOC (AI Freight Control Tower) provides the operations team with a network-wide predictive view, proactively alerting them before anomalies escalate into delays and costly recovery actions. AIULD (AI Unit Load Device and Asset Management) tracks and predicts the location, status, and availability of unit load devices (ULDs), helping to reduce losses and delays caused by unclear asset status. AICTX (Unified Messaging and Interoperability Backbone) connects freight systems and partners through a standardized messaging layer, enabling accurate data flow across the network and supporting operational decision-making.
All four applications are delivered via API, allowing them to run directly on airlines’ existing cargo management systems – whether commercial platforms or self-built systems – without requiring platform migration. According to Air Cargo News on November 2nd, these applications are modular, and the company claims they can be implemented in “weeks, not years.” Airlines can start with a single use case, see the results, and then expand. The four tools can be used individually or deployed as a complete set, and the implementation plan is designed to work around existing operations rather than requiring a complete overhaul.
In an official press release, SmartKargo’s Chief Revenue Officer, Olivier Houri, stated that “every conversation in air cargo today revolves around AI: every vendor has a roadmap, every conference has an AI forum, and every airline is told that AI will change everything.” However, SmartKargo’s approach starts with “a business problem.” According to Houri, any AI capability must pass a triple test before being built or deployed: can it increase revenue, reduce costs, or mitigate risk? Each application launched must satisfy at least one of these criteria and operate under the principles of safety and reliability. He emphasized that “these applications are not research projects or slides on a roadmap,” but are designed to integrate with existing airline systems and quickly generate value. The company also stated that it will continue to expand its application portfolio and deepen existing capabilities using the same standards.
Over the past month, international civil aviation AI news has primarily focused on passenger pricing, airport experience, and air traffic management. The application of AI in the cargo sector has been relatively low-profile, but its business logic is arguably more straightforward – every item on the cargo profit and loss statement can be quantified. SmartKargo's approach – "no system replacement, project-based on business problems, and deployment within weeks" – provides a benchmark for the industry: whether AI can truly be integrated into operational processes depends on whether it addresses specific business problems, rather than simply demonstrating its capabilities. The subsequent expansion and integration, along with airlines' actual usage feedback, are worth monitoring.