CargoAi Launches AI Predictive Tracking To Anticipate Air Cargo Delays

CargoAi has introduced AI Predictive Tracking, a new capability designed to help air cargo stakeholders anticipate shipment risks and delays before they occur. The tool is available within CargoMART and as an add-on to the CargoCONNECT Track & Trace API.

Traditional tracking platforms typically provide visibility only after milestones are reported, limiting the time available to respond to disruptions. CargoAi’s predictive layer addresses this gap by forecasting upcoming shipment events and issuing early alerts when risk patterns emerge.

The system uses machine learning models trained on millions of historical shipments alongside live airline flight data to estimate timing for key cargo milestones, including FWB, RCS, MAN, DEP, ARR, NFD and DLV. It generates probability-based forecasts such as median (P50) and conservative (P90) estimates, which update continuously as new operational data becomes available. These forecasts are also translated into simplified signals that can be used directly by operational teams or integrated into existing systems.

For airlines, the tool can flag shipments that have not reached acceptance or manifest stages before cutoff, allowing stations or GSAs to intervene, release blocked capacity or prioritize high-risk cargo. Freight forwarders can identify at-risk shipments earlier, resolve documentation gaps and proactively communicate with customers. Ground handlers and system integrators can use conservative estimates to prioritize acceptance, automate pre-alerts and feed risk indicators into dashboards or SLA monitoring tools.

For example, on a flight scheduled to depart at 6 p.m., the platform may detect by 10 a.m. that required documentation has not been received, even though historical data shows it should have been completed in most cases. The system then generates a high-risk alert so operational teams can take corrective action.

The predictive engine combines airline, route and product performance data with live schedules and standardized milestone structures aligned with CargoIMP and IATA ONE Record. Each milestone includes estimated timestamps and confidence levels, along with risk indicators categorized as low, medium or high. The solution is fully backward compatible, allowing existing Track & Trace integrations to remain unchanged.

AI Predictive Tracking can be deployed through CargoAi’s user interface or embedded directly into customer systems. Within CargoMART, it integrates with enterprise workflows, including CargoBridge connections to TMS or ERP platforms that consolidate shipment data and reduce duplicate entry. Through CargoCONNECT, predictive milestones and alerts are delivered within API responses for integration into internal dashboards or automated workflows.

CargoAi said the launch responds to growing operational volatility in air cargo, where tighter cutoffs, schedule variability and higher service expectations are increasing pressure on logistics teams. By shifting from reactive tracking to predictive risk detection, the company aims to support more resilient, data-driven cargo operations.