• AI in air cargo is expanding from route and demand optimisation to proactive compliance and risk management, helping forecast peaks, secure capacity, and prevent bottlenecks or clearance delays.
• Predictive analytics and generative AI can automatically detect documentation anomalies, missing information, and compliance deviations, shifting oversight from reactive to preventive.
• Effective deployment depends on structured digital data flows, secure platforms, and investment in cargo community systems to bridge gaps in emerging markets and ensure regulatory compliance.

Artificial intelligence is moving beyond route optimisation and demand forecasts to play a direct role in air cargo compliance and risk management. That’s the message from Kale Logistics Solutions, which argues that the technology is no longer just a tool for efficiency, but a safeguard against disruption.

“AI is now at the forefront for air cargo with use cases around route management, price discovery, and capacity optimisation,” said Amar More, CEO and Co-founder of Kale Logistics Solutions. “For example, predictive analytics powered by AI can help airports and handlers forecast cargo peaks in advance, ensuring better workforce and equipment planning. Similarly, forwarders are using AI-driven demand forecasting to secure optimal capacity and negotiate competitive rates, improving both efficiency and profitability.”

From prediction to prevention

The shift is not only about forecasting demand. Kale says the next wave of AI deployment is focused on spotting risks before they turn into costly breakdowns.

“Yes, absolutely. With the advancement of generative AI and predictive analytics, it is now possible to flag potential risks well in advance,” More explained.

“For example, AI can identify patterns that may lead to congestion, bottlenecks, or clearance delays, allowing stakeholders to take preventive action. It can also analyse documentation and shipment data to automatically detect anomalies, missing information, or deviations from compliance requirements.”

This represents a move away from reactive, after-the-fact compliance towards proactive oversight built into the flow of data.

Compliance pressure and digital gaps

The stakes are high. Regulators are tightening data and security requirements across multiple regions. But in emerging markets, especially in Southeast Asia and Africa, digitisation is uneven. Airports and handlers working with paper-based systems or fragmented digital platforms are at greater risk of non-compliance or delays.

Kale believes AI can bridge some of those gaps by automating checks and highlighting discrepancies early. But the technology depends on structured data flows—and that means investment in cargo community systems (CCS) and standardised digital processes.

Balancing trust and automation

For AI-driven compliance to gain traction, data security remains a critical issue. Kale says its SaaS platforms are designed with multilayered safeguards, including GDPR compliance, encryption, and audit trails—tools that will be essential if AI is to move from pilot projects into day-to-day cargo handling.

“While stakeholders such as airports, customs, and airlines may have competing business priorities, they all recognise the need for standardised and reliable data from their customers,” More noted. “Our platforms ensure that data is shared in a secure, role-based manner, so each stakeholder only accesses the information relevant to their function.”