Artificial intelligence has become one of aviation’s most discussed technologies, but its impact is varying significantly across different parts of the industry. While airlines are already using AI to optimise operations and improve decision-making, the next major shift for air cargo may come through autonomous AI systems capable of communicating directly with one another.

According to Kim Macaulay, Senior Vice President Information and Data and Chief Information and Data Officer at IATA, the conversation is no longer centred on whether AI will transform aviation, but on how quickly different parts of the industry will adopt it.

“Airlines are making heavy investments in data and Large Language Models (LLMs),” she said. “The first step is to ensure that trusted frameworks for sharing data are developed.”

For air cargo, one of the most significant developments lies in the emergence of Agentic AI – AI systems capable of independently completing increasingly complex tasks with limited human intervention. Rather than simply assisting users with repetitive tasks, Agentic AI could enable different participants across the supply chain to interact autonomously.

Macaulay points to a future where a freight forwarder’s AI agent communicates directly with AI systems representing both the shipper and the cargo carrier to organise a shipment.

“For example, each year, IATA publishes the Dangerous Goods Regulations – a massive document covering thousands of rules for the safe handling of goods,” she explained.

“With Agentic AI, a freight forwarder Agent could chat to a shipper Agent and a cargo carrier Agent to arrange a shipment, and ensure it is correctly labelled and packed in accordance with the DGR rules, with only the minimum of required human oversight.”

Such an approach could reduce manual administrative work while improving compliance with one of air cargo’s most complex regulatory requirements.

Safety remains aviation’s highest priority, and Macaulay believes AI’s greatest immediate contribution lies in analysing vast quantities of operational data more quickly than humans. She said scaling AI for aviation safety is less about technological capability and more about organisations adapting to use it effectively. AI is already being used to process large datasets, supporting faster and more accurate identification and management of safety risks through prediction, text classification, image recognition and speech analysis. Across airline operations, AI applications are also expanding into route optimisation, predictive maintenance, forecasting, revenue management and customer service.

While AI-powered chatbots are becoming increasingly common, Macaulay believes their value extends beyond reducing costs.

“On balance, I believe chatbots will lead to a better customer experience, not simply be a cost-cutting exercise,” she said. Despite rapid progress, Macaulay argues that widespread adoption depends less on AI models themselves than on creating secure ways to share information between industry partners.

“It’s obviously vital that commercially sensitive information can’t be hacked, and that important operational data can be shared in confidence between industry partners,” she said.

Trusted data-sharing frameworks will become increasingly important as AI systems begin supporting decisions across multiple organisations rather than within individual companies. Although airline investment in AI continues to grow, Macaulay acknowledges that cost remains a barrier for many operators.

“Airline margins are much thinner than many other large business sectors, which reduces the potential investment in expensive cutting-edge AI,” she said.

However, falling computing costs are expected to make advanced AI increasingly accessible across the industry. One indication of how quickly adoption is progressing is software development itself.

“Presently, some 20–40 percent of coding for airlines is being done by AI,” Macaulay noted, adding that productivity gains are likely to accelerate further as the technology matures.