Mr. Abhishek Goyal, Executive Director & CEO, Aeroprime Group, believes AI adoption in logistics should begin with identifying real operational challenges rather than following a technology trend.
Measurable efficiency, not automation, defines meaningful AI adoption.
Drawing on the company’s experience as a Cargo GSA representing global airlines and working closely with the freight forwarder community, he sees immediate opportunities in rate enquiries, shipment documentation, cargo tracking, demand forecasting and customer communication.
Before committing, organisations must weigh data quality, integration with existing systems, implementation costs and measurable time savings. Equally important is whether a tool can cope with the complexity of differing airline procedures, routing options and cargo requirements. The purpose is to simplify workflows and sharpen responsiveness while keeping human oversight wherever commercial and operational decisions are involved.
The Road Ahead for Air Cargo
Over the next three to five years, AI is expected to make air cargo operations more predictive, efficient and data-driven. Beyond automating rate enquiries and shipment tracking, it will increasingly support demand forecasting, capacity planning and the identification of optimal routing options. A significant opportunity lies in load planning and Unit Load Device (ULD) build-up, where AI-powered tools can help optimise cargo placement, weight distribution and available aircraft space while accounting for cargo dimensions, compatibility and operational constraints. The result can be better space utilisation, fewer inefficiencies and stronger cost management. For Cargo GSAs, AI can also strengthen sales intelligence, improve quotation turnaround times and help teams anticipate disruptions before they reach customers. The real value, however, will depend on how well these tools integrate with airline systems, ground handling processes and existing logistics platforms.
The Judgement No Dataset Can Supply
AI can analyse data quickly, yet cargo logistics still rests heavily on relationships, commercial understanding and operational experience. Negotiating rates, balancing yield with volume, prioritising customers and developing airline and freight forwarder partnerships all call for an understanding of market realities that data alone may not capture. When capacity is constrained, schedules change or shipments meet unexpected challenges, teams must weigh competing priorities and communicate transparently with customers. AI is seen as a tool that strengthens human capability rather than replaces it. The most effective logistics teams will combine data-driven insights with industry experience, accountability and strong customer relationships. In practice, that clarity of purpose depends on how well a company’s own data and systems are organised.
