Air cargo is a trade facilitator that contributes to global economic development, creates millions of jobs, and enables the delivery of high-quality products at competitive prices to consumers worldwide. About 33% of the world’s trade by value is facilitated by air cargo, which transports over US $8 trillion worth of goods. These technologies offer unprecedented opportunities to enhance operational efficiency, reduce costs, and improve service quality in the air cargo technology industry.
While AI and automation in logistics are often used interchangeably, they address different aspects of operational efficiency in air cargo technology.
- Automation involves using technology to perform repetitive and rule-based tasks without human intervention. Examples include automated cargo handling systems and robotic warehouse operations that streamline logistics processes.
- AI, on the other hand, adds an intelligence layer to automation in logistics. It involves systems capable of learning, reasoning, and adapting to new information. Artificial intelligence in logistics and transportation enables predictive analytics, optimised route planning, and advanced decision-making processes.
Impact of AI on the air cargo industry
AI-driven solutions are transforming the air cargo technology industry by enhancing efficiency, reliability, and customer experience. Key impacts of AI in logistics and transportation include:
- Predictive analytics: AI algorithms analyse historical data and market trends to forecast cargo demand accurately. This helps logistics providers manage capacity efficiently, reducing the risk of underutilised cargo space.
- Optimised route planning: AI tools analyse factors like weather, fuel consumption, and air traffic to determine the most efficient flight routes, improving delivery times and reducing operational costs.
- Dynamic pricing models: AI enables real-time pricing strategies by evaluating market demand, competitor pricing, and customer behaviour, ensuring competitive pricing and maximising profitability.
- Enhanced customer support: AI-powered chatbots provide real-time shipment tracking, estimated delivery times, and quick responses to customer queries, improving overall service quality.
- Risk management: AI systems assess potential risks by monitoring global events, supply chain disruptions, and security threats, enabling proactive management strategies.
Impact of automation on the air cargo industry
Automation in logistics significantly enhances productivity and reduces operational complexities in the air cargo sector. Some of the valuable benefits include:
- Operational efficiency: Automated cargo handling systems increase the speed and accuracy of loading and unloading cargo, reducing turnaround times and minimising errors.
- Cost reduction: Automation in logistics helps minimise labour costs and other expenses by reducing human intervention in repetitive tasks.
- Improved safety: Automated systems enhance workplace safety by mitigating the risks associated with manual cargo handling.
- Scalability: Automated logistics solutions support the scalability of operations, allowing air cargo companies to handle varying demand levels without significant changes to infrastructure or staffing.
- Regulatory compliance: Automated systems streamline documentation and regulatory compliance processes, reducing the risk of human errors and compliance breaches.
Challenges while leveraging AI and automation in the air cargo industry
While the benefits are significant, the adoption of AI and automation in logistics presents certain challenges:
- High initial costs: Implementing advanced technologies requires substantial investment in equipment, software, and training.
- Integration complexity: Many companies rely on legacy systems that may not seamlessly integrate with modern AI and automation solutions.
- Data security risks: As logistics operations become more digitalised, the risk of data breaches and cyber threats increases.
- Regulatory hurdles: The air cargo industry operates under strict regulatory frameworks, which can complicate the deployment of new technologies.
- Workforce adaptation: There is a need for reskilling and upskilling the workforce to manage and operate new automated systems effectively.
How can Infosys BPM help companies leverage AI for logistics and transportation?
Infosys BPM offers a comprehensive suite of services to optimise operations and drive exceptional outcomes by integrating ai in logistics and transportation. These services address critical challenges such as revenue leakage, AWB disputes, high operational costs, low eAWB penetration, and service quality issues.
Frequently asked questions
IATA's ONE Record became the preferred method for air cargo data exchange from January 2026, with airlines representing more than 70% of global air waybill volumes committed to implementation. The electronic air waybill (e-AWB) has been the default contract of carriage on enabled trade lanes since 2019, and IATA Cargo-XML standards govern electronic messaging with customs authorities. AI initiatives that are not built on these standards create data silos that limit interoperability with forwarders, handlers and customs.
Capacity and revenue management typically produce the earliest measurable return, because dynamic pricing and load-factor optimisation act directly on yield per chargeable kilogramme. Industry analyses indicate machine-learning demand forecasting reduces forecast error by 30% to 50%, which converts into higher load factors and fewer distressed-rate sales. Document automation, particularly air waybill data capture and dispute resolution, is the second area because it addresses revenue leakage directly.
Air cargo automation must satisfy advance screening mandates such as the US Air Cargo Advance Screening (ACAS) programme and the EU ICS2 pre-loading advance cargo information rules, plus dangerous-goods declarations that still carry more than 1,200 state and operator variations. Automated systems that file incorrect advance data create regulatory exposure for the carrier, not the vendor. Enterprises typically keep human review on exceptions flagged by the automation and log every automated filing for audit.
Revenue leakage in air cargo arises mainly from weight and volume discrepancies between booking and actual shipment, misapplied surcharges, unbilled ancillary services and air waybill disputes. Industry estimates place leakage at 1% to 3% of cargo revenue. Automated reconciliation of booked, accepted and flown data against the rate sheet identifies discrepancies before invoicing, and AI-assisted dispute handling reduces settlement cycle times and write-offs.
Carriers typically start with data standardisation and an integration layer, then deploy AI on high-volume, well-defined decisions before attempting end-to-end automation. Legacy cargo management systems often cannot expose real-time data, so an API or event-streaming layer aligned to ONE Record is the prerequisite. A typical roadmap is 6 to 9 months of data foundation, followed by forecasting and pricing use cases, then document and claims automation.
