• AI is transforming airfreight from rigid, form-based systems into conversational, intelligent assistants that execute tasks through natural dialogue, improving usability and decision-making across booking, forecasting, and pricing.
• Predictive and generative AI are boosting forecast accuracy, refining dynamic pricing, and automating repetitive tasks while keeping humans in control — balancing automation with judgment and transparency.
• The next phase of digital transformation focuses on human empowerment, using AI to cut rework, improve data quality, and free professionals to focus on strategic, high-value decisions rather than routine operations.

The airfreight industry has long relied on rigid, legacy systems — green screens, menu-driven commands, and workflows demanding precision but lacking intuition. Now, a new era is emerging. Artificial intelligence is replacing static interfaces with dynamic, conversation-driven processes that feel more like asking a colleague for help than filling in endless forms. 

The change is not only technological but cultural, redefining how airlines and cargo operators interact with their systems, forecast demand, price capacity, and balance automation with human expertise.

“We already see a move from click-heavy, menu-driven screens to agentic AI — digital assistants that understand natural language and execute workflows end to end,” says Radhesh Menon, Head of Cargo and Logistics 

Solutions at IBS Software

Instead of navigating multiple forms, operators could soon interact with systems naturally. “Agents might simply say, ‘Book three tonnes of electronics from Singapore to Frankfurt for Friday night and hold a waitlist on earlier legs.’ The AI would orchestrate the steps, validate data, and return a confirmed plan.”

Menon sees this as the next evolution of user interfaces. “It’s the next turn in the UI journey — from green screens to graphical interfaces, and now to conversational, task-oriented agents. In many ways, it’s coming full circle — only far more usable.”

Forecasting Volatility with AI

If airfreight has one constant, it’s unpredictability. Traditional forecasting struggled with shocks and volatile markets. AI is changing that.

“Classical models rely on fixed statistics and pristine data. AI learns from wider, even unstructured sources, automating prep tasks like smoothing and outlier handling. Layering AI on classical methods has improved forecast accuracy by up to 50 percent, with far less manual effort,” says Menon.

While no model predicts a true “black swan” like COVID, AI helps companies recover faster. “We can re-train models, de-weight corrupted periods, and regain reliable signals quickly. Adaptability in volatile markets is where AI proves its worth.”

Cracking the Complexity of Pricing

Pricing in airfreight remains notoriously complex, shaped by shifting demand, trends, and customer behavior. “This puts pressure on analysts to balance demand and yields,” Menon explains.

AI is helping break down that complexity. “We decompose capacity and demand into fine-grained segments and estimate willingness to pay by corridor, product, and booking window. We blend long-term data with short-term signals to compute bid prices per flight segment — selling the right weight to the right segment at the right time.”

Transparency remains key. “Dynamic pricing doesn’t mean opacity. We use explainable signals — load factor outlooks, recency trends, product priorities — and publish clear guardrails like SLAs and surcharge policies.”

The balance comes from keeping humans in control. “Experts can ‘review and approve’ some data points and go ‘autopilot’ on others. That sustains yield without eroding trust.”

Human in the Loop

Concerns about over-reliance on automation are valid, Menon says. “AI excels at pattern-finding; humans excel at context and causality. Our model keeps the human in the loop: AI handles data prep, model selection, anomaly detection, and first-cut pricing, while experts apply judgment or overrides.”

The goal is not replacement but amplification. “We’re taking away stress and rework so people can focus on high-value exceptions and decisions.”

The human impact is clear. “Nearly 60 percent of professionals have considered leaving due to outdated tools and skills gaps. Yet 63 percent believe better real-time data would improve both outcomes and morale.”

For Menon, this proves a point: “Digital transformation in air cargo cannot just be about dashboards and KPIs. It has to be about people.”

Generative AI: The Next Frontier

Looking forward, Menon sees generative AI extending beyond forecasting and pricing into daily operations. “Generative and agentic AI together will transform the value chain — auto-reading emails or chats, converting them into structured actions like bookings, tracking updates, or claims triage. The human agent validates rather than keys everything in.”

Operationally, the impact could be profound. “We expect to offload 50–70 percent of repetitive exception handling — missed connections, rebookings, notifications — freeing people for complex recovery and customer work.”

But the focus remains pragmatic. “No science projects. We target tangible use cases that close productivity gaps — like anomaly detection in shipment data or plausibility checks flagging an express shipment showing 10,000 kilograms. Real tools that reduce stress, improve accuracy, and make daily work better.”