The Role of Human Expertise in an AI Assisted Operations Environment

The introduction of artificial intelligence into freight operations has raised questions about the future role of human staff. Many forwarders are interested in using AI to manage repetitive tasks, but there is concern about whether this could reduce the need for experienced operators. The current evidence suggests a different direction. AI appears to strengthen the work of skilled staff rather than replace it.

Freight forwarding depends on judgement. Operators interpret incomplete information, anticipate delays, coordinate with partners, and maintain relationships with clients. These tasks rely on situational awareness, context, and familiarity with the industry. AI tools perform well when they receive clear inputs, but they struggle with ambiguity or situations that fall outside ordinary patterns.

In practice, the most effective use of AI places it in a supporting role. It can prepare draft emails, extract data from documents, summarise long threads, or surface exceptions that deserve human attention. Staff can review this material quickly and decide how to proceed. The result is a smoother workflow that reduces fatigue and improves consistency, while preserving the operator’s judgement where it matters most.

This shift may also change how companies structure their operations teams. New roles may emerge that blend technical understanding with traditional freight experience. Some staff may focus on validating AI outputs, while others concentrate on complex shipments or client facing work. The division of labour becomes clearer and less reactive.

FLAIR is examining how forwarders view the relationship between human expertise and automation. Our interviews show that most companies want to keep operators at the centre of the process while reducing the administrative burden that occupies so much of their time. As AI tools mature, the challenge will be to integrate them in ways that enhance reliability and service quality without introducing new forms of complexity.