I have run operations with sophisticated AI models. The best decisions came when experienced planners could see the patterns AI surfaced — and then apply context the model couldn't know. The future is augmented intelligence, not replacement.
The dominant narrative around AI in aviation and logistics is one of displacement. AI will optimize routing. AI will predict demand. AI will replace the network planners, the capacity managers, the commercial teams who currently make these decisions.
I have been running operations with sophisticated AI and predictive models for the better part of a decade. The displacement narrative is wrong — not because AI lacks capability, but because it misunderstands what expertise actually consists of in complex operational environments.
At DHL, we had predictive models running across 41 countries. The models were good. They identified patterns in demand data, flagged capacity imbalances, surfaced pricing anomalies. But the best decisions we made were not the ones where the model told us what to do. They were the ones where an experienced network planner saw the pattern the model surfaced — and then applied context the model couldn't know.
A government policy change opening a market in 48 hours. A competitor's operational disruption creating a two-week capacity window on a specific lane. A customer relationship that made a below-model yield acceptable because of the long-term strategic value of the account. These are not data problems. They are judgment problems. The model surfaces the pattern. The operator understands the context. Together, they make decisions that neither makes well alone.
The 52% eBooking adoption we achieved at Polar did not come from replacing human judgment with automated systems. It came from giving human judgment better tools — real-time visibility, better demand data, faster feedback loops. The humans still made the decisions. They made them faster and with more confidence.
What AI actually does in complex operational environments is expand the decision surface available to operators. It surfaces patterns that would take a human analyst days to identify. It removes the cognitive load of monitoring routine parameters. It allows experienced operators to focus their attention where judgment matters most — on the edge cases, the context-dependent decisions, the situations that require both data and experience.
The organizations that will get the most from AI investment are not the ones that deploy it as a replacement for operational expertise. They are the ones that use it to augment operators who already know what good judgment looks like — and give those operators a larger surface to apply it.
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