There are things AI can and cannot do in ocean logistics. Today, it is most effective where disruption is common: missed appointments, rolled sailings, chassis and yard constraints, and documentation gaps. Predictive models can identify exceptions earlier and recommend corrective actions that prevent downstream delays and costs. These tools enhance operator decision speed and quality.
However, AI cannot resolve the structural constraints of the supply chain. Terminal capacity limits, inland infrastructure, labor availability and regulatory processes remain physical realities. AI also depends on accurate milestone data to be effective. In 2026, the competitive advantage will come from human-in-the-loop automation — supporting operators and standardizing response rather than replacing judgment.
In a similar vein, as the industry has reached a point where most shippers and providers have visibility, the differentiator is the ability to use information to change an outcome. The highest value comes from platforms that reduce exceptions, minimize manual touches and increase cost predictability through proactive decision-making. Visibility is only strategic when it controls dwell, prevents avoidable accessorials and shortens the time between issue detection and issue resolution.
In the realm of reliability, end-to-end supply chain performance increasingly depends on drayage reliability — it is the defining frontier. Appointment windows, chassis availability and yard flow determine whether shipments stay on schedule. Accountability is shifting toward real-time exception response and measurable recovery performance. Shippers will favor partners who can manage variability, not only report it.
We have three expectations for 2026. Operator-facing AI assistants will evolve into workflow engines that not only suggest actions but also initiate bookings, escalations, documentation release and reconciliation steps. Appointment and gate scheduling APIs will become standard tools across many markets, reducing rework and unnecessary waiting time. Finally, contract terms will increasingly reference exception recovery time and cost transparency as core service performance indicators, rather than relying primarily on on-time delivery percentages.
AI will strengthen operations, but it will not satisfy the need for disciplined execution.