AI Port Operations: Lessons from L.A.’s 10M TEU Year

9,447,731 TEUs with a month still to count. That’s where the Port of Los Angeles stood after November—1% ahead of 2024—even as tariffs, trade uncertainty, and a high-profile ship fire swirled around the industry.

Most companies get the story wrong here. They see a “strong year” headline and chalk it up to luck, a temporary demand bump, or a one-off operational push. The more useful read is this: high-volume ports are proving that resilience is an operations discipline, not a motivational poster. And in 2026 planning cycles, that discipline increasingly looks like AI-driven logistics optimization—forecasting, flow control, and exception management—because manual coordination doesn’t scale to 10 million decisions.

The Port of Los Angeles expects to finish 2025 north of 10 million TEUs, placing it in its top three years of all time. This post breaks down what that performance signals for shippers, carriers, and logistics leaders—and how AI in transportation and logistics can help you run a port-connected network that stays stable when the world doesn’t.

What L.A.’s 10M TEU pace actually proves

The point isn’t that Los Angeles moved a lot of containers. The point is that it did so without congestion.

Port leadership emphasized a clean operational run: “All that cargo moved without congestion and not a single ship backed up.” When you’re pushing close to 10 million TEUs annually, avoiding vessel backlogs isn’t a nice-to-have. It’s the difference between predictable inland flows and cascading chaos—missed DC appointments, chassis shortages, rail dwell spikes, and expensive expedite decisions.

Here’s the reality? Congestion is usually a data problem before it becomes a physical one.

Congestion-free doesn’t mean “nothing went wrong”

A notable example: the Nov. 21 fire aboard the ONE Henry Hudson. The port reported no measurable impact on operations and no injuries.

That’s not magic. That’s what happens when an operation has:

• Clear exception playbooks (who decides what, in what order)
• Enough real-time visibility to reroute work fast
• Slack in the right places (yard capacity, labor flexibility, rail coordination)

AI doesn’t replace those fundamentals. It reinforces them—by surfacing exceptions earlier and recommending actions faster.

The November dip is a signal, not a slump

In November, total volume came in at 782,249 TEUs, down 12% year-over-year. Loaded imports were 406,421 TEUs (-11%); loaded exports were 113,706 TEUs (-8%); empty containers were 262,122 (-13%).

That drop is easy to misread. The article points to a key driver: frontloading earlier in the year.

For operators and shippers, frontloading is a forecasting stress test. It compresses peak behavior into earlier months, distorting “normal seasonality” and punishing teams that plan capacity based on last year’s calendar.

AI forecasting models (done right) help here by:

• Detecting demand shifts earlier (not after weekly averages smooth them away)
• Separating true demand from pull-forward behavior
• Quantifying the cost of being wrong (labor, dwell, detention, inventory)

AI in port operations: where it creates measurable throughput gains

AI in transportation and logistics is often pitched as futuristic. Ports don’t need futuristic. Ports need predictable flow.

The most practical AI use cases for port operations and port-adjacent networks land in three buckets: forecasting, flow orchestration, and exception automation.

1) Forecasting: better ETAs, better labor, fewer surprises

A port can’t “save” a bad ETA. Neither can a dray carrier. When ETAs swing, everything downstream whipsaws—labor planning, yard strategy, appointment availability, rail bookings, and customer promises.

AI improves forecasting by fusing signals that don’t live in one system:

• Vessel AIS and schedule reliability trends
• Port call patterns (berth windows, historical dwell by service string)
• Terminal productivity patterns by shift/day
• Inland constraints (rail service variability, chassis availability, gate turn times)

A good output isn’t just an ETA. It’s a confidence band (“arrives Tuesday 2–6 a.m. with 80% confidence”) and a list of what would break it.

If you run a network tied to Los Angeles/Long Beach, this matters because it directly affects:

• Appointment strategy (book early vs book late)
• DC receiving labor (fixed shifts vs flex labor)
• Inventory positioning (when to replenish, when to pause)

2) Flow orchestration: keep containers moving to keep the yard usable

Ports don’t run out of space all at once. They lose space a little at a time when containers stop moving.

AI-driven flow orchestration focuses on throughput, not just visibility. Practical examples include:

• Pickup prioritization: recommending which import boxes to evacuate first based on dwell risk, demurrage exposure, and downstream demand
• Appointment smoothing: forecasting gate demand and nudging appointment allocations to reduce surges
• Yard planning assistance: predicting rehandles and suggesting stack placement to reduce wasted moves

These are operationally unglamorous—and financially huge. Reducing rehandles, turn time variance, and avoidable dwell often produces more value than “finding a cheaper carrier.”

3) Exception automation: stop treating disruptions as surprises

Tariffs, trade intrigue, and economic uncertainty were explicitly called out in the source article. Those aren’t edge cases anymore. They’re the operating environment.

AI helps by turning disruptions into manageable queues:

• Identify which SKUs/containers are exposed to tariff changes
• Flag inbound orders likely to miss delivery windows based on port/rail/dray signals
• Recommend alternate routings (intermodal vs transload vs expedite) with cost/service trade-offs

A useful line I’ve found for leadership teams: “If exceptions are common, manual exception handling becomes your primary process.” That’s an expensive way to run.

Tariffs and trade uncertainty: why AI demand modeling beats gut feel

Tariffs don’t just change cost. They change behavior. Importers pull forward volume, shift sourcing, or reroute through different gateways. That behavior shows up as:

• Strange seasonality
• Higher variance in order sizes
• More “urgent” freight (because plans changed late)

The Port of Los Angeles serves roughly 200,000 importers and exporters annually, which hints at how fragmented the decision-making is across the customer base. Fragmentation drives volatility.

AI-based demand and inventory modeling helps logistics leaders answer questions that actually matter:

• If we frontload again, how much DC capacity do we need—and when?
• Which suppliers are consistently late at the port-to-DC stage?
• What’s the cheapest way to protect service levels: more safety stock, more expedited moves, or different routing?

The stance I’ll take: frontloading without modeling is just panic with a purchase order. You might get lucky once. You won’t build a repeatable operating plan.

A practical playbook for shippers and 3PLs tied to L.A.

If you’re a shipper, 3PL, or carrier with meaningful exposure to the Port of Los Angeles, you don’t need a grand AI program. You need a tight, measurable one.

Step 1: Pick two metrics that define “healthy flow”

Don’t pick ten. Pick two that connect directly to cost and customer impact. Common winners:

• Import dwell time (by terminal and by customer)
• Appointment lead time and gate turn time variance

Then measure them weekly, not quarterly.

Step 2: Build an “early warning” layer before optimization

Optimization without reliable signals is how teams lose trust.

Start with alerts like:

• Containers at risk of crossing demurrage thresholds
• ETA confidence dropping below a set percentage
• Rail plan mismatches (box discharged but rail booking missing)

Even basic machine learning classification models can outperform manual spreadsheet triage here.

Step 3: Turn recommendations into decisions, not dashboards

A dashboard that says “dwell is up” is late. A system that says “move these 37 containers first because they will cause 68 downstream late orders” is actionable.

The goal is decision support that ties to a dispatch plan, a booking change, or a customer notification.

Step 4: Stress-test your network against three disruption scenarios

Use the 2025 “headwinds” theme as your template. Run tabletop exercises with your data:

1. Sudden tariff announcement (frontload spike)
2. Vessel incident reducing capacity for 7–10 days
3. Rail service degradation during peak

If your current process can’t quantify impact in 24 hours, you’ve found the ROI case for AI.

People also ask: what does “10 million TEUs” mean for inland logistics?

It means inland systems become the constraint. When the port runs smoothly, bottlenecks move to rail ramps, chassis pools, dray capacity, and DC receiving.

It means predictive planning matters more than heroics. With volume at this scale, a 2–3% forecasting error can translate into thousands of containers mis-timed.

It means your advantage is coordination speed. The winners aren’t the ones who “see” disruptions first. They’re the ones who decide and execute faster.

Where this fits in the “AI in Transportation & Logistics” series

Ports are where global variability collides with local capacity. The Port of Los Angeles nearing 10 million TEUs despite headwinds is a clean example of why AI in transportation and logistics is trending toward practical outcomes: stable flow, fewer surprises, and faster exception handling.

If you’re planning for 2026, the question isn’t whether your network will face disruptions. It will. The question is whether your team will still be making port-to-inland decisions with yesterday’s data and manual triage.

If you want a useful next step, start small: choose one gateway lane tied to Los Angeles, define two flow metrics, and build an AI-assisted exception queue your operators will actually use. Then expand. What would your operation look like if “no ship backed up” became your baseline expectation—not a headline?