10M TEUs at LA Port: The AI Playbook for Resilience

The Port of Los Angeles is on track to clear 10 million TEUs in 2025—even with tariffs, trade uncertainty, and a container ship fire in the mix. That number matters because it’s not just a flex for a single port. It’s a signal that the logistics world is splitting into two camps: networks that can absorb shocks, and networks that get whiplash every time trade policy or ocean schedules change.

Here’s the part most companies miss: resilience isn’t “having a backup plan.” It’s having decision speed. When volumes swing, vessels bunch, or importers rush to frontload ahead of tariff deadlines, your advantage comes from how fast you can re-forecast, re-slot, re-route, and re-staff. And increasingly, that speed comes from AI in transportation and logistics—forecasting, optimization, and automation tied together into one operating rhythm.

Below, I’ll break down what LA’s near-10M-TEU year tells us about the future of port operations, and how shippers, 3PLs, and port-adjacent operators can use AI-driven logistics optimization to keep freight moving when conditions aren’t friendly.

What LA’s 10M TEUs really tells us (and why it’s not luck)

LA’s headline is simple: high throughput, low drama. The Port reported 782,249 TEUs in November, and total year-to-date traffic of 9,447,731 TEUs, sitting about 1% ahead of the prior year with another month still to count. November was down year-over-year because earlier frontloading inflated last year’s comps—exactly the kind of pattern that can mislead planning teams if they’re using basic trendlines.

The operational detail is the tell: port leadership emphasized no congestion and no ship backups during this run. That’s the outcome every gateway wants, and it doesn’t happen just because demand is strong. It happens when the ecosystem—terminal operators, drayage, rail partners, labor, and importers—keeps decisions aligned even as inputs change.

My stance: if you’re still treating ports as “fixed constraints” and planning everything upstream and downstream around static assumptions, you’re planning for a world that no longer exists. Volatility is the default now. The smart move is building a system where forecasting and execution talk to each other daily.

The hidden challenge: frontloading distorts your “normal”

Frontloading is a classic example of why AI forecasting is becoming mandatory.

• When importers pull demand forward to beat tariffs, monthly volumes lie.
• When carriers blank sailings or reshuffle strings, capacity signals lie.
• When empties move differently, future import indicators get noisy.

LA’s November mix shows the kind of segmentation you need to plan well:

• Loaded imports: 406,421 TEUs (down 11% year-over-year)
• Loaded exports: 113,706 TEUs (down 8%)
• Empties: 262,122 TEUs (down 13%)

A human planner can understand these numbers. The issue is doing it fast enough, across lanes, SKUs, customers, terminals, and rail ramps—without resorting to gut feel.

AI forecasting: the fastest way to turn trade chaos into a plan

The practical value of AI in logistics forecasting is straightforward: it separates signal from noise when demand is being pulled, pushed, or postponed.

Traditional forecasting often assumes the future resembles the past, with some seasonality and a few manual overrides. That breaks in tariff cycles because the “past” includes artificial spikes. AI models do better when they incorporate more explanatory variables and detect regime shifts.

What to feed your models (beyond shipment history)

If you’re building an AI demand forecasting approach for containerized imports, you’ll get more accuracy by adding inputs that reflect trade behavior:

• Tariff timelines and policy event calendars (effective dates matter more than announcements)
• Ocean capacity indicators (blank sailings, schedule reliability, dwell time)
• Port performance signals (truck turn times, rail velocity, terminal yard utilization)
• Inventory and point-of-sale demand for key categories (especially retail and consumer goods)
• Commodity and FX data for major sourcing regions

The goal isn’t a fancy dashboard. It’s producing forecasts that answer operational questions like:

• “Do we need more drayage capacity in the next 10–14 days?”
• “Which DCs will choke if arrivals shift by 72 hours?”
• “Should we transload at origin, at port, or inland this month?”

A simple “resilience KPI” I like

If you want a KPI that executives and ops teams both understand, track:

Forecast latency: the time between a meaningful external signal (tariff update, schedule change, capacity drop) and your network’s updated plan.

Teams that update plans weekly will lose to teams that update daily. AI helps close that gap.

AI-driven port and drayage optimization: how you prevent congestion before it shows up

Congestion is rarely a surprise. It’s usually the predictable result of small mismatches: appointment capacity vs. arrivals, chassis availability vs. yard density, rail cutoffs vs. gate labor. AI-driven optimization shines here because it can evaluate trade-offs faster than humans and keep re-optimizing as conditions change.

Where AI optimization pays off around a mega-port

For port-adjacent operations (terminals, drayage carriers, transload facilities, and 3PLs), the biggest wins tend to come from:

1. Appointment scheduling optimization

   • Predict no-shows and overbooking risk
   • Recommend appointment windows that smooth gate peaks
   • Prioritize pickups that reduce yard rehandles

2. Drayage dispatch and routing

   • Assign loads based on predicted turn times and traffic, not just distance
   • Optimize dual transactions (drop empty + pick loaded) to reduce deadhead
   • Forecast chassis constraints by pool and terminal

3. Yard planning and equipment allocation

   • Anticipate where containers will land based on vessel plan and downstream mode
   • Reduce rehandles by clustering by rail ramp, consignee, or cut-off date

4. Rail and intermodal coordination

   • Predict missed cutoffs and reroute to alternate ramps
   • Optimize container selection for trains based on dwell and destination priority

When a port says “not a single ship backed up,” you’re seeing the downstream effect of these micro-decisions going right more often than wrong.

“But we don’t run a terminal”—why this still matters to shippers

Even if you’re a BCO, importer, or 3PL, AI-driven logistics optimization can reduce your exposure to port volatility. If you can predict delays earlier, you can:

• Pull forward DC labor scheduling
• Rebook rail before capacity tightens
• Shift transload plans
• Avoid detention and demurrage by prioritizing the right pulls

You don’t need control of the port. You need predictive visibility plus operational authority over the decisions you actually own.

Automation as shock absorption: doing more with the same labor pool

One underappreciated theme in high-throughput years is that labor is both essential and finite. Even when a port has strong partners, peaks strain the system—especially around the holidays when demand surges and staffing is harder.

That’s why automation is increasingly less about “replacing people” and more about protecting them from peak chaos:

• Computer vision for gate processing and damage detection
• Automated exception handling for paperwork and release issues
• Robotics and AI slotting in transload warehouses near the port
• Automated yard equipment where feasible, with human oversight

The best implementations I’ve seen follow one rule: automate the repetitive decisions first, then use humans for exceptions and judgment. This is how you scale throughput without depending on heroics.

A realistic automation roadmap (90 days to 12 months)

If you’re a logistics operator trying to improve resilience in 2026 planning cycles, this progression works:

• 0–90 days: automate data ingestion and exception alerts (ETA changes, holds, last free day)
• 3–6 months: deploy AI forecasting for arrivals and drayage demand; connect outputs to labor planning
• 6–12 months: implement optimization for appointments, dispatch, and container prioritization

Don’t start with the hardest robotics project. Start with the workflows where you already have data and pain.

What leaders should do now: an AI resilience checklist for 2026

The Port of Los Angeles is proving you can move massive volume without gridlock, even in a messy trade environment. Shippers and logistics providers can take the same idea—decision speed beats perfect certainty—and apply it across their network.

Here’s a practical checklist I’d use going into 2026 contract season and peak planning:

1. Create a single “arrival truth” across ocean ETAs, terminal availability, rail cutoffs, and DC appointment capacity.
2. Model tariff and policy scenarios as probabilities, not one-off emergencies.
3. Measure forecast latency and set a target (e.g., updated plan within 24 hours of a major signal).
4. Automate the top 10 exceptions that waste operator time (holds, mismatched references, appointment misses, chassis unavailability).
5. Optimize drayage for turns, not miles—turn time is the constraint near ports.
6. Treat empties as a leading indicator, but validate with multiple signals (capacity, bookings, schedule reliability).

If your current tech stack can’t do these things without spreadsheets and late-night calls, you’ve found the real bottleneck.

The bigger story: smart logistics is becoming the default

LA’s near-10M-TEU year isn’t just about strong consumer spending or a good operational month. It’s a preview of where the industry is headed: ports, carriers, and inland networks that run on predictive systems will keep freight flowing; everyone else will keep paying for surprises.

As part of our AI in Transportation & Logistics series, this is the throughline I keep coming back to: AI isn’t valuable because it sounds futuristic. It’s valuable because it makes planning and execution act like one system—fast, measurable, and repeatable.

If you’re mapping your 2026 resilience strategy now, ask yourself one forward-looking question: When the next tariff deadline or routing disruption hits, will your network respond in hours—or in weeks?