10M TEUs at Port of LA: The AI Playbook for 2026

Port of Los Angeles is on track to finish 2025 north of 10 million TEUs, landing the year in its top three of all time—and doing it “without congestion and not a single ship backed up,” according to port leadership.

That’s the headline. The subtext is more useful: high throughput is no longer the hard part—predictable throughput is. When a port can move 9.45M TEUs through November (with November at 782,249 TEUs, down 12% year over year due to earlier frontloading), the next competitive advantage shifts to the rest of the network: drayage, rail, yard planning, warehouses, and the final mile.

In our AI in Transportation & Logistics series, we keep coming back to one idea: the winners aren’t the companies with the most data—they’re the ones that turn port signals into decisions fast. Port of LA’s volume story is a reminder that AI isn’t a vanity project. It’s how you keep velocity when tariffs, trade detours, and one-off disruptions try to steal it.

Why 10M TEUs matters (it’s not a brag, it’s a stress test)

10 million TEUs is a live-fire test of the entire import and intermodal ecosystem. For shippers and logistics leaders, Port of LA’s 2025 performance answers a practical question: can the gateway absorb volatility—tariffs, shifting sailing patterns, even incidents like a shipboard fire—without cascading delays?

At this scale, small mistakes become huge bills. If just 1% of monthly containers miss an appointment window, your “minor exception” is suddenly thousands of loads of detention, missed DC cutoffs, and angry customers.

What I like about this specific moment (December 2025) is that it’s not a crisis narrative. The port is moving freight. The market is still uncertain. That combination is exactly where AI helps most: when operations are stable enough to implement change, but volatility is high enough to justify it.

The signal hidden in November’s drop

November was down year over year because 2024 levels were inflated by frontloading. That pattern—pulling volume forward to avoid risk—is one of the clearest reasons to invest in AI-powered demand forecasting.

Frontloading is rational, but it’s expensive:

• Warehouses get crushed early, then sit underutilized later n- Drayage capacity spikes, then softens
• Inventory carrying costs rise
• OTIF becomes harder to maintain

AI doesn’t eliminate frontloading, but it can quantify tradeoffs in plain English: “If you pull 3 weeks of volume forward, here’s the expected cost in storage, labor, chassis dwell, and expedited linehaul—versus the risk cost of waiting.”

Ports don’t “run out of capacity” first—they run out of coordination

The bottleneck in modern port logistics is coordination across independent parties. Port of LA’s leadership credited longshore labor, truckers, terminal operators, and rail partners for keeping freight flowing. That’s accurate—and also the core challenge.

A port community is a distributed system:

• Terminals optimize for yard productivity and crane rates
• Drayage carriers optimize for turns and appointment availability
• Rail optimizes for train starts, dwell, and block integrity
• Importers optimize for inventory and DC flow

No one party sees the whole picture in real time. That’s where AI and automation earn their keep.

What “AI for port operations” actually means

For most organizations, AI in port and intermodal logistics breaks into four concrete layers:

1. Prediction (what will happen)

   • import volume forecasting
   • ETA prediction at vessel, rail, and truck levels
   • dwell time prediction by terminal, customer, and SKU class

2. Optimization (what should we do)

   • appointment slot optimization
   • yard and warehouse labor planning
   • drayage dispatch and load matching

3. Orchestration (how we coordinate)

   • exception workflows when ETAs shift
   • automated rebooking and replanning
   • shared visibility with role-based alerts

4. Learning loops (how we get better)

   • post-mortems that update models
   • root-cause tagging (equipment, documentation, congestion, weather)
   • KPI attribution (what actually moved the needle)

If your “AI initiative” doesn’t map to one of these layers, it’s probably going to stall.

Trade uncertainty is exactly why AI forecasting belongs in your TMS

Tariffs and geopolitics don’t just change rates—they change behavior. The article points to “trade uncertainty” and tariff headwinds. In practice, those pressures show up as:

• earlier purchase orders (frontloading)
• supplier shifts (China to alternative origins, or multi-origin splits)
• different port pairs and rail ramps
• more frequent re-planning

Traditional planning tools assume tomorrow looks like yesterday. That’s the wrong assumption for 2026.

A practical forecasting stack for importers

If you’re an importer, 3PL, or BCO moving meaningful volume through LA/Long Beach, here’s a forecasting approach that works in the real world:

• Baseline forecast: historical shipments by SKU family + seasonality
• Leading indicators: bookings, PO creation, ASN creation, supplier lead-time changes
• Port signals: TEU volume indices, vessel schedules, blank sailings, terminal dwell
• Downstream constraints: warehouse labor capacity, appointment calendars, promo events

The win isn’t “a forecast.” The win is a forecast tied to decisions:

• when to pull inventory forward
• how many drayage carriers to stage
• whether to route inland via rail vs transload
• where to place safety stock

A good AI system doesn’t just output a number. It outputs actions with confidence bands.

“No congestion” is fragile—AI is how you keep it that way

Port of LA’s statement that cargo moved without congestion is impressive. It’s also fragile because it depends on many variables staying aligned: labor availability, chassis supply, terminal velocity, rail fluidity, and importer pickup discipline.

The simplest way to break a smooth port is poor pickup timing:

• importers pick up too early → yards get cluttered, warehouses overflow n- importers pick up too late → free time expires, dwell rises, terminals slow

Where AI helps immediately: dwell and appointment discipline

Dwell time prediction is one of the most bankable AI use cases in port logistics because it connects directly to cost.

What you can do with it:

• Prioritize pickups for containers likely to go “past free time”
• Automatically recommend appointment slots based on predicted availability
• Trigger exceptions early (customs holds, documentation gaps, exam risk)
• Set customer expectations with realistic delivery windows

Here’s a blunt stance: if your team still triages port containers in spreadsheets, you’re paying a hidden tax. You might not see it on one shipment. You’ll feel it across a peak season.

Incident resilience: planning for the weird stuff

The article notes a ship fire (with no measurable impact on operations). That’s a good outcome. But incidents do happen—fires, weather, crane outages, labor disruptions, cyber events.

AI doesn’t prevent incidents. It reduces blast radius by:

• simulating downstream impact (which DCs will stock out first)
• recommending reroutes (alt ports, rail ramps, transload locations)
• prioritizing the “most painful” containers first (high-margin SKUs, promo deadlines)

Resilience is a planning function, not a heroics function.

From port to doorstep: turning TEUs into service levels

High container volume only matters if the freight arrives where it needs to go—on time, intact, and at a cost you can defend.

Port of LA’s November details are useful because they hint at what’s next:

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

Even without overinterpreting a single month, the operational takeaway is straightforward: imports, exports, and empties compete for yard space, equipment, and gate throughput. If you’re only optimizing your piece (say, drayage dispatch), you’ll miss system-wide constraints.

The “network digital twin” mindset

A practical north star for AI in transportation and logistics is a lightweight network digital twin—not a sci-fi 3D model, but a living model of constraints and flows:

• port/terminal dwell distributions
• gate appointment capacity
• drayage cycle times by lane and hour
• rail dwell and departure reliability
• DC receiving capacity and labor plans

When this model updates daily, you can answer questions leadership actually asks:

• “If we get a 15% volume spike next month, where do we break first?”
• “What’s the cheapest way to protect service for our top 200 SKUs?”
• “Which carriers and ramps are most reliable under peak conditions?”

Implementation: the 30-60-90 plan that doesn’t melt your ops team

AI projects fail when they ask operations to change everything at once. The better pattern is to start with narrow decisions tied to measurable KPIs.

First 30 days: get the data boring and usable

• Map container lifecycle events (booking → discharge → availability → pickup → in-gate DC)
• Normalize identifiers (container, B/L, PO, SKU, location)
• Define three KPIs everyone agrees on: dwell, demurrage/detention, OTIF

Next 60 days: deploy one model that pays for itself

Pick one:

• dwell time prediction to reduce demurrage
• drayage ETA prediction to reduce missed appointments
• appointment recommendation to improve turns

Make it visible where work happens (TMS view, dispatch screen, exception queue).

By 90 days: close the loop with exception automation

• auto-create tasks when risk crosses a threshold
• route exceptions to the right owner (customs, carrier rep, warehouse)
• track outcomes so the model learns

If you can’t measure “before and after,” you’re not running a product—you’re running a science fair.

What to do next if your 2026 plan depends on Port of LA

The Port of Los Angeles nearing 10M TEUs is a signal that the gateway can perform. Your bigger risk is everything around it: forecasting errors, appointment chaos, warehouse constraints, and slow exception handling.

If I were advising an importer or 3PL heading into 2026, I’d start with three commitments:

1. Treat port visibility as a control system, not a dashboard. Alerts should trigger actions, not meetings.
2. Put AI where decisions get made. If dispatchers and planners won’t use it, it doesn’t exist.
3. Optimize across modes. Port, dray, rail, and warehouse are one problem wearing different uniforms.

The open question for 2026 isn’t whether Port of LA can handle volume. It’s whether the rest of the supply chain will finally operate at the same speed—and whether your organization will be the one that turns port-scale signals into port-scale decisions.