AI-Ready Red Sea Shipping: Route Risk Without Guesswork

Vessel arrivals in the Gulf of Aden dropped 65% in November versus the same month in 2023. That’s not a “normal” seasonal swing—it’s what happens when security risk suddenly becomes a routing constraint.

Now carriers are testing the waters again. Ocean Network Express (ONE) is returning to the Red Sea with a new Red-Sea China Service (RCS) via a slot charter agreement, timed alongside reports of de-escalation following a Gaza ceasefire. The detail that should grab every shipper and logistics planner: the service doesn’t transit the Suez Canal. It’s an explicit reminder that “back to normal” isn’t a plan.

For teams managing transportation and logistics, this is where AI earns its keep. When the route itself becomes a variable—changing week to week based on geopolitics, insurance conditions, port congestion, and customer service commitments—manual playbooks break down. The smartest operators treat a Red Sea return as a data problem first: predict risk, price volatility, schedule reliability, and downstream effects before capacity is committed.

What ONE’s Red Sea return signals for global routing

ONE’s move says one thing clearly: demand is pulling capacity back toward Red Sea ports, but the industry isn’t ready to bet everything on a single chokepoint. A slot charter (rather than a full standalone network redeploy) is a classic “probe the market” strategy—get exposure, learn quickly, keep options open.

The announced service is fortnightly, beginning with a sailing from Shanghai on Jan. 15, using an eight-week rotation: Shanghai, Qingdao, Nansha, Shekou; then Jeddah, Sokhna, Aqaba; and return via Jeddah, Shanghai, Qingdao. Reported vessel capacity is around 3,000 TEUs, typical for feeder-type deployment.

Why bypassing Suez matters

Choosing not to transit Suez is more than a footnote. It implies the service design is optimizing for:

• Reduced exposure to specific high-risk transit segments
• More controllable schedule buffers (even if longer on paper)
• More predictable insurance and security requirements

For shippers, that changes how you think about lead time. A “safe” route that’s consistently 3–5 days longer often beats a shorter route with a 25% chance of disruption. Reliability is an inventory strategy.

The December 2025 planning reality

We’re heading into the post-peak, early Q1 window where many teams reset contracts, re-score carrier performance, and re-balance inventory after holiday demand. If your 2026 plan assumes stable ocean transit times through the Red Sea region, you’re building on sand.

A better stance is to assume route volatility is structural and invest in decision systems that can react in hours, not weeks.

Why AI is built for high-risk maritime corridors

AI is valuable here because Red Sea routing isn’t just “choose Route A or Route B.” It’s a multi-variable optimization problem with feedback loops: a security event changes sailings; sailings change port dwell; dwell changes equipment availability; equipment availability changes spot rates; spot rates change booking patterns; booking patterns change blank sailings.

Humans can reason about parts of this. AI can monitor and recompute the full system continuously.

AI route optimization: beyond shortest path

In ocean logistics, “best route” usually means the best tradeoff across:

• Estimated time of arrival (ETA) reliability (not just average transit time)
• Service frequency and missed-connection risk
• Port congestion probability
• Security risk exposure by segment
• Total landed cost (freight, insurance, demurrage/detention risk, buffer inventory)

A practical AI routing approach uses a risk-adjusted cost function. Instead of optimizing a single number (like transit time), it optimizes an objective like:

Minimize total expected cost = freight + delay penalties + disruption probability × disruption impact

That last term—probability times impact—is what most spreadsheets ignore, and it’s exactly what high-risk corridors demand.

Risk forecasting: treating geopolitics as an input signal

You don’t need AI to read headlines. You need AI to translate signals into operational decisions. Useful inputs include:

• Historical incident patterns by corridor segment
• Naval advisories and security level changes (as structured risk flags)
• Insurance premium shifts and underwriter constraints
• AIS-derived traffic density and rerouting behaviors
• Carrier schedule changes and blank sailing announcements

The output shouldn’t be a vague “risk score.” It should be something planners can use tomorrow morning:

• “Route via corridor X has 18% higher probability of missing the connection at port Y this week.”
• “If you keep current safety buffers, you’ll add $0.12/unit in carrying cost; if you reduce buffers, stockout risk rises by 6 points.”

Those are decisions, not dashboards.

What changes operationally when a carrier re-enters the Red Sea

A carrier’s return doesn’t just affect ocean freight. It ripples into inland transportation, warehousing, and customer promise dates.

1) Inventory planning needs scenario-based ETAs

When routing options multiply, the right question isn’t “what’s the ETA?” It’s:

• What’s the ETA distribution (P50, P80, P95)?
• What’s the probability of a hard disruption (missed sailing, diversion, port closure)?

AI-powered supply chain forecasting can model demand alongside transit uncertainty to recommend:

• How much safety stock to hold by SKU and region
• Which SKUs can tolerate variability (slow movers) and which can’t
• Where to pre-position inventory if Red Sea reliability changes

If you’re only tracking average transit time, you’re underestimating the cost of variability.

2) Port and terminal strategy becomes a competitive edge

ONE’s rotation includes Jeddah, Sokhna, and Aqaba—each with different inland connections, customs profiles, and congestion patterns.

AI can help answer tactical questions that matter in execution:

• If Jeddah dwell time spikes, which shipments should be re-booked through Sokhna?
• Which customers are most sensitive to late delivery penalties?
• Which containers should be prioritized for discharge and drayage based on downstream appointments?

This is where AI intersects with transportation management systems (TMS) and appointment scheduling. The win isn’t “visibility.” The win is automated prioritization.

3) Contracting and procurement shifts from rate-first to reliability-first

When carriers “test” routes with slot charters and smaller vessels, service consistency can vary. Procurement teams should treat Red Sea service options like a portfolio.

AI-supported procurement can:

• Score carriers and services by on-time performance and volatility
• Predict which lanes are likely to see capacity tightening (and when)
• Recommend a mix of contract + spot coverage based on forecast risk

My take: if your ocean buying strategy is still mostly annual RFP + quarterly check-ins, you’re leaving money on the table in volatile corridors.

A practical AI playbook for shippers and 3PLs (next 30 days)

If you’re evaluating Red Sea routings again—whether via ONE’s RCS or other services—here’s what works in practice.

Build a “risk-adjusted lane” view (not a static lane guide)

Create lanes that include risk variables, not just origin/destination.

At minimum, track:

• Corridor exposure (segments and chokepoints)
• Alternative discharge ports
• Connection dependencies (transshipments)
• Buffer time assumptions by customer tier
• Cost of delay (per day, per lane)

Then use AI (or a lighter-weight model) to recompute recommended routings weekly.

Set decision triggers that don’t require a meeting

Volatile routes punish slow governance. Define triggers like:

• If disruption probability > X for 72 hours, switch to alternate routing
• If schedule reliability falls below Y for two consecutive sailings, reallocate volume
• If port dwell exceeds Z days at a node, re-prioritize drayage capacity

Good triggers turn “everyone’s watching the news” into a controlled operating model.

Connect ocean changes to inland capacity planning

The most common failure mode: ocean teams change routing, and inland teams find out late.

Use AI-driven flow forecasting to predict:

• Weekly container arrivals by port
• Required dray moves by day
• Warehouse receiving labor requirements
• Potential chassis/equipment constraints

Even simple forecasts reduce expensive last-minute premium moves.

Common questions teams ask about returning to Red Sea routes

Is a ceasefire enough to restore stable shipping schedules?

A ceasefire can reduce immediate risk, but schedule stability depends on carrier network decisions, insurance conditions, and whether traffic actually returns at scale. Expect uneven reliability during the “re-entry” period.

Why would a service avoid the Suez Canal if it’s a key shortcut?

Because the optimization target often isn’t shortest distance—it’s controllability. If a corridor introduces concentrated risk (security, cost spikes, sudden closures), planners may prefer a route that’s longer but more predictable.

What’s the first AI use case to prioritize if we’re not mature yet?

Start with ETA prediction + exception risk scoring for your highest-value lanes. If you can identify which containers are likely to miss downstream commitments 7–10 days earlier, you can re-book, expedite, or re-allocate inventory before the cost explodes.

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

In this series, we keep coming back to a simple point: AI is most valuable where variability is expensive. The Red Sea is a perfect example. When a single routing decision can swing inventory buffers, customer service levels, and freight spend, you need more than static SOPs.

ONE’s return with a Red Sea China service is a real-world signal that carriers are exploring capacity reallocation—but doing it cautiously. Shippers and 3PLs should be at least as disciplined: treat high-risk maritime routes as a probabilistic planning problem, then use AI to convert uncertainty into repeatable decisions.

If you’re preparing your 2026 ocean strategy, the question I’d ask isn’t “Are we going back to the Red Sea?” It’s: Do we have a system that can tell us when to go back—and when to pull back—before the disruption hits our customers?