Cass TL Index Up Again: AI Moves to Make Now

Freight volumes are still falling, yet truckload linehaul rates keep climbing. That’s not a paradox—it’s a warning label.

Cass Information Systems’ latest read on the market shows November shipments down 7.6% year over year, while the Cass TL Linehaul Index rose again (its third straight month of sequential gains). Total freight spend stayed close to last year because rates did the heavy lifting.

If you run transportation procurement, manage a fleet, or lead a 3PL/brokerage desk, this matters for one reason: the market is getting harder to “feel” by gut. Volumes can be soft and rates can still tighten fast when weather, regulation, and capacity mix changes collide. In the AI in Transportation & Logistics series, this is exactly the kind of setup where AI earns its keep—forecasting cost risk earlier, improving routing decisions, and helping you buy capacity with less regret.

What the Cass TL Linehaul Index is telling you right now

The simplest read: rates are rising even while shipments are weak, and that combination often signals a fragile capacity picture.

Cass reported:

• Shipments: -7.6% y/y in November (but +0.7% m/m, +2.7% SA)
• Expenditures: -1.2% y/y (near flat), implying rate pressure
• TL Linehaul Index: +2.2% y/y and +0.1% m/m (third consecutive sequential increase)

Cass also projected full-year 2025 volumes down ~6% y/y, with December expected to be down again.

Why rising linehaul rates with falling volumes happens

This pattern shows up when the market tightens locally and temporarily even if national demand is muted. November and December frequently do this due to peak season and network imbalances, but 2025 has extra ingredients:

• Weather-driven disruption: multiple winter storms (aligned with a La Niña pattern) compress effective capacity for days at a time.
• Driver supply constraints and enforcement: more scrutiny around non-domiciled CDL restrictions, English language proficiency expectations, and compliance crackdowns can reduce usable capacity.
• Freight mix shifts: Cass flagged it’s reassessing inferred rate data partly due to mix moving between modes (for example, LTL vs. TL). Mix changes can make “average rate” measures look calmer—or hotter—than your own book of business.

Here’s the operational takeaway I’d bet on: your cost risk is increasingly about variability, not just the average. And variability is where AI models usually outperform spreadsheets.

Why “flat spend” can still blow up your budget

When spend looks stable year over year, it’s easy to assume your transportation budget is under control. Most companies get this wrong.

Cass’ numbers suggest a scenario like this:

• You ship fewer loads (down 7.6%)
• But you pay more per mile/load (rates up; Cass implied ~7% y/y rate lift in November based on spend vs. volume)
• Your total spend doesn’t move much

That sounds fine—until you consider where the rate increases land.

The hidden problem: cost concentration

Rate inflation rarely spreads evenly. It clusters in:

• storm-impacted regions
• tight outbound markets
• short-notice tenders
• lanes with driver availability constraints

So your annual spend can look “flat” while a handful of critical lanes become budget wreckers. AI-driven transportation analytics can surface that concentration early by automatically flagging:

• lane-level cost drift (rate change vs. historical baseline)
• volatility hotspots (markets where price variance is widening)
• service-risk correlation (where rising rates coincide with rising fall-offs, late pickups, or tender rejections)

If you’re still reviewing lane performance quarterly, you’re basically choosing to learn about cost spikes after they’ve already happened.

Where AI fits: turning market indexes into lane-level decisions

The Cass TL Linehaul Index is a market signal. Your TMS data is a reality check. The value comes from combining them.

A practical way to think about AI in transportation management is: it converts “macro signals” into “micro actions.”

1) AI for freight rate forecasting (not just reporting)

Answer first: Use AI to predict your next 2–8 weeks of rate exposure by lane, not just the national average.

What works in practice:

• Train a model on your historical tender/accept data plus external signals (index direction, seasonal patterns, weather disruption indicators, capacity proxies like rejection trends).
• Forecast distributions (p50/p90) rather than one point estimate.

Why distributions matter: when linehaul is climbing in a soft-volume environment, your p90 outcomes tend to move faster than your averages.

Example: A shipper with steady contract coverage might still see spot usage jump from 8% to 18% on storm weeks. A model that forecasts spot share and rate tails helps you set a budget buffer that’s defensible.

2) AI for capacity planning under “fragile” conditions

Answer first: Use predictive models to identify lanes where acceptance is likely to drop before it drops.

In Cass’ report, the narrative is clear: capacity can tighten quickly due to holiday demand surprises and operational constraints. AI can translate that into actions like:

• pre-booking backup carriers on lanes with high predicted failure risk
• shifting pickup windows proactively
• rebalancing mode choices (multi-stop TL vs. LTL, pool distribution, etc.)

If you wait for tender rejections to spike, you’ll pay peak pricing and you’ll burn relationships.

3) AI route optimization when weather compresses capacity

Answer first: When storms hit, the winning move is often “less clever routing,” not more.

During disruptive weeks, AI route planning should prioritize:

• reliability of corridors
• dwell time risk at facilities
• driver hours feasibility

That means your optimization objective function should temporarily overweight service stability and hours-of-service feasibility, even if miles increase.

I’ve found that many teams keep the same routing rules year-round. Winter peak doesn’t reward that. Your routing system should behave differently in December than it does in May.

A simple playbook: what to do if linehaul keeps rising into early 2026

Rates rising three months in a row doesn’t guarantee a sustained upcycle, but it’s enough to justify preparation—especially with tariffs and affordability pressures likely spilling into 2026.

Shippers: protect service without overbuying capacity

Answer first: Add targeted resilience, not blanket contract inflation.

Actions worth taking in the next 30 days:

1. Lane segmentation: classify lanes into “strategic,” “volatile,” and “low-risk.” Only pay for resilience where it matters.
2. Dynamic routing policies: implement winter/peak rules in the TMS (or an AI layer) that trigger when disruption signals cross thresholds.
3. Mini-bids, not mega-bids: rebid the top 20 cost-risk lanes monthly/bi-monthly instead of reopening everything.
4. Score incumbents realistically: reward carriers that performed during disruption, not just those who looked good in calm months.

Carriers: price with discipline, but don’t ignore volume softness

Answer first: Rising spot can be real even if the cycle isn’t. Treat it like a harvest, not a new normal.

Operational moves:

• Use AI to predict network imbalance and repositioning needs (empty miles are where margin goes to die).
• Identify which customers create detention and reschedule risk during storms; price that explicitly.
• Lock in select contract improvements where your service reliability is strongest.

Brokers/3PLs: win with visibility and exception management

Answer first: Your edge in this market is speed of replan and accuracy of ETA/cost forecasts.

If driver pool constraints and weather are squeezing capacity, the broker who can:

• predict which tenders will fail,
• re-source quickly,
• and communicate accurate ETAs

…keeps the customer and protects margin. AI-based exception management (late pickup risk, facility dwell prediction, weather reroute suggestions) is often a better ROI than yet another dashboard.

People also ask: how should I use the Cass Index in procurement?

Use it as a timing signal, not a pricing engine. The Cass TL Linehaul Index is great for confirming direction (rates up vs. down), but procurement decisions happen at lane, origin market, and service-level detail.

A practical workflow:

• If the index is rising 2–3 months, trigger a “tightening review.”
• Pull your top lanes by spend and volatility.
• Use AI forecasting to estimate next-month exposure (spot share, rate p90).
• Decide where to add carriers, adjust routing, or change contract/spot mix.

That turns a headline into an operating plan.

The bigger point: market volatility is now a data problem

Cass’ November report paints a market where volume softness and rate firmness coexist because constraints are increasingly episodic—storms, compliance enforcement, and sudden capacity mix changes.

Transportation leaders who treat this as a “watch the market” moment will react late. Leaders who treat it as a model the market moment will buy capacity earlier, route smarter, and avoid the ugliest spot spikes.

If you’re building out AI in transportation and logistics, this is a clean starting use case: take a trusted external index, fuse it with your shipment history, and predict where cost and service risk will pop next.

Where do you see the biggest pain right now—unpredictable spot exposure, tender rejections, or weather-driven service failures? That answer usually points to the first AI workflow you should implement.