Truck Jobs Hit a Low—AI Keeps Freight Moving

Truck transportation employment just printed a number the industry hasn’t seen in years: 1,509,600 jobs in November 2025, the lowest level since June 2021. That’s not a headline you can file under “interesting trivia.” It’s a capacity signal.

Here’s the part most teams miss: labor isn’t just a hiring problem—it’s a planning problem. When driver counts fall while freight still has to ship (and seasonal surges still show up), the winners aren’t the companies with the flashiest recruiting campaigns. They’re the ones that can do more with the people and trucks already on the roster.

This post is part of our “AI in Trucking & Freight: Fleet Intelligence” series, and the point is simple: AI-driven logistics isn’t about replacing drivers—it’s about protecting service levels when labor, regulations, and demand stop behaving nicely.

What the latest BLS trucking jobs data really says

Answer first: The BLS data shows trucking employment is sliding consistently, even as wages rise—meaning the market is paying more per hour while still losing people.

According to the Bureau of Labor Statistics, truck transportation employment fell to 1,509,600 in November 2025, down from 1,514,000 in October (seasonally adjusted). With revisions included, employment is down 13,800 jobs from July’s 1,523,400.

Put that in context:

• The all-time high was 1,587,900 jobs in July 2022.
• Current levels are roughly 5% below that peak.
• November 2025 is the lowest since June 2021.

At the same time, the average hourly wage for production and nonsupervisory trucking employees hit a new high: $31.40 (through October, the latest month available), up from $31.10, and up from $29.88 a year earlier.

That combination—fewer workers, higher wages—usually means one thing for shippers and brokers: you’re buying reliability at a premium, and you still might not get it during spikes.

Why the “regulatory bite” matters operationally

Answer first: If enforcement reduces the pool of eligible drivers, capacity tightens fastest in the messy places—spot markets, short lead-time lanes, and peak weeks.

Industry commentary tied part of the employment drop to crackdowns on non-English speaking and non-domiciled CDL holders, with a warning that reduced capacity can bring spot-rate volatility and a higher likelihood of disruptions.

Whether you agree with the policy or not, operationally it creates a familiar pattern:

1. Capacity looks fine… until it isn’t.
2. Tender rejections creep up in specific regions.
3. Service failures concentrate around holidays, weather, and promotions.

December 2025 is a perfect example of the “quietly dangerous” period: holiday delivery demand, winter weather risk, and end-of-year inventory moves collide. If your network still runs on static routing guides and manual dispatch heroics, you’re exposed.

Labor declines don’t reduce freight—so efficiency becomes your capacity

Answer first: When trucking jobs fall, the “new capacity” comes from better utilization: fewer empty miles, tighter appointment planning, and smarter load-driver matching.

Most fleets already know the basics: empty miles are expensive, detention kills turns, and last-minute changes burn your dispatcher’s day. What’s changed is the tolerance for inefficiency.

When the labor pool shrinks, every avoidable waste gets louder:

• A 45-minute late arrival that triggers a missed dock appointment becomes a two-hour detention.
• A slightly wrong trailer pre-plan creates a deadhead reposition.
• A manual driver assignment that ignores HOS realities forces a costly relay.

Fleet intelligence (AI + operations data) is how you keep service steady without pretending you can hire your way out.

The “do more with less” stack: where AI actually helps

AI in transportation and logistics is most valuable when it targets repeatable decisions that humans make under time pressure. In trucking, that’s a long list.

Practical AI-driven logistics solutions that directly offset labor constraints:

• Route optimization that respects real constraints (HOS, appointment windows, vehicle type, driver preferences)
• Dynamic dispatching that re-optimizes when a load cancels or a facility runs behind
• Predictive ETAs using lane history + weather + facility dwell patterns
• Automated load matching to reduce manual planning time and minimize deadhead
• Detention and dwell prediction to avoid “time traps” that wreck turns
• Network-level forecasting that anticipates tightness by region and week, not after the spot market spikes

If you’re leading operations, this matters because the best outcome isn’t “we saved 3% on cost.” It’s we didn’t fail our service commitments during a labor squeeze.

A playbook for applying AI when driver availability is tightening

Answer first: Start with high-friction workflows—then connect planning, execution, and feedback loops so the system learns.

A lot of AI initiatives in trucking stall for one reason: teams start with the fanciest model instead of the ugliest operational pain.

Here’s what I’ve found works: pick problems that (1) happen daily, (2) have clear cost/service impact, and (3) produce measurable outcomes within 60–90 days.

Step 1: Attack empty miles and missed turns first

Answer first: Empty miles and low turns are “silent headcount drains.” Fixing them is like adding trucks without buying trucks.

Use AI-assisted planning to:

• Combine compatible pickups/deliveries into tighter tours
• Balance headhaul/backhaul pairing by probability, not hope
• Pre-position capacity based on forecasted tender volumes

Operational metric targets that are realistic for many fleets:

• Reduce empty miles by 5–12% on targeted networks
• Increase turns per truck per week by improving appointment fit and reducing dwell

Even small improvements matter when employment is falling. A fleet that increases utilization often avoids the panic of “we need 30 more drivers by Q1.”

Step 2: Make ETA accuracy a service product (not a guess)

Answer first: Better ETAs reduce detention, prevent missed appointments, and cut the labor needed to manage exceptions.

ETA accuracy isn’t just “nice visibility.” It’s how you:

• Trigger re-appointments early
• Re-route around weather and congestion
• Coordinate yard and dock labor
• Reduce check calls and exception handling

If your team is still spending hours chasing location updates, AI-driven ETA + automated exception alerts is a direct labor offset. Less time on the phone. More time solving real problems.

Step 3: Build an exception-first dispatch model

Answer first: The goal isn’t to automate dispatch—it’s to automate routine decisions so humans focus on exceptions.

The best dispatchers don’t want an algorithm telling them what to do all day. They want the system to:

• propose the top 2–3 feasible driver/load matches,
• explain the tradeoffs (cost vs. on-time risk vs. driver hours), and
• surface risks early (facility delays, weather, HOS pinch points).

That approach tends to win internal adoption because it respects the human edge: judgment under ambiguity.

Step 4: Treat regulatory constraints as inputs, not surprises

Answer first: If regulatory changes shrink capacity, AI planning systems must incorporate compliance constraints upfront.

Whether the trigger is enforcement intensity, credential validation, or shifting contractor models, your system needs structured data for:

• driver qualification and credential status
• equipment eligibility by customer/facility
• lane restrictions and time windows
• safety and compliance scoring

When compliance is embedded into the planning engine, you reduce last-minute load swaps and the “we can’t cover this legally” scramble.

What about rail, warehousing, and couriers? The network is wobbling, not just trucking

Answer first: The BLS report hints at broader logistics labor volatility—meaning AI has to optimize across the network, not inside one silo.

The same report showed:

• Rail employment continuing to slide (down from an early-2024 peak, with November at 150,800, the lowest since Feb 2023)
• Courier employment dropping sharply from October to November (an unexpected pattern for peak season)
• Warehouse employment ticking up slightly in November (+1,900 jobs) after several months of decline

That mix matters because disruptions cascade.

If parcel carriers lean harder on contract models, or if courier networks pull back in certain metros, you often see:

• more freight pushed to regional LTL and final-mile partners,
• more time-sensitive shipments moving to premium truckload,
• more pressure on appointment scheduling and yard flows.

This is where AI-driven supply chain forecasting becomes practical—not academic. The goal is to anticipate where the network will bend so you can shift mode, inventory positioning, and capacity plans before service breaks.

“AI replaces drivers” is the wrong frame

Answer first: The near-term value of AI in trucking is productivity: higher utilization, fewer failures, and less manual coordination per load.

When trucking jobs hit multi-year lows, it’s tempting to jump straight to autonomy headlines. But most fleets don’t need sci-fi. They need throughput.

AI is already strong at:

• optimizing multi-stop routes with constraints
• predicting dwell and arrival risk
• automating appointment coordination and exception workflows
• recommending pricing and capacity actions based on forecasted tightness

That’s how you keep freight moving when hiring is hard, wages are up, and regulations tighten the pool.

If your operation requires perfect labor availability to hit service targets, it isn’t resilient.

Resilience comes from systems that assume variability and still perform.

A practical checklist for 2026 planning (use it this week)

Answer first: If trucking employment stays low into 2026, you’ll need tighter planning loops and faster decision cycles.

Use this checklist as a starting point:

1. Map your top 20 “failure lanes.” Where do tenders fail, dwell spikes, or service misses repeat?
2. Quantify manual work per load. How many touches from quote to POD? Track it.
3. Pick one optimization target. Empty miles, turns, ETA accuracy, or appointment adherence.
4. Integrate constraints early. HOS, equipment, facility windows, credential rules.
5. Automate alerts, not just dashboards. If a dispatcher has to watch a screen, it won’t scale.
6. Close the loop. Feed outcomes back into the model: dwell, rejections, late reasons, accessorials.

Do these six things and you don’t just “add AI.” You build a fleet intelligence loop that gets sharper each month.

What to do next if your team wants fewer disruptions (and fewer fire drills)

Truck transportation jobs are at their lowest level since mid-2021, and the data doesn’t suggest a quick snap-back. Planning for stable service in 2026 means planning for constrained labor.

If you’re responsible for cost, service, or capacity, the most pragmatic move is to prioritize AI where it directly offsets labor pressure: route optimization, predictive ETAs, exception automation, and smarter load matching.

Where do you feel the labor squeeze first—coverage, dispatch bandwidth, detention, or last-mile execution? That answer usually tells you exactly where fleet intelligence should start.