Truck Jobs Hit a 2021 Low—AI Fills the Capacity Gap

Truck transportation employment just printed a number the industry hasn’t seen since mid-2021: 1,509,600 jobs in November 2025. That’s not a rounding error. It’s down 13,800 jobs from July’s 1,523,400 (a recent peak), and roughly 5% below the all-time high of 1,587,900 in July 2022.

Most companies get the story wrong here. They treat fewer trucking jobs as a simple labor headline—hire harder, pay more, wait it out. The operational reality is harsher: when capacity tightens unexpectedly (especially in peak seasons), your network gets fragile fast. One missed appointment turns into detention. One late pickup cascades into service failures. Costs spike while customer patience drops.

This post is part of our “AI in Trucking & Freight: Fleet Intelligence” series, and I’m taking a stance: the right response to shrinking trucking employment isn’t panic hiring—it’s building a smarter, more automated operating model. AI won’t replace drivers. It will make each available driver, dispatcher, and tractor-trailer combination produce more reliable outcomes.

What the November 2025 trucking jobs data really says

Answer first: The BLS numbers point to a multi-month erosion in trucking employment that increases the odds of spot-market volatility, tighter capacity during surges, and more service disruptions.

According to the BLS (seasonally adjusted), truck transportation employment fell from 1,514,000 in October to 1,509,600 in November. With revisions included, the bigger signal is the slide since July.

Two details make this more than “normal churn”:

• Timing: November is typically not when you want surprises. Retail peak, weather impacts, year-end shipping pushes—operations are already stressed.
• Price behavior vs. demand narrative: Industry commentary noted seasonal rate increases even while demand has been described as weak. That combination is often what you see when capacity is quietly disappearing.

And it’s not just trucking. The same report highlighted:

• Courier employment falling sharply month over month (Oct to Nov), surprising for peak season.
• Rail employment continuing a slow decline from earlier highs.
• Warehouse employment ticking up slightly after several months down.

The practical takeaway for shippers, brokers, and carriers is simple: labor isn’t reliably expanding to meet seasonal complexity. Your planning systems have to get better.

“Regulatory bite” is a capacity story, not a political one

Answer first: When enforcement changes remove drivers from the market, you feel it as capacity compression, and AI tools become essential for prioritization and exception handling.

Industry experts have attributed some job losses to crackdowns tied to non-English speaking and non-domiciled CDL holders, predicting more volatility and disruption risk.

Regardless of where you land on the policy debate, the operations impact is predictable:

• Fewer available drivers on certain lanes n- More last-minute tender rejections
• Higher spot exposure for shippers
• More margin pressure for brokers who price too confidently

In other words, the network gets noisier. And noisy networks punish manual decision-making.

Why fewer trucking jobs increases the ROI of fleet intelligence

Answer first: As labor tightens, the value of AI shifts from “nice efficiency gain” to core resilience—the ability to keep service stable with fewer people and less slack.

When staffing shrinks, companies typically respond with one or more of these moves:

• Add overtime and stretch dispatch teams
• Push drivers harder (which backfires on retention)
• Accept more spot market exposure
• Cut service promises (and lose business)

AI-driven logistics automation offers a different path: keep headcount flatter while improving execution quality.

Here’s what I’ve found works in real fleets and logistics teams: focus on decisions that are (1) high frequency, (2) time sensitive, (3) governed by patterns hidden in messy data. That’s AI’s wheelhouse.

The four “capacity leak” problems AI can actually fix

Answer first: AI increases effective capacity by reducing empty miles, dwell time, preventable breakdowns, and dispatch churn.

1. Empty miles and weak repositioning

   • AI route optimization and network balancing can reduce deadhead by recommending smarter reload sequences and repositioning moves.

2. Detention and dwell time

   • Predictive ETAs plus facility performance scoring help you plan around bottlenecks instead of discovering them at the dock.

3. Unplanned maintenance events

   • Predictive maintenance models catch failure patterns before they become roadside breakdowns that destroy a week’s plan.

4. Human bottlenecks in dispatch

   • When a dispatcher manages too many exceptions, decisions become inconsistent. AI can triage exceptions, recommend actions, and standardize playbooks.

A useful mindset shift: you don’t “add trucks” to add capacity—you remove friction to create capacity.

AI use cases that matter most when trucking employment slides

Answer first: Prioritize AI that improves planning accuracy and execution reliability: route optimization, dynamic load matching, predictive maintenance, and last-mile orchestration.

Below are the fleet intelligence use cases that consistently show up as high impact when labor is tight.

Route optimization that respects real constraints

Answer first: The best route optimization isn’t about shortest distance—it’s about service, hours-of-service feasibility, appointment windows, and facility risk.

Modern AI routing systems can incorporate:

• Hours-of-service constraints and historical driving speeds
• Appointment window probabilities (not just scheduled times)
• Weather/seasonal risk and typical delay zones
• Facility dwell-time history by day/time

That matters because when driver supply shrinks, you can’t afford plans that look good on paper and fail at 2 p.m.

Practical KPI to track:

• Planned vs. actual stop completion time (variance trendline)

Dynamic load matching and tender strategy

Answer first: If capacity is thinning, the winning move is to tender smarter, not just earlier.

AI can help by predicting:

• Which carriers are most likely to accept a given load (lane- and time-specific)
• What rate will clear the market at different lead times
• When to split freight between contract, mini-bid, and spot

This reduces the expensive pattern of “reject → scramble → overpay.”

Practical KPI to track:

• First-tender acceptance rate and spot share by lane

Predictive maintenance that protects the schedule

Answer first: Preventing one breakdown often saves more capacity than hiring one additional driver, because it avoids cascading missed appointments.

Predictive maintenance models use data like:

• Fault codes
• Telemetry (engine, brake, temperature, vibration)
• Maintenance history and parts lifecycle
• Driver inspection notes (structured via NLP)

The payoff is reliability. And reliability is what customers pay for when the market gets jittery.

Practical KPI to track:

• Road calls per 100,000 miles and on-time performance impact

Last-mile delivery optimization under courier volatility

Answer first: The courier job drop is a warning sign for last-mile: more volatility, more contractor mix, more routing complexity.

If you rely on parcel/courier networks (or you run your own), AI can stabilize performance through:

• Dynamic route sequencing and capacity pooling
• Delivery promise optimization (setting realistic ETAs)
• Exception prediction (failed delivery likelihood, address risk)
• Smart batching for returns and pickups

In December, this matters because customer tolerance for missed delivery windows is low and refund pressure is high.

Practical KPI to track:

• Stops per route hour and failed delivery rate

A practical 90-day plan for shippers, brokers, and fleets

Answer first: You don’t need a moonshot. You need a short, disciplined rollout that improves decisions in the next peak, not two years from now.

Here’s a 90-day approach I’d bet on when trucking employment is sliding:

Days 1–15: Find the hidden capacity drains

• Map your top 20 lanes by cost and service failures
• Rank facilities by dwell time and late-load frequency
• Identify where exceptions cluster (weather, time of day, customer)

Output you want: a simple list of “avoidable chaos” hotspots.

Days 16–45: Start with one AI workflow that touches daily execution

Pick one:

1. Route optimization for a defined region
2. Predictive maintenance triage for a subset of assets
3. Tender acceptance prediction on your most volatile lanes

Avoid the common mistake: deploying analytics dashboards without changing operational decisions.

Days 46–90: Operationalize with a playbook

• Create decision rules (when to re-route, when to swap trailers, when to go spot)
• Assign owners for each exception category
• Build a weekly review rhythm tied to KPIs

If your team can’t explain why an AI recommendation was followed or rejected, you don’t have an AI system—you have a suggestion box.

People also ask: what does a drop in trucking employment mean for 2026?

Answer first: It means more rate volatility and a higher chance of localized capacity shortages, even if macro demand stays mixed.

If labor continues to shrink while demand has seasonal spikes, the market behaves like this:

• Contract rates lag reality
• Spot rates jump faster and fall faster
• Service failures increase on “hard” lanes (rural, imbalanced, time-sensitive)

One of the more counterintuitive implications: wages can rise even as employment falls. The BLS data shows average hourly wages for production and nonsupervisory trucking employees reaching $31.40 (latest available through October).

That’s consistent with a market where the remaining drivers have more leverage—and where efficiency is the only sustainable cost-control strategy.

Where fleet intelligence fits in the bigger AI in trucking story

Answer first: Fleet intelligence is the bridge between strategy and execution—turning messy operations data into better routing, maintenance, and capacity decisions every day.

If you’re following this series, you’ve seen the theme: AI wins in trucking when it’s attached to a specific operational constraint. This month’s constraint is clear: fewer people in the system, more volatility on the margins.

The companies that outperform in 2026 won’t be the ones yelling “driver shortage” the loudest. They’ll be the ones who can say, with data, “We improved on-time delivery while running the same headcount.”

If trucking employment is falling, what part of your network still depends on heroics and tribal knowledge—and what would change if your planning system could predict the next disruption before it happens?