Let’s stop pretending AI is just a shiny toy in trucking. The real story? It’s becoming the only way many fleets are staying alive.
We’re not talking about predictive ETAs or digital dashboards. We’re talking about fixing the age-old problems that have quietly drained millions from carriers for decades: empty miles and fuel inefficiency.
The Silent Killer in Trucking Operations: 35% of Miles Still Run Empty
Every fleet manager has wrestled with this: the backhaul problem. You run a load from Dallas to Chicago and then… nothing. Your truck runs 900 miles back partially loaded or, worse, completely empty. Multiply that across a 200-truck fleet and the numbers aren’t just frustrating, they’re unsustainable.
According to the American Transportation Research Institute, over 35% of truck miles are still run empty in the U.S. That’s not just lost revenue. It’s wasted fuel, unnecessary wear and tear, and increased emissions. In 2025, when fuel prices are volatile and sustainability pressures are mounting, running inefficient lanes isn’t just bad business, it’s a liability.
How Uber Freight Quietly Changed the Game
Here’s where the trend gets interesting.
Uber Freight, now handling over $20 billion in annual freight, has turned this inefficiency into an AI problem and is solving it like a tech company, not a traditional broker. They’ve deployed agentic AI models that not only optimize route planning but actually learn from historical data, weather, driver behavior, rate fluctuations, and real-time market conditions.
The result? Uber Freight reports a 10–15% reduction in empty miles across select lanes. That’s not a gimmick. That’s a margin-improving, planet-friendly change to the bottom line.
And it’s not just Uber. Convoy was pushing similar innovations before their collapse, and now, newer players like Loadsmart, Flock Freight, and Transfix are carrying the baton forward, embedding deep AI systems into load-matching and pricing logic.
AI Is Not a Magic Button, But It’s a Tool That Works If You Use It Right
Let’s get real: AI doesn’t mean plug-and-play.
It requires operational alignment. It demands data quality. It requires dispatch teams to trust the system, and that’s a cultural shift many small to mid-sized fleets aren’t prepared for. One regional carrier in Illinois I worked with tried to deploy an AI-powered load board plugin. The tech worked. But their dispatchers kept overriding suggestions out of habit. Adoption was dead in the water until leadership ran a two-week training sprint that paired dispatchers with data analysts to explain why the recommendations mattered.
Once adoption kicked in, their empty mile rate dropped 9% in 60 days, and fuel spend decreased by nearly $0.06 per mile.
This is the part nobody talks about in fancy AI press releases: the hardest part is not the tool, it’s the people.
What to Do Now: Practical Frameworks for Fleet Optimization Using AI
So what can carriers do today?
Here’s a realistic approach based on what’s working in the field:
1. Vendor Selection Criteria for AI Matching Platforms
Not all AI solutions are created equal. When evaluating tools, consider:
- Integration ease with your existing TMS
- Transparency in logic (black-box AI doesn’t win dispatcher trust)
- Historical training data sources (regional bias matters)
- Driver app interface (if it frustrates drivers, it’s doomed)
Pro tip: Avoid vendors that only emphasize pricing logic. If they can’t also optimize route flow and deadhead reduction, you’re only solving half the problem.
2. ROI Assessment Framework
Before you sign a $50,000 contract, ask:
- What is your current empty mile percentage?
- What’s your average cost per mile?
- What is the estimated fuel savings per load?
- How many loads would need to optimize to break even?
You’d be surprised how many fleets skip these basics.
3. Cultural Alignment Strategy
You must prep your dispatch and planning teams for the shift:
- Run A/B tests between manual and AI suggestions.
- Make weekly “AI wins” visible across dispatch teams.
- Set a 90-day benchmark for adoption before full rollout.
It’s not just about tech. It’s about belief in the process.
Looking Ahead: AI’s Role in Dispatch Strategy and Driver Retention
Here’s something we’re seeing more of in 2025: AI tools that also account for driver preferences and wellness. For example, systems that adjust routing based on sleep cycles, home-time preferences, or personal load history. That’s not just smart, it’s retention gold.
In a market still struggling with a persistent driver shortage, tech that respects the human behind the wheel gives you a serious edge.
Final Thoughts: The Industry Is at a Fork in the Road
AI in logistics isn’t hype anymore. It’s here. The only question is whether you’ll let it sit on your digital shelf or use it to rebuild your dispatch strategy from the ground up.
Empty miles are no longer a cost of doing business. They’re a sign of an outdated system. If Uber Freight can eliminate 15% of them at scale, regional carriers and mid-sized fleets can too. The key is to start with intent, align your people, and build smart systems that don’t just automate, but actually optimize.
Because in this industry, margin isn’t made in the miles you run. It’s made in the ones you don’t have to.
Truck & Trade Trends 
Leave a comment