Freight bids have always been a gamble. Carriers sharpen their pencils, shippers push for lower rates, and somewhere in the middle a contract gets signed. But too often, the ink dries on an agreement that looks fine on paper and bleeds money on the road. The U.S. trucking industry in 2024 showed just how fragile the system is—average operating margins fell below 2% in most sectors, with truckload carriers actually posting negative margins of -2.3%. When a single miscalculated lane can tip the balance from slim profit to steep loss, bidding blind isn’t an option anymore.
Generative AI (GenAI) brings a new possibility: forecasting lane profitability before the commitment is made. By pulling together structured and unstructured data, GenAI can test scenarios, predict margins, and highlight hidden risks—or opportunities—that humans miss. This isn’t a theory. Early adopters are already cutting deadhead miles, boosting bid-win ratios, and restoring trust between shippers and carriers. The point is simple: in freight procurement, foresight is fast becoming the only safeguard against failure.
Bids in trucking have long relied on a mix of historical averages, market chatter, and gut feel. The result? A patchwork of rates that may look competitive at the time of award but often fail in execution.
The numbers tell the story. In 2023, the average cost of trucking per mile hit $2.27, up nearly a full cent from the previous year. Driver wages rose 7.6%, equipment payments 8.8%, and insurance premiums 12.5%. Add to that 16.3% deadhead mileage for non-tank operations, and it’s no surprise margins are underwater.
Carriers win lanes they shouldn’t, and shippers sign contracts that collapse under real costs. Long-haul lanes, in particular, are often bid at breakeven—or worse—because competition drives prices down even as costs go up. The outcome is predictable: too many trucks chasing too little freight, a negative truckload margin, and contracts abandoned midstream.
Profitability isn’t just the difference between the rate and the cost per mile. It’s a shifting equation with dozens of moving parts.
Deadhead miles remain a chronic drain on profits. With 16% of miles on average driven empty—and some fleets reporting as high as 35% on return trips—carriers are literally paying drivers to move no revenue. These trips inflate fuel use, wear down equipment, and reduce the time available for paid freight.
Fuel volatility makes things even harder to predict. Since fuel costs account for 21–30% of operating expenses, even a small fluctuation can turn what looked like a profitable lane into a loss. Smaller carriers, who lack the scale to negotiate strong fuel surcharges, are especially exposed to these swings.
Demand isn’t static either. A lane that’s profitable in one quarter might collapse in the next. Seasonal slowdowns, shifting regional demand, or a sudden change in tender rejections can quickly reshape profitability. Spreadsheets simply don’t capture these kinds of fluctuations in real time.
Driver turnover further complicates the picture. With truckload turnover climbing five percentage points in 2023, every disruption to labor availability pushes up costs. A bid that assumed steady staffing might suddenly face unplanned downtime or higher wage premiums.
Finally, hidden costs like tolls, detention, and accessorial charges often slip past initial models. These “small” line items can erode profit margins lane by lane, especially for longer hauls where compliance and scheduling complexity multiply.
Here’s the thing: GenAI thrives on messy, complex data. It takes in telematics, TMS records, ELD logs, weather reports, fuel indexes, even driver notes, and turns them into usable forecasts.
GenAI enables scenario testing in a way traditional tools can’t. Instead of one static forecast, it simulates multiple versions of the future—what happens if fuel prices spike, if demand dips 10%, or if a backhaul becomes available? This allows carriers to see potential risks and rewards before committing.
It also introduces dynamic lane bundling. Instead of evaluating lanes in isolation, GenAI models the entire network. It spots opportunities where a return trip can carry a backhaul, or where connecting two “okay” lanes creates a profitable cycle. This network-first approach is where much of the hidden value sits.
Forecast accuracy improves dramatically. Studies show GenAI can improve demand and delivery timing forecasts by up to 30%, which translates directly into better capacity planning. That means fewer wasted miles, more reliable commitments, and tighter margin control.
Perhaps most importantly, GenAI doesn’t stop learning. Every new bid, every contract, every lane outcome feeds back into the system. Over time, this creates a continuously improving engine that refines its profitability forecasts with every cycle.
For carriers, forecasting lane profitability with GenAI isn’t just about surviving volatile markets—it’s about building a more resilient business model.
Higher bid-win ratios are a tangible outcome. AI-powered bid optimization has lifted win rates from 29% to 36% in some cases. That jump doesn’t just mean more contracts won; it means contracts won with a clearer understanding of their profitability, protecting carriers from taking on bad business.
Margins improve too. McKinsey estimates that AI-driven pricing can deliver around 10% better margins. In an industry where many truckload carriers struggle to even break even, that’s a significant shift. It translates into healthier bottom lines and more room for reinvestment.
Empty miles decline as predictive routing gets smarter. Fleets using AI-powered load matching and routing have reported 15–25% reductions in deadhead. That saves fuel, cuts emissions, and frees up drivers for paying work—all of which flow straight into profitability.
Relationships with shippers also improve. With GenAI making every cost component transparent—fuel, detention, accessorials—there’s less room for disputes and surprises. That transparency builds trust, which is increasingly a differentiator in a highly competitive market.
But let’s be clear: adoption isn’t seamless.
Data fragmentation is a big one. Many fleets still run on siloed spreadsheets and disconnected platforms, meaning their “truth” about costs and revenues is spread across multiple systems. Without integration, GenAI models are forced to make do with partial visibility.
There’s also the issue of AI literacy. Pricing teams accustomed to gut-based bidding can be skeptical of algorithmic recommendations they don’t fully understand. For GenAI to gain traction, explainability matters as much as accuracy.
Volatility remains a challenge too. AI is only as good as the data it’s trained on, and the past few years have shown just how unpredictable freight markets can be. Models need to be constantly retrained and recalibrated to stay relevant.
And then there’s the human factor. GenAI isn’t replacing the judgment of seasoned pricing managers or dispatchers. The best results come when human expertise and AI-driven foresight work together, each compensating for the other’s blind spots.
At Amazatic, we see GenAI not as a shiny tool, but as a practical shift in how trucking businesses should think about profitability. Our perspective is rooted in one belief: decisions about freight bids shouldn’t be guesswork—they should be informed predictions backed by real data.
We build GenAI systems that live inside the workflows carriers already use, not as stand-alone dashboards that collect dust. That means embedding lane profitability forecasting directly into TMS, ERP, or procurement platforms so that pricing teams see the insight right where decisions are made.
Our focus is not only on predicting lane-level profitability, but on reshaping how businesses think about bidding. Instead of chasing “wins” at the expense of margins, we help carriers and logistics companies target the right lanes—the ones that align with long-term growth strategies.
Transparency is another core principle. We don’t just deliver a “yes/no” or “profitable/unprofitable” output. Our models show the why behind the forecast—fuel volatility, driver churn, compliance risk—so teams trust the recommendation and can explain it to their customers.
In short, we help our partners move from a reactive mindset to a profitability-first approach. It’s not about technology for its own sake; it’s about building systems that make trucking businesses more resilient in a market where volatility has become the norm.
The freight industry has always been cyclical, but the way bids are managed doesn’t need to follow the same old cycles. The shift is toward predictive bidding, where lanes are priced with foresight weeks in advance and with clear confidence intervals.
We also see collaborative forecasting becoming the norm. Shippers and carriers won’t just negotiate rates; they’ll negotiate based on shared models of profitability, creating contracts that are sustainable for both sides.
Most importantly, we see a culture shift—from chasing “any lane” to focusing on “the right lane.” That subtle but critical change is what separates companies that survive rate cycles from those that thrive through them.
Freight bids have always carried risk. But in an industry where margins are razor-thin and volatility is constant, guessing isn’t a viable strategy anymore. GenAI offers carriers and shippers the ability to know profitability before committing. That ability alone can mean the difference between a year of losses and a year of growth.
The trucking industry doesn’t need more tools. It needs systems that turn data into foresight and foresight into better contracts. That’s the change Amazatic is helping to make real.
If you’d like to explore how GenAI can reshape your freight bidding process, connect with Amazatic.