Freight margins have tightened across the industry, and carriers feel it in every line item. Ulugbek Djuraev, chief executive of B ONE Trucking, argues that the next advantage will come from better information rather than more equipment. His case for data-driven trucking rests on a simple pairing. Experienced teams working alongside intelligent technology reach sharper decisions than either reaches alone.
Why Cost Pressure Is Reshaping Fleet Priorities
The American Transportation Research Institute reported that the industry-average cost to operate a truck reached $2.336 per mile in 2025, a record in its annual benchmarking series. Tolls, repair and maintenance, and driver benefits all climbed that year. For a carrier running dozens of trucks, a few cents per mile decides whether a lane earns money or loses it.
Smaller and mid-sized fleets feel that squeeze first. They carry comparable insurance and equipment costs without the volume to absorb them. Djuraev treats the math as the practical argument for data-driven trucking. Buying more trucks raises revenue and cost together. Measuring how the current fleet performs moves only one side of the equation.
What Data-Driven Trucking Looks Like Day to Day
B ONE Trucking is implementing an application built around two operational questions. Where does the fuel go, and where do the miles go. Both answers already sit inside a carrier’s daily records, spread across fuel receipts, telematics feeds, dispatch notes, and toll statements. The work involves pulling those records together and putting them in front of the people who assign loads.
Djuraev describes the target in plain terms. A dispatcher should open one screen and see what changed since yesterday. A driver should understand how last week’s habits affected the numbers.
Fuel Data as a Daily Discipline
Fuel sits among the costs a carrier can influence week to week. The U.S. Department of Energy reports that idling a heavy-duty truck consumes about 0.8 gallons of fuel per hour, and that a long-haul truck typically idles roughly 1,800 hours a year, burning close to 1,500 gallons of diesel. Argonne National Laboratory estimates that rest-period idling across the country consumes up to a billion gallons annually.
The planned application tracks consumption by truck, idle time by driver, and fueling stops by route. Djuraev looks for patterns rather than single events. One long idle on a freezing night explains itself. The same pattern across twenty trucks every week points to something a manager can act on. Data-driven trucking treats fuel as a habit to coach rather than a bill to accept.
Route Optimization and the Value of Every Mile
Every mile carries cost in fuel, hours, wear, and tolls. At many carriers, route planning still depends on the memory of two or three veteran dispatchers. That knowledge is genuinely valuable, and it is also hard to scale.
Route optimization gives that experience a wider base. Historical transit times, seasonal delay patterns, toll structures, and customer dwell times can all be measured. Djuraev expects the steadiest gains from small corrections applied consistently. Ten miles trimmed from a lane that runs four times a week adds up faster than one dramatic reroute. Here is where data-driven trucking connects directly to operating cost.
Experience Still Drives the Decision
Djuraev is direct about what software cannot do. “AI will not replace the experience behind trucking; it will give experienced people better tools to make better decisions.”
A dispatcher who has worked a corridor for fifteen years knows which receiver runs late in December. No system knows that until someone records it. Djuraev wants technology to capture that judgment and share it across the team, so a new hire starts with context that once took years to build. His version of data-driven trucking keeps the person at the center and puts the tool in service of the decision.
Questions Transportation CEOs Should Ask
Djuraev suggests leaders start with the decision, not the platform. Which recurring choice does this tool improve. Who makes that choice today. How will the team know the choice got better. Tools that answer all three earn their place in the operation. He also warns against buying a system for the company a carrier hopes to become. The tool has to fit the loads on the board this month.
Adoption comes down to drivers. Systems that feel like surveillance meet quiet resistance. Systems that help a driver protect pay and home time get used without much prompting. Driver adoption decides whether data-driven trucking works in practice, and no feature list changes that.
Building the Next Generation of Carriers
Djuraev expects the coming generation of carriers to be judged on how intelligently they run their trucks rather than on how many they own. Data, automation, and AI can support efficiency and reliability across a fleet. The advantage shows up when that technology turns into better decisions for drivers, customers, and the business. A data-driven trucking program, in his view, is an operating habit rather than a software purchase.
B ONE Trucking continues to build out its platform while running freight every day. For transportation leaders weighing similar investments, Djuraev’s guidance stays consistent. Start with the decisions the team already makes, measure them honestly, and let data-driven trucking improve the judgment experienced people already bring to the road.
