AI route optimisation leads US fleet use, survey finds

AI route optimisation is the most widely used AI tool among US fleet businesses, according to a Tech.co survey, with 58% of fleet professionals using it.

The findings are based on a survey of 265 professionals in the US transport and shipping sector. AI predictive maintenance ranked second, used by 23% of respondents, followed by AI dash cams at 13%.

The results point to broad AI adoption across fleet operations. The survey found that 69% of fleet professionals see AI as a competitive advantage, while 95% said AI tools save them between one and 20 hours a week.

Route optimisation stood out not only for its adoption rate but also for the time savings linked to it. Fleet managers using automated route optimisation save an average of seven hours of work a week, Tech.co found.

The software identifies more efficient driving routes by factoring in distance, traffic conditions, delivery schedules and weather patterns. It can also use real-time and historical data on road conditions and delivery times, giving fleet managers more information to assign jobs and helping drivers avoid congestion and accidents.

Fuel spending is another area where the tool appears to have an effect. AI route optimisation cuts fuel budgets by 19.3%, according to Tech.co, as shorter and less congested routes reduce fuel use on each journey.

Adoption trends

The survey suggests that while several AI applications are gaining ground in fleet management, route planning remains the clearest day-to-day use case. Predictive maintenance, which ranked second, is typically used to spot vehicle issues before breakdowns, while AI dash cams monitor driving behaviour and road incidents.

Route optimisation appears to have moved ahead because it addresses several pressures at once, including driver scheduling, delivery timings and fuel costs. That makes it one of the most direct ways for fleet operators to measure a return on AI spending.

AI use in logistics has expanded as operators face tighter margins and continued pressure to improve delivery performance. In that environment, tools that reduce manual planning work and cut operating costs have drawn particular interest from fleet managers.

Operational impact

Many modern fleet management systems now include route optimisation as part of a wider software package, Tech.co said. Implementation typically involves importing driver and vehicle data, setting route start and end points, uploading stop locations, and applying constraints such as vehicle capacity and maximum route duration.

Operators then allocate routes on a trial basis and monitor results before making adjustments. This reflects how route planning software is being folded into broader operational systems rather than treated as a standalone tool.

The wider survey findings indicate that logistics professionals are no longer viewing AI as an experimental technology. Instead, many now see it as part of routine operational management, especially where time savings can be measured in weekly workloads.

Jack Turner, editor at Tech.co, said: "While predictive maintenance might save the most hours per task, route optimization is a clear priority, with our research showing 58% of US fleets leveraging it to turn logistical headaches into a competitive edge."

"By reclaiming seven hours a week and slashing fuel costs by nearly 20%, AI tech has moved from a nice to have to an operational necessity for 69% of industry professionals."