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Trucking fleets adopt generative AI widely but data gaps limit operational gains, survey finds

87% of trucking fleets now use AI for back-office tasks, but data integration failures block real gains. Only 9.7% feed telematics data into AI for live operational insights.

1 min read
Management

Trucking Fleets Adopt AI Fast, But Data Problems Block Real Gains

Nearly nine in 10 trucking fleet operators now use generative AI and LLM tools for back-office work, but foundational data infrastructure failures are preventing the industry from translating that adoption into operational results, according to a Fleet Advantage survey released this year.

The survey found 87.1% of respondents deployed large language models for administrative tasks, driver feedback systems, and extracting insights from internal documents like maintenance manuals and compliance guides. That far exceeds adoption of predictive analytics (38.7%), machine learning (35.5%), and computer vision tools (0%).

Driver safety monitoring shows stronger traction, with 61.3% of organizations using AI to track and coach driver behavior. Still, 6.5% reported no formal driver safety program at all.

Data Problems Worsened Year Over Year

Implementation barriers intensified across the board. Data integration issues jumped from 38.1% to 71.0% of respondents. Concerns about inaccurate data rose from 23.8% to 64.5%. Lack of internal expertise climbed from 19.0% to 45.2%.

Two gaps stand out as major missed opportunities. A majority of fleets (51.6%) collect telematics and electronic logging device data but never integrated it with AI systems. Only 9.7% feed that data into AI models for real-time operational insights.

The second blind spot: 64.5% of fleets do not use or evaluate AI for lease-end processes-damage scoring, vehicle remarketing, and excess mileage assessment. This represents the largest single area of non-adoption in the survey.

Where the Real Value Lies

Fleets that win in the next phase will invest now in data analysis quality and structured measurement frameworks, not just deploy tools and hope for results. The biggest opportunity sits in applying AI to core financial and operational work: total cost of ownership modeling and safety management, where adoption still lags significantly.

Without this foundation, AI remains a convenience for paperwork rather than a source of competitive advantage.

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