Trucking Fleets Embrace Gen AI, but Data Problems Slow Growth

Why It Matters

Robust AI can cut total‑cost‑of‑ownership and improve safety, but data gaps prevent fleets from unlocking those financial and operational benefits.

Key Takeaways

• 87.1% of fleets use GenAI for back‑office tasks, highest adoption rate
• Data integration issues rose to 71%, blocking deeper AI returns
• Only 9.7% integrate telematics/ELD data into real‑time AI models
• 64.5% have not evaluated AI for lease‑end processes
• AI safety tools used by 61.3%, yet 6.5% lack monitoring

Pulse Analysis

The trucking industry is witnessing a wave of generative AI adoption that far outpaces other advanced analytics. Large language models are now embedded in daily back‑office workflows, from parsing maintenance manuals to automating compliance reporting, while AI‑driven driver coaching platforms are gaining traction. This surge reflects broader pressure on fleets to reduce costs, meet tighter regulatory standards, and improve driver retention, positioning AI as a strategic lever rather than a novelty.

Despite the enthusiasm, the survey highlights a stark mismatch between AI ambition and data readiness. Integration hurdles have jumped from 38% to 71%, and more than half of respondents cite inaccurate data as a critical barrier. Telemetry and electronic logging device (ELD) streams—rich sources for predictive insights—remain largely siloed, with under 10% of fleets feeding them into AI models. Similarly, lease‑end processes such as damage scoring and remarketing are still untouched by AI, representing a missed opportunity for revenue recovery. The talent shortage compounds these issues, as many firms lack the expertise to cleanse, harmonize, and operationalize data at scale.

For fleets that can bridge these gaps, AI promises measurable gains in total‑cost‑of‑ownership, safety outcomes, and asset utilization. Investing in data pipelines, real‑time telematics integration, and structured measurement frameworks will enable predictive maintenance, dynamic routing, and more accurate TCO modeling. As the competitive landscape tightens, operators that treat AI as a data‑first initiative—not just a back‑office convenience—will capture the next wave of efficiency and profitability.