Artificial intelligence is no longer confined to pilot programs in transportation. New data from Trimble shows AI taking hold in planning, pricing, and execution, as companies look to translate years of experimentation into operational gains. The survey suggests the competitive divide is shifting toward how consistently AI is embedded into everyday transportation decisions.
From Pilots to Measurable Gains
According to the survey, 44% of respondents are already using AI for transportation planning and optimization, with additional applications emerging in freight procurement and real-time visibility. Pricing and lane optimization rank close behind, with 42% deploying AI in those areas, while 39% report using AI for real-time tracking.
Looking ahead, respondents overwhelmingly expect AI to reshape transportation planning, pricing, and execution over the next three to five years. Eighty-six percent anticipate a significant impact in these areas, though priorities diverge between shippers and carriers. While both groups see planning and execution as the main battleground, only 59% identify it as AI’s single most important value driver, reflecting more selective expectations around where returns will materialize.
Notably, respondents describe a clear pivot away from early-stage experimentation. The emphasis is now on translating AI investments into measurable efficiency gains, improving service reliability, reducing manual intervention, and tightening cost control, rather than proving technical feasibility.
Agentic AI Sharpens Execution Focus
The report highlights growing interest in agentic AI, autonomous software agents capable of monitoring data, making decisions, and executing tasks within defined parameters. These capabilities are increasingly viewed as a way to compress decision cycles in high-velocity transportation environments.
Shippers point to real-time ETA monitoring as the most immediate opportunity, cited by 52% of respondents, followed by route and network optimization and carrier selection and tendering. Carriers, by contrast, prioritize ETA calculation and alerting, identified by 59%, along with route and fuel optimization and spot quote negotiation.
Underlying these use cases is a shared recognition that AI delivers its strongest results within connected ecosystems. The survey finds that 43% of shippers see enhanced predictive capabilities, such as improved ETA accuracy and disruption risk management, as the top benefit of combining AI with network-based transportation management systems. Among carriers, 55% cite smarter load matching as the primary gain when AI is deployed across connected networks rather than isolated systems.
Where AI Pressure Will Quietly Accumulate
As AI becomes embedded in transportation execution, the strain is likely to surface not in algorithms but in operating models. Recent industry research shows that AI-driven decisions increasingly cut across procurement, network planning, and carrier management, yet accountability for those decisions often remains fragmented. Over time, organizations will be forced to clarify who owns AI-driven outcomes, pricing accuracy, service tradeoffs, or exception handling, when those outcomes are generated automatically. That governance shift, rather than any single technical breakthrough, may prove to be the most consequential adjustment as AI moves from support tool to operational actor inside transportation networks.
