Breaking

Spatial Reasoning Gap Halts AI Adoption in Truck Routing

General-purpose AI models achieve only 55% accuracy on basic directional questions, creating a critical barrier to deploying agentic AI in logistics, according to HERE Technologies executives. The company, which has supported transport and fleet operations for decades, contends that location intelligence must anchor AI workflows from inception rather than as an afterthought. Current large language models excel at language comprehension but lack the spatial reasoning required for operational freight tasks. Rerouting trucks around low-clearance bridges, locating parking, and forecasting congestion demand granular, location-specific data absent from standard AI systems. HERE maintains over 800,000 truck-centric data points covering tolls, road restrictions, and approved routes. Without embedding geospatial models directly into AI systems, responses become unreliable. The company identifies a persistent gap between back-office route planning and real-world driver conditions. Properly integrated location data could bridge planning and field execution, capturing driver feedback to improve future routing decisions.