Transportation Mode Shifting Analysis

A major North American LTL carrier provides a well-documented case study in AI-driven freight optimization. The carrier, which handles 2.6 million miles of linehaul freight per night, deployed proprietary AI-driven linehaul models to optimize freight flows across its network. According to a 2025 Trucking Dive report, the carrier's AI models analyze volume, capacity, and dimensions to determine the most effective ways to consolidate and route freight. The carrier's CEO reported in a 2025 CNBC interview that the AI system reduced empty miles by 12% and delivered a low-single-digit improvement in productivity that, at the carrier's scale, translated to tens of millions of dollars in annual savings. The carrier also reduced outsourced linehaul miles to a record 5.9% of total miles, down 770 basis points year over year, further improving profitability. The carrier is now beta testing AI to optimize trailer and route assignments at the shipment level, factoring in appointment windows to enhance on-time performance.

In a separate example, a global package delivery company deployed its ORION route optimization system, which uses AI to calculate optimal delivery paths. According to a 2025 DocShipper analysis, the system processes 30,000 route optimizations per minute and saves 38 million liters of fuel annually while preventing approximately 100,000 metric tons of carbon dioxide emissions each year. Additionally, a digital freight matching platform reported reducing empty miles from 25% to 22% in one year, saving four million empty miles, with estimates of potential industry-wide reductions of up to 64% according to the platform's own analysis. These examples illustrate that AI-driven mode and route optimization delivers quantifiable returns across carriers of varying size and operating models, though results depend heavily on data quality, network complexity, and the maturity of existing TMS infrastructure.