AI-powered software platforms are reshaping how freight brokers and forwarders operate, putting pressure on asset-light logistics incumbents whose competitive moats have long rested on proprietary software platforms, according to a McKinsey & Company analysis authored by Tom Bartman and Ryan Gavin with contributions from Cheyenne Allenby, Guillermo Dominguez, and Nicolas Lopez . The central question the report poses is whether AI will allow new entrants to quickly and cheaply replicate powerful logistics software interfaces—or whether AI agents could stitch together necessary functionality using open data, bypassing the need for specialized logistics platforms altogether .
The report identifies two distinct risks facing incumbents. The first is that AI might enable new entrants to "vibe code" robust logistics software interfaces, drastically reducing the time and money previously required to develop or obtain such tools. The second, more radical scenario is that AI agents could assemble functionality on their own from open data sources, eliminating the customer's need for any dedicated logistics platform .
Despite these threats, McKinsey argues that incumbent logistics providers possess advantages that software cannot easily replicate. Scale allows asset-light brokers and forwarders to secure volume discounts from carriers and offers wider networks for moving cargo. Personal connections—relationships built over years and decades—engender trust between shippers and providers, and between providers and carriers, giving customers confidence that promises will be kept and that someone accountable is available when disruptions occur .
Experience is another durable advantage. Incumbents have "seen it all" at the enterprise level and deeply understand the needs of both shippers and carriers. Most critically, incumbents hold large, diverse sets of proprietary data gathered from millions of shipments and customer interactions spanning years—including records of which rates customers declined and in what contexts. These data flows can train AI models on real-world freight behavior in ways newer entrants cannot match .
The report notes that asset-heavy logistics providers tend to be more insulated from AI disruption given the capital required to replicate physical networks, and can benefit from AI in maintenance, asset utilization, and network optimization . For asset-light incumbents, the path forward lies in combining scale, relationships, experience, and data with a forward-looking approach to technology—embracing innovation faster and implementing it better than competitors .
Leading logistics incumbents are already demonstrating measurable results. One transportation company implemented an AI-enabled supply chain platform that boosted productivity by more than 40 percent since 2022, applying automation to pricing, capacity sourcing, freight tracking, and document handling. Another deployed 50 AI agents that automated 60 percent of check calls (status updates on cargo), 73 percent of order acceptances, 80 percent of paper invoice payments, and generated two million quotes—saving tens of thousands of hours of labor .
The McKinsey analysis suggests that understanding and implementing AI is now imperative for transportation and logistics companies. The report implies a strategic sequence: focus first on domains large enough to generate meaningful value without disrupting the entire business, such as commercial functions like lead generation or automated quoting, before scaling to broader operational transformation. Long-term value creation depends on internal talent, flexible operating models, and clean data architecture . The underlying message is that AI models themselves are becoming commoditized; the true competitive moat lies in how firms fuse the technology with assets that cannot be easily replicated—trusted relationships, deep operational experience, and proprietary real-world data at scale .