The first great rush of corporate AI spending went into industries already comfortable with software: coding, marketing, customer service and other knowledge-heavy work. Logistics is a different proposition altogether. According to a recent Boston Consulting Group report, only 13% of companies in the sector are seeing measurable financial gains from AI, even as expectations rise and more shippers now weigh AI capability when choosing providers. That gap helps explain why the next phas...
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e of AI investment may prove more consequential than the first.
The appeal lies in the economics. In low-margin businesses, even modest savings can have an outsized effect on profit. A small reduction in costs can transform a freight operator’s bottom line far more dramatically than it would in a high-margin software company. In a sector where margins are often just a few percentage points, efficiency is not a nice-to-have; it is the difference between resilience and strain.
Logistics is also enormous. The industry still relies heavily on spreadsheets, email, messaging apps and phone calls, with crucial knowledge often locked inside experienced dispatchers, planners and operations teams. That dependence on manual coordination creates cost, slows decision-making and leaves firms vulnerable when staff move on. It also leaves a large swathe of activity outside the reach of traditional software.
AI is starting to change that. It can process messages, calls, location data, invoices and delivery paperwork, then convert them into structured decisions and actions. That moves logistics from being a system that merely records what happened to one that can help run the operation in real time. The larger prize, however, is not the model itself. As frontier models become more widely available, the real advantage is shifting towards context: the specific operational data, exceptions and feedback loops that allow a system to understand how a business actually works.
That is why a static agent can be copied, but one trained over months on millions of real-world events, corrections and outcomes is much harder to replicate. The company that captures that operational knowledge before it is lost to churn may end up with a more durable moat than any single model.
The shift also changes how software is sold. In logistics, where AI is increasingly expected to do work rather than simply store information, pricing is moving away from seat licences and towards outcomes. Boston Consulting Group’s findings suggest the industry is still early in this transition: despite growing interest, fewer than 10% of logistics service providers have scaled AI across core operations. Redwood Logistics, in its 2026 AI in Logistics Report, similarly found that only 13% of shippers deploying AI are generating quantifiable results.
Those figures point to the same problem: many companies have added isolated tools without redesigning workflows. Fragmented systems, siloed operations and outdated infrastructure are limiting returns. By contrast, firms that integrate AI across functions and rework processes around it are more likely to see meaningful gains.
For investors, that makes logistics unusual. It combines a vast, manual, thin-margin market with a technology shift that can deliver disproportionate profit uplift if deployed well. The opportunity is not in putting a chatbot on top of old software. It is in building platforms that understand the physical operation deeply enough to connect data, decisions and execution, and then prove the savings at scale.
In an industry long defined by movement, the next competitive edge may come from systems that do more than track the flow of goods. They may increasingly help direct it.
Source: Noah Wire Services
