C.H. Robinson's insightful AI strategy: How 450 engineers built a logistics game-changer

The Numbers That Tell the Story

C.H. Robinson isn’t just talking about AI transformation—the numbers speak volumes. The company’s stock surged 55.3% in 2025, outpacing every other logistics competitor in the sector. While industry observers point to multiple factors, one stands out: the operational deployment of 30 agentic AI tools actively reshaping how the company processes logistics at scale. With an expected $11 billion in revenue for 2025, deploying this many specialized AI systems signals serious structural change, not superficial optimization.

CFO Damon Lee recently offered insights into how this technology is translating into measurable business outcomes—moving well beyond the vague promises of “efficiency gains” that dominate most industry conversations.

Why Most Companies’ AI Bets Aren’t Working

The Transportation Intermediaries Association’s April meeting revealed a familiar pattern: logistics companies everywhere are racing to adopt AI. The conversation typically orbits around automating invoice processing or converting call recordings into actionable broker intelligence. Sounds reasonable, right?

Yet here’s the uncomfortable truth: most organizations deploying these generic, off-the-shelf AI solutions are actually increasing costs without delivering proportional productivity improvements. Usage-based AI pricing models have become a budget trap for companies betting on third-party tools. Lee views this skeptically—it explains why so many AI initiatives across the industry have failed to generate meaningful returns, despite massive spending.

The Competitive Edge: Building Custom AI From Scratch

C.H. Robinson took a different path. Rather than renting capabilities from AI vendors, the company assembled an internal team of 450 engineers dedicated to building proprietary AI applications. This investment has yielded 30 purpose-built tools, each engineered to solve specific operational bottlenecks.

The North American Surface Transport (NAST) division offers the clearest example of this approach’s power. NAST handles over-the-road brokerage—the company’s core business—and fields approximately 600,000 rate quote requests annually.

The old system had a brutal limitation: the company could only respond to 60-65% of these inquiries. Response times stretched to 17-20 minutes, and many customers simply moved to competitors. With the introduction of a custom agentic AI tool, C.H. Robinson now responds to every single request—100% coverage. The response time collapsed to 32 seconds.

But speed isn’t the real story. The AI system analyzes tens of thousands—potentially hundreds of thousands—of data points to generate each quote. A human broker might reference five to ten variables. The machine considers market conditions, historical patterns, competitive positioning, and real-time supply/demand signals simultaneously. The resulting pricing is dramatically more sophisticated and competitive.

Margin Optimization at Unprecedented Speed

Historically, freight brokers operated on a predictable rhythm: set pricing strategy at the beginning of a period, execute it, then review results monthly or quarterly. Strategic adjustments were rare, clunky, and slow.

The AI-powered pricing tool inverts this model entirely. Pricing strategies deployed Monday morning can be tested, evaluated, and refined by afternoon. The system enables hundreds of micro-adjustments daily—what Lee describes as “gross margin arbitrage.” When incoming volume spikes, the system prioritizes margin expansion. When loads dry up, it shifts to aggressive pricing to capture volume. All brokers aim for this balance, but C.H. Robinson’s AI executes this optimization in real time, at a speed and precision the industry has never seen.

This agility manifests in financial results. LTL operations posted a 6.7% year-to-date increase in adjusted gross profit. While truckload brokerage faced headwinds in a challenging 2025 freight market, the decline was modest at 2% quarter-over-quarter—defensive performance during a down cycle.

The Skeptic’s Question: Is This Real?

Some investors remain unconvinced. As of mid-December, 6.47% of the company’s stock float was sold short—a notable bet against the company. The skepticism hinges on a single question: Is the stock rise driven by traditional brokerage performance, or by AI hype?

Lee’s counterpoint is insightful and worth considering. While the AI ecosystem generates headlines—chip manufacturers, data centers, model providers—these are largely upstream players. Operational companies successfully implementing AI at the application layer remain rare. C.H. Robinson occupies that scarce territory: a real business, handling physical goods movement, deploying AI to solve genuine operational constraints, and converting those improvements into measurable financial performance.

The 450-engineer team, the 30 operational tools, the 32-second quote response, the margin optimization—these aren’t marketing narratives. They’re proof points of an operational transformation that few logistics companies have attempted, let alone executed.

Whether the market’s faith proves justified will unfold in 2026.