This article originally appeared on TechCrunch.com.
Silicon Valley has spent the past several years building increasingly powerful AI models and agents. But as more and more enterprises are learning, putting AI to work inside a complex enterprise isn’t the same thing as buying the latest offering.
Yet that’s the promise of AI at scale: moving beyond copilots, chatbots, and isolated experiments to embed AI directly into the workflows that make businesses run. We’re effectively asking AI to hit a million moving targets, from interpreting real-world conditions and making decisions in real time to taking action across dynamic environments with real-world consequences.
The gap, as so many enterprises have learned, is that the latest model gets you only so far.
So, what’s missing? To answer that question, TechCrunch spoke with Arun Rajan, chief strategy and innovation officer at C.H. Robinson, about what changes when AI moves from generating generic answers to executing work at scale.
“AI itself is not a differentiator,” Rajan said. “Anyone can use AI. Everyone is using AI. What matters is how AI is engineered, operationalized, and scaled. It’s only as effective as the data and context and discipline that power it.”
Rajan has reason to know. After all, he previously disrupted industries while at Travelocity, Zappos, and Amazon and currently leads strategy and innovation at C.H. Robinson, which has seemingly cracked the code on deploying AI at scale. This 120-year-old Fortune 200 logistics company has already embedded hundreds of AI agents across its operations, helping manage and optimize a network responsible for roughly 37 million global shipments annually.
In other words, C.H. Robinson has already operationalized agentic AI at scale. Its success could offer some answers to one of AI’s biggest questions: What does it take to make the technology work across an enterprise at scale?
Context: Intelligence needs to understand the business
Consider something as seemingly straightforward as arranging a freight shipment.
Even if an AI model could identify available carriers, compare rates, and recommend the most cost-effective option, its recommendation is useless if it doesn’t understand delivery constraints, a carrier’s preference, or a customer’s specific requirements.
And this dynamic isn’t unique to logistics. Consider a manufacturing plant that optimizes production without accounting for equipment downtime and material availability. Or an energy provider that balances supply and demand without understanding the constraints of its grid. Or even a hospital that makes clinical decisions without considering an individual patient’s medical history.
Across industries, the challenge is the same: Making a technically plausible decision isn’t necessarily the same as making the right business decision. That’s exactly where AI agents tend to fail. And it’s exactly why companies such as C.H. Robinson are thriving.
“Context can come only from an extremely deep reservoir of operational information and knowledge,” Rajan said. “We’ve accumulated more than 100 trillion proprietary data points over tens of millions of shipments each year. That depth of intelligence is the product of decades of real-world execution. It isn’t something another enterprise can simply buy, build, or replicate overnight.”
And therein lies C.H. Robinson’s advantage. The complexity of operating a global supply chain makes deploying AI particularly challenging. But decades of navigating that complexity have also given the company the operational knowledge needed to make AI effective at scale.
Of course, an enormous data set will get you only so far. The key is connecting all that information — the accumulated human knowledge, historical patterns, customer requirements, and operational constraints — to the particular circumstances surrounding past decisions. Only then can AI systems distinguish between what might work and what will work.
Guardrails: From AI that answers to AI that acts
But knowing what to do isn’t the same as doing it reliably.
“When we talk about AI at scale, we aren’t talking about a chatbot that offers a recommendation and then leaves the next step to a human,” said Rajan. “We’re far beyond that. We have a closed-loop system of agentic workflows where agents that analyze, predict, and flag optimization opportunities are connected directly to a system of agents that make decisions and take action. An agent embedded in these autonomous logistics workflows needs to understand its responsibilities. It needs to understand which decisions it can make. And, maybe most important of all, it needs to recognize when a situation falls outside its authority.”
Installing these guardrails becomes difficult, because most enterprises rely on a single general-purpose AI system meant to handle every challenge. That is a mistake. Instead, Rajan and his team have developed specialized agents designed for clearly defined tasks and with specific guardrails in place, to ensure accuracy and prevent the opportunity for hallucinations.
Consider an order arriving by email. What appears on the surface to be a routine administrative request actually involves multiple steps: extracting shipment details, interpreting customer-specific instructions, validating information, solving for missing data, and initiating the appropriate operational workflow. C.H. Robinson uses agents built to specifically handle each step within established rules and boundaries.
The results illustrate the difference: A process that once took roughly four hours can now be completed in approximately 90 seconds — all powered by a fleet of agents constrained to carry out just their individual part in the process.
The same approach extends to freight quoting, shipment management, and other logistics functions. Each agent has a specific job, access only to the information needed to perform it, and defined limits on what it can do independently.
The lesson is somewhat counterintuitive. As AI models become more powerful, the instinct might be to give them increasingly broad responsibilities. But C.H. Robinson is taking the opposite approach: breaking complex workflows into narrowly defined tasks, giving all agents precise instructions and strict boundaries, and connecting them into a system capable of executing work at scale.
From execution to continuous intelligence: Building a system that learns
Of course, there’s a catch to deploying hundreds of specialized agents: Someone, or something, still has to coordinate them. After all, a collection of agents performing individual tasks doesn’t automatically add up to an intelligent enterprise.
“A core piece of our evolution toward becoming an AI-native logistics company has been pairing our technology with a disciplined Lean operating model rooted in Lean methodology,” said Rajan. “We start with the work, identify where technology can create measurable value, embed AI directly into those workflows, measure the results, and continuously improve. That discipline is what has allowed us to operationalize AI at scale.”
That’s the thinking behind C.H. Robinson’s Lean AI approach to supply chain orchestration.
At its center are two complementary capabilities:
- Lean AI Planner orchestrates workflows and decisions across the supply chain, coordinating how individual agents and processes work together.
- Lean AI Engineer analyzes that execution to identify patterns, emerging risks, and opportunities to improve performance.
Think of it as an orchestration system designed to be self-healing. When agents execute their tasks, their actions generate new operational information. That information then enables the system to identify areas for improvement.
C.H. Robinson believes that this closed-loop approach — connecting AI-driven execution with ongoing analysis and refinement across supply chain operations — represents a first for the industry. Rather than merely automating individual workflows, the company is building a system designed to learn from the work it performs.
The distinction is important. Just as most companies have invested in single general-purpose agents, they have also spent years using software to analyze how their business operates. But operating with AI at scale creates the opportunity for something more ambitious: systems that participate directly in operations and use what they learn from execution to inform what happens next.
This is where the Lean philosophy becomes especially relevant. Rather than treating AI deployment as a finish line, enterprises such as C.H. Robinson treat it as an ongoing process: identify opportunities, measure results, and refine how work gets done.
For Rajan, anything less is a failure.
“The goal can’t simply be to automate more tasks,” he said. “The potential is too rich, too vast, to stop there. The goal needs to be building an operation that gets better at performing its tasks over time, at a faster pace than humans alone are capable of — while retaining the strategic oversight and judgment that only people can provide.”
The ultimate test: Delivering measurable outcomes at scale
It’s an ambitious vision. But for all the excitement surrounding AI, there’s ultimately only one question that matters: Does it actually make the business better?
At C.H. Robinson, the numbers suggest an emphatic yes. The company reports:
- Core business outgrew the market for the 14th consecutive quarter, meaning that the company has outpaced the market every quarter since it launched its Lean AI strategy three years ago
- 65% improvement in productivity, measured in shipments per person per day
- 92% autonomous shipment management for its managed solution customers, dramatically reducing the need for manual intervention
- 100% of quote requests answered through AI-powered quoting workflows, ensuring that customer inquiries don’t fall through the cracks
- Hundreds of hours of work saved every day through automation of routine operational tasks
- Up to 23% faster speed to market, up to 35% higher on-time pickup performance, and 25% potential delivered savings for customers
Consider what those figures represent: not faster responses to prompts or higher scores on AI benchmarks but fundamental changes in how a 120-year-old company operates. More shipments managed. More customer requests handled. More routine work done without human intervention.
And, perhaps most importantly, more capacity to grow without adding resources at the same rate, freeing employees to focus on higher-value, more complex customer needs.
Yes, Silicon Valley will keep building more powerful models and enterprises will keep gaining access to them. But buying the latest technology, deploying more agents, or spending more on compute doesn’t guarantee better business outcomes. Without the operational context to make informed decisions, the infrastructure to execute them, and the discipline to measure and improve performance, even the most sophisticated AI can fall short of its potential.
C.H. Robinson’s experience illustrates what happens when those pieces come together.
And that may be the defining competitive advantage in the next era of enterprise AI. Not who has access to the smartest models, but who can turn that intelligence into reliable execution, measurable results, and operations that keep getting better.
Exclusive insights