C.H. Robinson Worldwide, Inc. Citi’s 2026 Global TMT Conference

No, yeah. Thanks, Ari, and happy to be here at your conference. Thanks for having us. For us, as we talked before, it's not strange to us being here because we know the story and the transformation we've done at Robinson over the last three years. I'll just frame it and then have Damon and Arun get into it because it is a story. You talked about Lean AI. What we've done is introduced a transformation of a 120-year company to essentially make it a disruptor within this industry. We've done it in two vectors. One, a very different lean operating model based on lean principles. This is about continuous improvement, problem-solving, pace, speed, all things that have been around, but we've introduced into this company and I think also have been pivoting this industry.

That has really opened up and allowed our technology, which we are builders, not buyers, and always have been. It allowed our technology and our data set, which is pretty proprietary and the largest in the industry, to really come alive, to make that data intelligent. Between our Lean AI, the best technology, we think, in the industry, and our people, of course, which are some of the best logisticians in the world. Those three have been symbiotic in the system we run at C. H. Robinson and has really allowed some of those gains that you just talked about. That is what has driven it. Let us get into the technology in particular, and also our operating culture, which drives bottom-line results. We can do that. Damon, Arun?

Yeah, Arun, why don't you talk about the tech, and I will wrap it all up.

Yep. From a technology perspective, like you noted, we have improved our productivity by 60% over the past four years. It is sort of this journey of we worked backwards from what would a tech company do to disrupt this industry, right? When you work backwards from that, you say, well, a typical tech company would say, well, how do we decouple headcount growth from volume growth, right? Which is to make this a very scalable business, right? Some of us came from Amazon. That is the model you would do. The first thing you do then is okay, well, how do you engineer the system to ensure that it is scalable, effectively decoupling headcount growth from volume growth? We have been on that journey for multiple years now. And you see the 60% number.

One thing that has changed materially over the last couple of years is we used traditional software engineering approaches for the, I have been at the company for five years, but the first two or three years were more traditional software engineering approaches. Over the last couple of years, in the past, a lot of projects would not make the ROI bar because there is a lot of software engineering effort. This industry is plagued by nuances that are specific to customers, that are specific to carriers because we sit in the marketplace, right? We sit in the middle between capacity and customers. Unlike a peer-to-peer marketplace, this is B2B marketplace, which has lots of nuances. And we sit in the middle of that and a lot of these projects did not make the bar two or three years ago. But with AI, now all of a sudden, engineering is massively better.

We have 500 engineers, but those engineers punch like they are 2,000 or 3,000 engineers as they start to use coding tools. That is on one side. The other side of it is we have engineered a platform, an agentic AI platform, that basically allows us to capture the collective knowledge of the company, the context of the company into our agentic AI platforms. All of a sudden, you are not building software the old way. We are not engineering every single rule or every single SOP into software. It is in a context layer. In a way, the company is being programmed in English. It is probably the simplest way to think about it with the advent of LLMs. This agentic harness we built on top of the LLMs has created this massive acceleration in our productivity in the last couple of years, combined with the lean operating model.

That is one side of it. That is the productivity side of it. The other side of it is, as a marketplace, what do you do? There is pricing on one side, how you price to customers, and then on the costing and the capacity side is how you procure capacity efficiently. In the marketplace, then it is like you make the spread. This notion of personalized pricing that drives our pricing algorithm. We have a machine learning-based algorithmic pricing, which we have had for 10 years. But as the data compounds and grows, this is traditional AI. I will call it classical AI machine learning that effectively drives our gross margins by driving the right pricing for the right customer for the right amounts of value we deliver.

Likewise, on the capacity side, it is about cost discovery and how we procure the capacity at the lowest price, the lowest cost to us, to put on a particular load. Again, if I was to step back and sort of summarize what I said, which is, well, if we were trying to disrupt C. H. Robinson as a tech company, what would you do? You would create this scalable model to drive down to unit economics, and then you would drive this intelligent pricing and costing discovery to drive the best sort of gross margin number. And that then compounds, ultimately, what do you do with the lower cost to serve, like we have accomplished? You parlay that into growth by driving prices lower.

That is the sort of formula for driving our business. Lower cost to serve, higher gross margins, but you parlay some of it back into growth by lowering prices to customers.

Just to put a bow on what Dave and Arun said, since the end of 2022, we've not only benefited with 60% productivity since the end of that year, but we've also seen demonstrable revenue growth, we've seen demonstrable gross margin expansion, and certainly the operating margin expansion we've realized has been on the back of the productivity. But for us, AI, Lean AI specifically, it's more than just productivity. We see revenue growth, we see revenue management capabilities, so think better price, better procurement of freight as well as productivity. We see the benefits of our Lean AI strategy up and down the entire P&L. We know what we're doing is very defensible. We have 450 engineers that build our own tech. This is custom tech for our company's specific problems.

Very difficult to be able to go off the shelf and try to replicate what we've done. We've estimated that you would have to partner with maybe 15 to 20 individual AI platform companies to replicate what we've done. Even once you did that, you're getting a generic solution set for a company's specific set of problems and opportunities versus our customized solutions. I think maybe the most important thing outside of that is the cost. Our marginal cost of ownership once we build an agent is close to zero. Whereas if you're using somebody else's third-party tech, you're going to pay by the drink every single time Right you use their technology.