Conference Transcript - CHRW 2026-09-08

Speaker 4

at Citi. He's going to be asking the smart tech questions. And then we're thrilled to welcome C.H. Robinson to our conference. We have Dave Bozeman, CEO, Damon Lee, CFO, and Arun Rajan, Chief Strategy and Innovation Officer, informally CTO, I suppose is fair. It's interesting. So just before we started, Dave and I were talking about the idea of a of a transportation and logistics company at a tech conference might be a bit strange to some folks. And yet those who are familiar with the C.H. Robinson story certainly would not be surprised by it. Just looking back at the last couple of years, if we think about your North American surface transportation segment, we've seen gross profit per employee up more than 60 percent. Headcount is down roughly 30 percent. Shipments per employee have increased at a double digit rate. There's really been some remarkable things that you guys have been able to do with technology and leveraging ai and and specifically you guys refer to lean ai maybe you could discuss that just kind of as an introduction what are the changes that you've introduced that have been able to drive those gains uh and you know specifically it would help i think if you gave some specific examples around uh what have been the challenges that have kind of you've had to overcome to get to that no yeah thanks sorry and uh and happy to be here uh at your conference Thanks for having us.

Dave Bozeman, CEO

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 kind of frame it and then have Damon and Arun kind of get into it because it is You talked about Lean AI. What we've done is introduced a transformation of a 120-year company. We essentially make it a disruptor within this industry. And we've done it in two kind of 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 but we've introduced into this company and I think also have been pivoting this this industry that has really opened up and allowed our technology, which we're 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 kind of come alive, to make that data intelligent. And then between our lean AI, the best technology we think in the industry, and then 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 Robinson and has really allowed some of those gains that you just talked about. That's what's kind of driven at. But let's get into the technology in particular and also our operating culture, which drives bottom line results. But we can we can do that. Damon or Rune?

Damon Lee, CFO

Yeah, Rune, why don't you talk about the tech and I'll wrap it all wrap it all up. Yep.

Arun Rajan, Other

So from a technology perspective, you know, like you noted, we've improved our productivity by 60% over the past four years. And, you know, it's sort of this journey of, you know, we work backwards from what would a tech company do to disrupt this industry, right? So 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, which is to make this a very scalable business. So some of us came from Amazon. That's the model you would do. So the first thing you do then is like, okay, well, how do you engineer the system to ensure that it's scalable, effectively decoupling headcount growth from volume growth? We've been on that journey for multiple years now. And you see the 60% number. One thing that's changed materially over the last couple of years is we use traditional software engineering approaches. I've been at the company for five years, but the first two or three years are more traditional software engineering approaches. But over the last couple of years, in the past, a lot of projects wouldn't sort of like make the ROI bar because there's a lot of software engineering effort. This industry is kind of 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. And unlike a peer-to-peer marketplace, this is, you know, B2B marketplace, which has lots of nuances. And we sit in the middle of that. And, you know, a lot of these projects didn't 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 there are 2,000 or 3,000 engineers as they start to use coding tools. That's on one side. The other side of it is we've 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. Now, all of a sudden, you're not building software the old way, right? We're not engineering every single rule or every single SOP into software. It's in a context layer, right? So in a way, a company is being programmed in English, right? I mean, it's probably the simplest way to think about it with the advent of LLMs. So 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. So that's one side of it. So that's the productivity side of it. And the other side of it is, as a marketplace, what do you do? There's pricing on one side, how you price the customers, and then on the costing and the capacity side, it's how you procure capacity efficiently. So in the marketplace, then it's kind of like you make the spread. So this notion of personalized pricing that drives our pricing algorithms. So we have machine learning based algorithmic pricing, which we had for 10 years. But as the data compounds and grows, this is traditional AI, right? I'll 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. And likewise, on the capacity side, it's about cost discovery and how we procure the capacity at the lowest price, the lowest cost to us to put on a particular load, right? So 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 the 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've accomplished. You parlay that into growth by driving prices lower. So that's sort of the formula for driving our business. That's lower cost to serve, higher gross margins, but you parlay some of it back into growth by lowering prices to customers.

Damon Lee, CFO

And just to put a bow on what Dave and Arun said, so since the end of 22, you know, 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 then 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, right? We see revenue growth. We see revenue management capabilities. So think better price, better procurement of freight, as well as productivity. So 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, right? So we have 450 engineers that build our own tech. This is custom tech for our company-specific problems, right? 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. And even once you did that, you're getting a generic solution set for a company-specific set of problems and opportunities versus our customized solutions. And 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 you use their technology. So we've generated hundreds of millions of dollars of operating income value since the end of 22. And on an annualized basis, we spend less than $1.2 million on tokens. So just soak that in. In an ecosystem, an AI, where you cannot find a company that's generated a positive ROI from their investment in AI, we've generated hundreds of millions of dollars of value since the end of 22 with a very immaterial amount of token investment on an annual basis.

Dave Bozeman, CEO

And I think for this audience, from an investment perspective, this has been a structural change. Everything that Arun and Damon just said, we are very, very purposeful about where we put the technology, and that's embedding it into workflows. And for us, it was the order-to-cash workflow. That lent itself very much coming from machine learning into generative AI. And now we're actually doing a Gentic within our other business that'll come all around. That has allowed us to automate essentially our back end or operational type of roles that it doesn't matter if the market takes off or if the market stays lower for longer. This system is now going to be structural and that change. We won't add in a number of humans in this kind of kind of, you know, order to cash type of process. And so we always talk about transactional quotes. We have a mature agent that is doing our transactional quotes to come in with email. Used to only get to 60 percent. Now we do 100 percent quoting. We do it in 31 seconds. It used to take 17 to 20 minutes. We do it back in a conversational manner. And the point on all of that, that's allowed us to win more freight, see more freight. But it also says that if we're doing 600,000 of those quotes, you can add a zero. We'll do six million and we won't be adding humans to that because that's a mature agent that is placed within that realm and that workflow. So I know that was a long answer, but it covered a lot to show why this is symbiotic and why this is structural from an investment perspective. And we feel good about that story.

Speaker 2

Yeah, no, that's a great overview and really impressive productivity growth over a short period of time. Maybe diving in a little bit more on the technology stack. I mean, it sounds like a lot of this is homegrown, right, purpose built for Robinson. But how do you kind of see the, you know, who's, as investors here are looking at technology companies, like who's gaining wallet share? Is it mostly the infrastructure providers? Who's losing wallet share if there are, you know, technology vendors from that perspective? And then one of the interesting things you said was just $1.2 million of annualized token costs. I'm sure, you know, some other companies in the Valley would like to be that low in terms of token costs, what we hear. What are you using? You know, is it the frontier? Do you go more the open source route? Would love for you to hit on that as well.

Arun Rajan, Other

Yeah, yeah. No, great question. So, you know, I would say like strategic partnerships, we're a builder culture, right, which means we build our software, which means we will evolve to be an AI native logistics company, right? So then the question is like, well, what are the underlying infrastructure providers we use? We've long had a partnership with Microsoft. And, you know, they're a cloud platform. And they're also the access to multiple models, right? Through Azure, we can access multiple LLMs. You know, we have a partnership with Snowflake. Now, again, I think of these as infrastructure players. The real sort of value that's coming from AI is from our custom built harness that sits on top of the LLMs, right? So think of it as, you know, we have the ability to route a given workload to any LLM, right? We can use a frontier model if it's a complex reasoning problem. But if it's a simple transactional thing, we can use open source or we can use an older version of the models. We have a router that will route the work based on the sort of complexity of the work to the appropriate LLM. And in some cases, we just host our own open source LLMs. So the bottom line is, in the end, because we're a custom-built shop and we have our own harness and we built these agentic workflows, the way we architect our platform kind of accrues most of the value to us, right? certainly you know the token cut there's some token costs that open ai uh probably gets a giant share that you know 1.2 million dollar token token spend that uh damon described uh but there's a bunch of volume going to open source um and over time you know we will continue to route the simpler workloads to to open source models which is why uh the the costs are so low so then the question is like, well, how do you do it? You know, just like I think of this as you kind of roll back 15, 20 years to the cloud. And when the cloud first came out, I think you had this tendency of people to sort of like say, oh, the cloud is here. Like, I don't have to provision hardware in my data center. So I can just spin up this, you know, this instance in the cloud and like spending went crazy, right? I think the same thing is happening with LLMs where it's like, well, I can easily access this intelligence. So let me point my application at it and just you know hey look it's it's great but it costs a lot uh because of the way we built our harness the way and the way we've engineered our agents each agent has a very specific purpose and and it has a very specific context which means it has a limited context when context so it doesn't hallucinate but equally its token consumption is very limited and also we can use an older version of the model or an open source model to do the work so i think this all goes back to sort of to me that this looks this era looks like you have to get your platform engineering and your

Dave Bozeman, CEO

infrastructure engineering right to be able to get the true value of llms and if you don't harness you don't create this harness i think uh uh i think you end up in a lot of labs rich right so yeah i think again for this room why is it damon used the word earlier and why is this defensible it's defensible because one thing running on the back of all of that we've said this a lot already in the past is our data set and we have a largest data set in the industry it's 100 trillion data points that have been accumulated over time and and that data set is proprietary to robinson and um you know when you when you build a bespoke platform like we're doing with that data set, that makes that very powerful and very hard to replicate, even if you're an AI native company, start enough to do that because you don't have access to that data. You have access to some data, which can be averages of averages, but not the level of data that we have. And what Arun just laid out is super, super important on why this is a deeper, wider moat.

Damon Lee, CFO

And there is no hobby spend on AI at C.H. Robinson, right? So every dollar we spend on tokens, every dollar we spend on engineering capacity, you know, has a ready-made high probability ROI assigned to it, right? So we don't just give, you know, 9,000 employees or 10,000 employees co-pilot license and say, go try to do something creative, right? The only dollars we spend are based on a very high probability outcome from an ROI perspective, which is why we've had the success we've had.

Speaker 2

Yeah. And I guess as you think about, you know, you talked about the productivity gains, which is one side of it, but then those incremental revenue opportunities, right? Like building a more sophisticated pricing engine. Like how much would you sort of attribute these AI investments to driving productivity versus driving revenue? And then maybe just give a sense for the audience, like what are your biggest areas ahead as we look forward in the next few years that AI will tackle?

Damon Lee, CFO

I'll start and then hand it to Arun. I would say because we let the highest ROI project dictate investment, difficult to say what percentages of our benefits come from revenue management versus productivity versus growth. Because they're all in the same funnel competing for the same investment dollars. I would tell you, though, the revenue management unlock has been substantial. I would say as little as four years ago in this industry, just the way in which the industry priced was very unsophisticated, very low frequency, meaning you'd set a pricing strategy at the beginning of a month and maybe determine at the end of the month that you win or not from that pricing strategy. Whereas today, with our technology, with our discipline approach from lean, with that data set that Dave referenced, we're setting pricing strategies on a seconds and minutes basis. So an example we use a lot is we come in Monday morning, set a pricing strategy at 8 o'clock. If that's not yielding the volume margin expectations that we anticipated, we could change that strategy tens of times an hour, hundreds of times a day, thousands of times over a quarter. Whereas as little as four years ago, you may have only changed that pricing strategy once or twice in a 30 or 90 day period of time. Just the frequency in which we're interrogating the market from a revenue management perspective and just the ability now to access that data set that Dave mentioned is over 100 trillion data points. Before the advent of AI, the ability to analyze that data was extremely limited. You know, now with our advanced machine learning, with our predictive analytics, with generative and agentic AI, now we can use a significant portion of that data to drive arbitrage opportunities in our marketplace. And we believe, as I mentioned before, what we're doing on revenue management in the logistics industry, we believe is unmatched.

Arun Rajan, Other

Maybe I'll add, you know, I think the way I would say it is that an agentic AI platform or a harness, you kind of combine that with sort of first principles. And I think you get the same approach we've taken to productivity or the approach we've taken to revenue management and gross margins. The same applies to pretty much everything. So if you convert that into, say, go to market and our sales and account management motions, right? So think about a typical sales situation. There's a bunch of prospects that we have to call out to be top of mind, right? A lot of that's handled by AI, right? So all those, because those prospecting or reactivating customers who, especially small and medium customers, we reach out to them purely via AI, right? Because now the humans can focus on something else, right? They can focus on actually serving the customer. This notion of how do you take customers from the top of the funnel and drive them down lower into the funnel? An example might be, you know, we're not just connecting supply and demand. We're solutioning for customers. Customers are asking us, well, because, I mean, you know, truckload, the logistics sector isn't like a monolithic, single dimensional market, right? There's this flat band. There's like, you know, temperature controlled. And there's, you know, bulk movement. There are all kinds of different types of freight. I'll call it modes and services. And so when customers ask our account manager for that expertise, often they have to call an expert, a subject matter expert, to come in and join the call, right? So now we have AI agents that are trained to be that subject model expert or are trained to be a supply chain engineer because we only have so many subject matter experts and so many supply chain engineers. You apply the same principle of scalability. Now, you can take these roles, encode them into an AI agent that participates in the call to help us close deals, right? So, again, it's the same principle that we apply to productivity because this is a different type of productivity, but it's in the sales motion.

Dave Bozeman, CEO

I think just to put a bow on all of that to your question, I'm super excited in the next chapter of Robinson. We always say last two years have been awesome. The next two years are going to be very much more exciting than the last two or three. And I'm super excited about what the teams are building on our Argentic platforms when it comes to, say, our global forwarding business that will ultimately go to our NAS business as well. Very, very complicated business that if you do a quote, it can take upwards of 10, 12 days in a sense to put together a really complicated quote to move things from, say, China to North Carolina. And now we're building a platform that could potentially have agents do that quoting in a matter of hours versus days. And that's pretty significant in doing that. And so super excited about that technology. And then that also comes back into our NAS business as well, where that tech time is immediate. And now we're able to take a technology of agentic from generative and reapply that, bringing some things that we couldn't do below the line, above the line. That's why it's going to be super exciting for Robinson going forward.

Speaker 4

Dave, so I think you guys have done a great job of describing what Robinson is doing that's different from competitors and difficult to replicate. I know we don't have a ton of time, so I want to make sure we hit on kind of broader transport type of questions. One of the concerns that a lot of people had recently is potential slowdown in the macro, obviously rising interest rates put some pressure on potentially on the consumer, on industrial activity. Speak to what you're seeing out there from kind of a supply demand standpoint, because a lot of people think that, you know, we've kind of experienced this freight cycle inflection and that, you know, there's room to run. Do you agree with that? Do you still see that as the case? And then what does the earnings growth look like? Let's assume freight demand remains somewhat tepid. How important are these tech initiatives in kind of still being able to drive earnings growth regardless of what the macro environment looks like?

Dave Bozeman, CEO

Oh, good question. And we like that because it gets down to the receipts in our business, as you know, Ari. And we, Damon and I, we always talk about the receipts, first of all, on the macros. I mean, you guys see it out there. You are correct that this has been somewhat of a supply side correction, meaning supply has tightened up, spot prices have gone up. We see that, of course, and we're participating and doing really well when it comes to the spot side of it. But we're also doing very well when it comes to the contract side of the business of what we're doing. From a demand perspective, you called out industrials. We see some industrial technology out there, be like data centers and things like that. But we're cautiously optimistic on that because you really have to continue to watch retail, housing, and manufacturing. And those are the things that are going to really drive freight for the most part. And I think some of those are a bit muted right now and some some here and there green shoots. But but we are monitoring all that. But the thing with Robinson is higher highs, higher lows, as you know. And we are certainly winning at a at a very four and a half year type of freight recession. And we think we will not only linearly, but exponentially have a curve that when the market inflects with the thing we built, it is only going to, it's going to generate even more, but we'll get into some of the actual numbers and why we feel that as well.

Damon Lee, CFO

Yeah. So just on the earnings potential, just a couple of double clicks there. So I'd say, you know, in an almost four-year freight recession, right? Certainly in 24 and 25, we had over 20% earnings growth in both of those years. Certainly the market didn't help drive any of those earnings performance. And then certainly if consensus holds this year, it'll be another 20% earnings growth year in 26 with again, another flat down market. And Q2 was, I think, a real important quarter for us. You had the market down again, four and a half percent. That was with spot cost up over 30%. And yet our AGP per load, which is a key KPI for us, was flat. So if you'd asked somebody two years ago, could a broker have flat AGP per load when spot rates were up over 30% in a market that was down 4.5%, they would have told you it's physically impossible. We demonstrated that in Q2. I think the other exciting point in Q2 that just shows the potential for earnings growth is our operating leverage. So in Q2, you know, we had substantial operating leverage, you know, so AGP dollar flow to operating income flow over 90%. You know, in fact, you know, Rich at Deutsche Bank reminded us that was the best operating leverage performance of any company in logistics, including the assets. Right. So here you get a broker demonstrated operating leverage. That's a concept nobody thought was even possible. Supposed to be a variable cost model. We've transformed Robinson now into a semi-fixed cost. So what you get with Robinson is you get the best of both worlds. You get the operating leverage of an asset in an asset light model, right? So we believe we've created something quite unique at C.H. Robinson, which is why we believe we will continue to outperform the market, both from an outgrowth perspective and both from an earnings perspective as we go into the future.

Dave Bozeman, CEO

That's important, Ari, and you see it, and that's in a really tough backdrop. So as this inflects, this system only goes wider and deeper.

Speaker 4

We're excited to see where it goes, certainly. I know we're close to time here, but last question, because it's probably the question I get most often. And obviously, you know, the stock has sold off a bit on concerns around broker liability and Supreme Court ruling that opened up brokers to liability in the case of accidents. Speak to that for the investors in the room or the investors listening in who might say, I can't get comfortable with the C.H. Robinson story until I know how this settles out. And potentially we're looking at years of brokers kind of fighting in courts or fighting these claims in courts. How do you think about that? What would you say to investors to kind of get more comfortable around that? And then just if we could tie it in quickly to the tech point, talk about how you can leverage AI maybe in carrier screening or what's being done there as well.

Dave Bozeman, CEO

Yeah, so three, four, four vectors you're really calling out, and we'll try to do it very quickly for you here. From a Montgomery perspective, these are just facts. We're a data company. We talk in data and facts. What the Supreme Court ruled on that was just no more of a preemption for brokers. But the facts are, prior to Montgomery, that's just one defense now that's off, but we had to deal with well over 30 states that didn't have that anyway. And so for Robinson, we've always had a docket. We've been public for 28 years. We've had a docket during that time. Everyone in this industry has a docket that they're dealing with. We've had tens of cases in that docket. We ship 37 million shipments a year. And over that time, that's hundreds of millions of shipments with tens of cases. And so that just tells you that, one, we're disciplined, we're measured, we know how to defend a docket. But more importantly, we run a very, very safe and disciplined company in doing that. The lower side of tens of cases, hundreds of millions of shipments. Now you break down that. So that's the facts of that. The second vector then for investors is, OK, Dave, what about the impact of inflationary insurance because of this? Will you get that? Let's talk about the facts of what that is. And maybe we'll finish off with the technology on what we're doing on vetting, which we think is the best in the industry.

Damon Lee, CFO

Just to round out LIPE, which is the last case, right? So we feel really good about the facts of that case. We chose not to settle that case. We feel like we will prevail on appeal. So, I mean, that's the facts of LIPE. 98% of all of our cases either get dismissed or settled. We don't think that trend is going to be disrupted post-Montgomery, post-LIPE. So we still believe the vast majority of our cases will be settled. Average settlement amount has been somewhere between $1 and $3 million. We think that trend probably holds into the future as well. As it relates to insurance costs, I honestly believe you don't have to wait until LIPE gets through the appeal process to get comfort in C.H. Robinson, we're going through insurance renewal right now. So I believe once the insurance companies essentially provide their verdict on C.H. Robinson for 2027, I think that'll give you great insight to what they view as the risk profile of our docket and what they view as the risk profile of Robinson. You know, some of the more bearish sentiment on the street that insurance costs are going to go up hundreds of percent, we do not view that's going to be the case for C.H. Robinson. We think inflation will be a very manageable number, that the majority of it will get a pass-through freight rates anyway, right? And so ultimately, the consumer will bore the majority of that cost, any legacy costs borne by Robinson. We get paid to offset that anyway. Just a baseline fact to show you how immaterial insurance has been to us historically. Insurance plus claims is less than 50 bps of gross revenue for Robinson, automobile liability insurance on its loan is less than 25 bps of gross revenue. So even if we did see a material increase in inflation on insurance, it's not going to have a material increase on our earnings. So look, we feel really good about where we're at. We ultimately believe the current legal landscape will drive a pretty accelerated consolidation of our industry, which will be once we get through the fog of war on Montgomery and life, we feel like this will be a very strong bull case for C.H. Robinson on the other end. So we've been active buyers of our stock. We continue to be active buyers of our stock. We're putting our capital where our words are.

Dave Bozeman, CEO

And finally, you know, we're driving a legislative and rules-making vector as well as we're working with FMCSA to get a standard through the Department of Transportation and have a lot of our transportation industry peers that are following with us on responsible freight. I'll be in D.C. next week and also work on a legislative solution to this as well with certain bills that are going through that we think that will apply the right accountability responsibility. So we're going to continue to lead the industry on that.

Speaker 2

And so thanks for having us.

Dave Bozeman, CEO

And hopefully your investors understand our story. More exciting to come in the next few years.

Speaker 4

It's a great story. And you guys tell it well. Dave, Damon, Erwin, thank you all.

Dave Bozeman, CEO

Thank you. Appreciate it.