Hey, everybody. It's Patrick Escolas with another Tire Tracks Banyan Technology podcast. And right now, we're doing another special miniseries on focusing on the role of AI in freight procurement. And as we delve deeper into that topic, we have with us Jason Roberts, the SVP of Digital Enablement for MODE. How are you doing, Jason? 

I'm great. How are you, Patrick? 

Good. I'm really good. Let's start with what is an SVP of Digital Enablement mean, and what does he do? 

It's a great question. Believe it or not, that is a much simpler title than I had for a couple years at most. I'll have to send you the business card. We'll just count through how many consonants were in it. But good question. I kind of try to think about what my objectives are into three kind of major buckets. Over the last couple of years, the CEO really wanted me to focus on building out our digital ecosystem. 

And just a quick backstory before that, I was the president of Avenger Logistics. Avenger was one of the companies that was acquired by MODE, at this point about four and a half years ago. 

Named that way because a big Marvel fan, or where's the Avenger come from? I'm a comic geek. 

Great question as well. I mean, yes, Avenger was – Marvel was still in its rise up instead of its – I think, the peak. 

We're not quite at the peak of that. 

But the peak has passed. But that was certainly a piece of that. And the other piece of it at the time was – yeah, look, I'm sure you know that in Chattanooga, right, it's a very saturated market in the brokerage space. And most people got their chops at Access America. When Access got bought, when they got bought by Coyote, there was a lot of splintering, other companies built. And then when they got bought again by UPS, even more companies decided to go do their own thing. And so part of it was to kind of avenge – 

Avenge. Yeah. No, that makes sense. No. You got brought into mode uh through that acquisition, and where did that lead? 

Yeah. On my side – and, again, why I was really passionate about stepping into this role was we wanted to build – number one, we wanted to build a digital environment for our carriers to come to and operate, right? We were not the first to that game. But what we wanted to make sure we did and what we saw with others – and sometimes there are first mover advantages and sometimes there are disadvantages to that. And in this case, we saw some of these companies kind of make some of the wrong decisions, right? 

I won't name names of some of the companies that had very high valuations that then went under. And what we saw was that they tried to kind of "completely automate the life cycle of a load in the process". And what we found is that you still got to have some operational acumen to understand that. Look, again, not a knock on any technologist, but most are only ever going to have a theoretical view of how this business works. And so it's important to me that we looked at it through the lens of an operator. And so that was why I accepted the role, stepped into it, and wanted to help us build that. 

Yeah, I was just to say, as you're in that role, what's your objective? You said basically, I think something along the lines of working on this digital ecosystem or the technological ecosystem of MODE Global. What does that pertain? Are we just making sure that a CRM is connected to a web portal, is connected to a TMS, and a WMS, and a fleet management system? Or is it something different than just plugging these things together? 

That is part of it, right? That is a critical piece and is the interconnectivity of our platforms, right? An organization, particularly for ours, we have three different major brands, at least two different major TMS's. We have a robust agent network, all of which makes us complex. And so that's really an important piece of it. But that was number one, was build this, create a space for our carriers, and be able to leverage our network. 

The other piece of it was building out a network optimization tool. We call it VPO. Stands for visibility, predictability, optimization. And so through that, we built out again a platform where we could take a customer's data set, layer it up against ours, and then show them, "Look, here's how we would – here's where we buy better than you. Here's where you buy better than us. Here's where we could convert truckload to intermodal. Here's where we could consolidate LTL into larger pools." 

Wait, wait, wait, wait, wait. But what was so wrong with the Excel spreadsheet RFP? I mean, everybody just loves that. And then you've got your 23 different tabs, and you're not sure which one you're supposed to look at. And it's copied and pasted eight times, so you can't really use the data to run any VSYNCs. No, no. I mean, what was wrong with that? 

Yeah. Exactly. Exactly. As an industry, we are well-known to adopt last when it comes to technology. And we've made a joke before that there's folks still out there using T-cards, and it really is wild west in some places. 

And to that point, and that's something I'm glad you brought up because I didn't want to offensively bring up to someone that is the SVP of digital enablement, how does that affect that piece where, yeah, logistics and brokerage does seem to be the red-headed stepchild of technology from an industry standpoint? How does that affect what you're doing, and how is that an obstacle that you have to overcome? 

Well, I think, look, it's an obstacle that anyone in our space has to overcome pretty regularly. We run through that all the time, right? Part of those exercises, as we built this out, was the cleanup of our data, right? And we've done this with a couple of different partners. We built out a predictive rating algorithm for our truckload. 

Well, the first step is, "Okay, take the data in." As you can imagine, it was dirty. I'll just say – 

I was going to say, you've got how many acquisitions? You've got three different departments, two different TMS's, a laundry list of people that may or may not still work there, or may or may not have put in credible numbers. And now that's the core you're basing it on, yeah. 

Go to any broker and ask them how they price their freight, or go ask them how they enter a load in their system, and you will get plenty of different answers. Yeah. Look, that was tough, right? We had to go and have a realistic look in the mirror and say what we're going to do differently. What kind of expectations and standards are we going to set across the enterprise to make certain that we don't – this is step one. That's table stakes. The data has to be right and true, and you have to have a single source of truth to build anything. And if that is tainted, if that is problematic – 

Then you got a poison well. 

Yeah. Don't even build all of this, right? Like, "Hey, let's build a pipeline to get this water across the entire town." It's like, yeah, it's poison in the water. 

It doesn't matter how high-end your pipeline or your truck's building it, it's still bad going through it. With all that and with what we're talking about, or at least I'm talking about within this miniseries is AI, how does AI come into play with that? Because like you said, true, full, 100% automization isn't the goal in logistics. And we've kind of seen that it does not work the way that it may work in a different industry. 

Yeah. Third bucket, right, is kind of all things automation in my role. And what I will say is over the last year and a half, two years, but really, back half of that, I have seen an – and there's problems with this too, but I've never seen something become commoditized faster in our industry than AI. I'm going to throw quotes on that because there's a downside to that, too. Everything isn't AI, by the way. Everything that's been pushed out isn't AI. 

No. 

It's kind of like you throw that on, then people, I think, just believe that there's an increase in valuation if you have that in your name. 

Sure. 

But my kind of take on it is that AIs won't replace people in freight, but people using AI absolutely will replace those who don't. The winners in this space won't be the ones who build the flashiest tools. It'll be the ones who embed AI into everyday decisions, freeing up people to focus on relationship, strategy, and exception management. It will be a copilot, a silent teammate. It won't ask for credit. It will change everything behind the scenes. 

Let me just jump in and say obviously, in my position, but even not. I'm a huge believer in the potential that it has, but it's all about the deployment of that within an organization. And so what we've thought about is two buckets, everyone's job, your job, my job, the broker on the floor. Everyone has – they were brought there for high-value activity, high-value good. And then there's this other bucket, and you call it a lot of different things. You can call it a necessary evil. You can call it low-value activity. You can call it some other words. 

I think I know where the podcast host fits into these two buckets. And I don't think it's the number one. 

I mean, that can't be true. That can't be true. I mean, you got this nice gear. I mean, that's a good-looking microphone. I'll use a broker, for example, right? You get a load. Customer sends that load to you. Okay, building the load. 

Sure. 

Necessary evil got to happen. Is it high-value activity? No. Is it generating revenue? No. Got to get done. We got to set an appointment at the shipper. Is that high-value activity? No. Is it generating revenue? No. Does it got to get done? Yes. Right? What does generate revenue is me having a conversation with my carriers, having a conversation with my customers. Right? 

We've tried to think about how can we automate all of that so that this is the – the thing we were brought here to do, we get to do at a higher level and a higher clip. 

Yeah. Because the hardest part for – I mean, not hardest, but I'm a salesperson here, but the part that you get paid to do there is to have those conversations and talk pricing. All those things in between are what get in the way or what slow you down. And it doesn't mean that they're not important, like you're saying, but you can speed those up. In theory, you get more conversations, more opportunity for pricing, and more wins, right? Is that accurate? Okay. 

Yeah, I think that's spot on. When we think about the procurement process – and I'll look at that through both lenses, right? The lens of a provider and the lens of a customer or a shipper. The lens of a provider – look, for our side, if we're a third party, there's a lot of work we've got to do to get accurate pricing out to a customer, right? And in the industry, most people are using some combination of some benchmark data of some rate providers. Again, I won't – 

You talked about you just take 10% off across it. You say you're 92%, 98% on time all the time. And then about 6 months from now, you have that awkward conversation where you may or may not have underbid on some of those lanes. 

That's right. Yep. Yeah. And look, I think it's important that – it is important that you say that because that is going to be one of the challenges still or one of the hurdles, because there's going to be a group of people who are doing this with pure pricing, pure data. And again, I think AI helps in a big way there because it can extrapolate historical data, current trends, forecasting. We can go on our side now and have workflows. 

Think about like an AI agent, which is that is, in my opinion, the future. You're going to have – someone who's going to be sitting in my role or your role in 10 years, their skill set, yes, there will still be sales and ops acumen needed there. But the other skill set that's going to separate them is how good they are at prompting AI. How good they are at building out and engineering workflows for their AI agent, copilot, bot, whatever you want to call it, right? 

And so in our case, we're building out workflows to say, "Okay, here's a load I need to get covered. Here's how I want you to go find a carrier for this spot load. I want to go into our system first. I want you to go down the list of the last 10 carriers that ran it. If that doesn't work, then I want you to come over here to this platform. I want you to reach out to these guys." And by reach out, I mean I want you to call them first. If you don't get a response – and that's AI. Then send an email. That's AI. And then you've got people for escalation management. They talk to a carrier on the phone, and the carrier sounds pissed. All right. Hey, let's talk to a human, right? Let's get off. 

Right. Yeah. 

But you're still going up against the guys who are doing exactly what you just said. The challenge is going to be customers also, right? They will be the ultimate decider of how successful this is. I can do all of this till I'm blue in the face and say, "No, no, no, but my price is real." And all the reasons why, they still may go with ABC. 

People still go with that ABC until it burns them. Now, interesting piece about what you say here, and I think this is how I've been starting to try to use AI in my own life. It's less of it's going to do it for you, and it's more you become a manager, and you're delegating these tasks now. And kind of what it sounds for the future or the goal for MODE Global or from your view is that I'm an agent, but I may have three or four of these workflows of these AI agents doing separate things, and I'm going to make sure they're all going and getting me either the point of that workflow where I'm involved. I'm going to, "Hey, yep, you did or you found it. This is a good thing. I'm going to go execute that deal and press okay." Because now we have a human saying this is good. Not some crazy number fund where it's not what it should be. 

I don't know if you heard, as AI first came out, and I think it was a car dealer somewhere in the south, and somebody found out how to get a car for a dollar because they just tricked the AI bot into using logic against it. Having that person there managing it, A, you're still going to have your personal connection, but you're also going to have the efficiency of the AI. Is the manager kind of aspect, is that accurate? Am I missing something there? 

No, I think it's accurate. And again, I think that there's some reluctance, there's some fear around it. And to me, you embrace this, right? 

Yeah. 

Like you would anything else. 

I was going to say, is it any different from any other change management of I've used a notebook my whole life, and someone came in and gave me Windows 95, and now I got to do this? Is it any different from that? Or is it because AI is different from the human thought, it's a bigger disruption to our normal pattern? 

Yeah, I think it is. I think it is bigger than that. I've been an early adopter. And look, not because of this role necessarily, but it helps. But using some of these generative AI platforms, right? Yes, it can give you some good data. It can also give you bad data. 

Yeah. 

You have to be able to understand and know when that was good and bad data, right? And and that's a big piece of this, right? If I went into – even if it was tethered to my own network, I have to know that when it hallucinates, or I don't know if that's the term. 

Love the idea of an AI hallucinating. I'd love to see that trip. Yeah. 

But I'll give you a good example where some of it's not there yet, right? We had a scenario where I wanted to put a banner up on a web page to freeze it, and I wanted to show that. It required some coding. I'm not and never will be, God willing, a coder and – 

Way above my head. Yep. 

Yeah. I started saying, "Here's what I want. Here's what I'd like to have. Can you give me some codes where I could put that on a web page temporarily?" Like, "All right, use this code." I'm like, "Okay. I used it. It didn't work." "Go back." "Hey, it didn't work." Oh, that's because we put the examples in the coding. I'm like, "Please don't do that." "All right, take it back. Put it on again." "Hey, it didn't work again." I looked back at the code. I'm not a coder, but you still left the examples in there. Like, "Oh, okay, God." And this is like a premium version that I'm paying for. 

laughing because I have had this exact situation where you're just like, "Okay, yep." And they're like, "Yeah, I got it." You're like, "You did not. Do this." They're like, "Yeah, I got it." "You still didn't." It's kind of like, for me, it's telling my six-year-old, "Hey, can you put the dishes away?" And they're like, "Yeah, I did it." And they run off. You're like, "Why are the dishes still here?" You know? Yeah, it's both – 

It's almost like the amount of time you took to cheat on the test. You could have just studied for the test. 

That's where I was going to say, is at what point with these prompts and this trial and error in AI, or is this just the learning curve? Are we using more time than it would take us to do the task? Or is this kind of similar to, "All right, I know you got work to do, but you got a messy desk. And you got to clean up the desk before you can get the work done." Sure, it's not leading to the work, but it has to be done no matter what. 

It's a really good point because one of the biggest early challenges that we've had is the when. When is this good enough to roll out to your audience? And we ran through that, right? One, it's setting expectations early. When I'm going through that, doing that on that platform, what I know is it is generative, it is learning, and it is tethered to me. I know that it's improving. And I'm giving it feedback. I try to treat it like I would a really eager intern, right? I'm giving you feedback on what you're doing right and wrong so that you're learning and you're storing that. 

And internally, we had the same kind of thing where when we roll this out to a smaller group, we set the expectations early and say, "Look, here's the vision." One, we've let everyone – we've explained the vision to everyone. I think that's huge. Because too often, we can just tell someone, "Hey, do this. We need you to do it." And that's it. Instead of we want – 

Here's why. Here's the big picture. Here's where that adds into that. Okay. Yeah, that's a huge point. 

Yeah, if you're giving them the why behind it. Here's our vision for this. I want you to see all of it. And here is this piece that's a cog in this larger wheel. And I need you to know going into this that there's going to be bumps in the road. And that's why we've chosen you to help us with this because we got to identify that to smooth this out before we ever are ready to expand, scale, etc. 

Yeah. And so how have you guys decided when it's good enough? When does better become the enemy of good in this? 

Yeah. Also, I think you have to look at each use case and determine for yourself. And this is another big piece of it. And I've put a couple posts out on this. We went to Manifest a month or two ago. And, man, when I say we had 40 meetings with AI solutions providers, that's the real number. 

I can only imagine that was still probably only half of the AI options that were at Manifest. 

Yeah. And it was always, "Hey, well, we do it different." Like, "No, you don't." We do it different. But the the biggest challenge for all of it is you shouldn't chase the shiny toy. It's what does this do for me and my organization? Can I CLEARLY ESTABLISH DIRECT ROI from this? And that's part of the challenge with some of it is these providers are asking you just to take a leap of faith and say, "Hey, no, once you've done all of this, your whole operation's going to be more efficient." I'm like, "What does that mean?" 

Yeah. Are we talking 1% or 2%? 

No. You're going to save time. Okay. What does that mean? 

How much time? Right. Exactly. Do you have any hard numbers? Yeah. 

Yeah. And so I've tried to be upfront with those providers and say, "Look, if you can't show me how I'm going to get net ROI on this, whether that is –" again, it's margin expansion, it's headcount efficiencies. What does that look like, and where do we deploy it? Even though you're a solutions provider delivering AI to me, you still have to be able to also show me and tell me how I'm supposed to deploy this to get the money back that you're asking me to spend with you. 

And I think you bring up a great point. It depends on the work case or the flow. Because in a situation like you're just talking about, anybody that comes off the street and says I can save you this amount of money and time and hasn't asked you for the data or how you do things, it has to be talking through the other end. Because how can you relate anything without having at least your baseline or your status quo of now to compare it against? 

One of the other things that we've gained from a lot of these partnerships is we talk again about like, "Oh, well, the data. This data is so valuable that you didn't have before." I'm like, "Yes, but if I don't know what to do with the data –"

How valuable can it be? 

Yeah. You say this. But how can I use it, right? And that's another kind of opportunity, I guess, I would say, is when – my advice, my take on it is, again, pick a use case that's linear, pick a use case that is easily identified, transparent, build from there. 

Yeah. 

But you also have to understand that who you're partnering with or how you're deciding to build that, even though you're starting with this small use case, how does it, and how can it, or can it roll into your larger environment seamlessly, right? 

Yeah. 

The other challenge can become, "Hey, we decided to partner with company A because they're going to help us with POD collection." Okay. Okay, that's great. We're going to have an AI agent, right? Everyone loves you in that. Yeah, great. Here's what we have. Here's what our DSO is. Here's what our outstanding balances are. Oh, we can reduce this by X. Got it. Sure. Can you tie it into my TMS? No. Okay. Why are we talking? 

Yeah. Yeah. Again, it goes back to the same thing. I met a TMS provider right here. If I wanted 23 different things to do 23 different tasks, then I wouldn't be looking for a robust system or a broker that does all of these things. I have, as I always like to say, one hand to shake and one throat to choke. Now you're looking at, okay, something's off. Which of the 23 different softwares that touch that before I get it actually have an issue? 

Within that – and I think as kind of we talked about with the workflow, and the data, and working with these partners to use, whether that's MODE's data or a prospective customer of MODE, how is AI being used with that data and with a tool like your VPO tool that you had talked about before, which to my knowledge existed before AI? How is it more enhanced with it? Because it was a pretty cool tool where you'd be like, "Hey, you're running this amount of shipments. You're doing it this way. But if you run these via the train, this via LTL this way, and get off at this stop, we can actually save you 15%." Does it just do the same math with AI? Or is there an added layer of value because you've added this constantly number-crunching AI agent to the field? 

Yeah. We haven't rolled this out commercially yet. However, kind of what we're working on in our sandbox, let's call it, and you're starting to see this tied into some other BI platforms, where we want to get to a place where you can prompt, right? A user can prompt the platform to pull out the relevant data, the relevant insights that they want. 

We've seen this with a couple of our providers. Think about like a Power BI, think about Omni, or Amazon Q, or any of these other BI, this intelligence, where you can as a user – it's simplifying all that, right? The reality is it's really complex on how they're pulling this data, but – 

It's us not being coders, but also being able to right and left click with real confidence. 

Yep. I'm going to go in and say, "Hey, I want you to pull the top 10 lanes that show the greatest amount of savings for this customer, and I want to be able to –" and it's going to pull that. Oh, got it. I've analyzed it. I'm pulling this dashboard up, and I'm giving that to you. A good example is like Amazon's. When you go on Amazon, that user interface is so easy to use.

My wife finds it way too easy, the amount of boxes every day – 

Yeah. But the reality is it's a really complex system beneath that that suggests the other items that you might like that – again, the beauty of it is how simple it appears to be to you. And that's the goal with all of this, right? It is over our heads. And we're not technologists. And we're not going to be technologists. We're sales and ops people. And that's what we want to be. It's still what our business is. MODE is not a technology company. We're not going to be a technology company. 

You're going to use technology. 

We're going to use technology. It will enable us. And we want to get there quickly because we know that we can create real differentiation between ourselves and competitors by getting there. 

And that's a question I had. Now, you want to get there quickly. Is that because it's a race? Is that because it's adapt or die? Or is that because – what are the market conditions that – and it might just be because the personality or the culture of MODE to be like, "Hey, this is out there. Let's find out how to use it and do it best." Or is it like, "Guys, if we don't, everybody else is going to do it." Where does that sit? Or is it a combination of those things? 

Yeah, I think the reality is it's a combination, right? Yes. Adapt or die. Yes, we cannot be look, Blockbuster had the opportunity to buy Netflix, right? People forget that. They had a chance to buy, and they're like, "Nah, that doesn't work." The other day, I saw Ryan Schreiber, I think, of Metafora put a post up a few weeks back. And it was a billboard where Blockbuster had said, "Our people are our algorithm." That was literally what it said. This was 20, 15 years ago, whatever it was. And it's so ironic and so true, right? Yes to that. 

Also, the market conditions now matter. For us, for our carrier platform, for example, right? It matters because of the gross imbalance right now between – that gauge of power between the shipper and the carrier, it's heavily on the shipper side today. That also means that we can ask our carriers to do something outside of their comfort zone. That thing is, "Hey, please come into our platform. Let us show you all of the clear benefits you're going to get." Now, look, the good news is when they get there. 

It's easy to prove, right? Yeah. 

But you got to get them there. 

There's the bit of that leap of faith that you had talked about before. Is AI always a bit of leap of faith if you haven't been using it with whatever you're getting into? Or should always be a bit of skepticism that you need to verify? 

Today, you should have some healthy skepticism. 

Okay. 

We are so early to this. 

Yeah. 

Yeah, my opinion on that is we owe that to ourselves, but we also innately do that so well. I think it's very fair, right? I shouldn't and I cannot go to my CEO and deliver a product or a use case and say, "We should use this. You should trust me. That's really all we need here." That doesn't fly, right? 

The used car salesman of yesteryear. Come on, man. It worked for years, but that was before the internet and people had access to information. Yeah. 

That's right. 

And with that, and with this discussion of the goal and where it is with the market conditions, is this something that are you close? Are you halfway in? Or is it always just going to be a learning, growing experience? And the second you think you've got it, something new is going to come up, and you're going to have to jump on that one? 

Yeah, it's a good question. I think, right now, it is. Look, no, I don't think we'll always have to keep doing it, right? Look, what we're doing isn't rocket science. Our industry, I mean. What we do, we move freight from point A to point B. Yes, that can be more complex than it sounds, but it's also – we're not having to create combustion engines over here. 

Right.

I do think we're going to get to a place where that cycle, the hype cycle, right? Where we're kind of still – that hasn't peaked yet. And on our side, I think it's more about you start with these different use cases. You try, you document, you expand. If there's other opportunities from there, you build from that. But I do still think that on our side, I'm going to say we're rounding second. 

Okay. 

And we have tools now that work, that have worked well, but we have to expand upon those. And we're delivering those to customers. We're showing that to customers, but we're not to a place yet where we're saying, "Hey, let's show all of this to the world." Because, again, I do think the way that you deploy it is also going to matter, right? It's not just that you have the tech. It's here's how we're using it differently than other people, right? We have a predictive rating algorithm. Great. What does that do? Well, it predicts what we're going to end up paying a carrier. Okay. 

Okay. 

There's a lot of things you can do with that, right? And for us, we use that uniquely. The way that we deploy that for our pricing strategies, our procurement strategies for our spot carriers, but also when we are giving kind of 360 views to our customers. We use historical data, their data, our predict trading algorithm to give them this full view of their network up against ours. And that is different than what somebody else is using a pricing algorithm for. 

And going back to kind of MODE specifically outside of just AI in general, what makes MODE Global's partnership or implementation of AI different? Or what makes MODE better at this than anybody else that might be doing this? Because we all know, you're not the only broker that's integrating with some AI answers. What makes MODE worth talking to because it's – just because you got a great smile, and you're out in Chattanooga, Tennessee, and that's where you're probably hanging out if you're a broker anyways. Or what makes MODE Global and AI a perfect fit, like peanut butter and chocolate?

I'll give you a really good statement that I think best encapsulates that. You all were there. I think this last summer, we did an Innovation Summit at our corporate office in Dallas. We brought a bunch of our existing vendors, a couple of potential vendors, and we sat everybody down because we wanted them to see, "Look, this is everything we're working on, everything we're working towards. Here's the things we know we can solve for. Here's the things we want you to help us solve for. Here's the things that we haven't identified anyone to help us solve for, and we're hoping that you guys can come to the table." 

By the end of that three-day summit, one of our partners said, "If we can solve for your problems, then we can solve for any broker's problem in the world because you all have it all." In that, we have the scale of a top 10 brokerage. 

You have the complexity. 

We have the complexity of smaller $100 million, $200 million brokerages. And we have the flexibility of that as well. That's the beauty of it, right? We have corporate offices that have robust networks. We have agent offices that have robust networks that can deploy those solutions in so many different ways. We also happen to be uniquely positioned in the marketplace because of our footprint on over the road, on intermodal, and LTL parcel. Nobody else that doesn't have assets does the scale that we do on all of those. 

And so for us, as we start – we're solving for that. And we think about it both ways, right? On our side, we're the jewel guy that's got the glasses with one lens that's going this way and then one that's this way. And we're having to look at both of those. Look at this one through a magnifying glass. And I have to think about it. Every decision we think about is here's how this works right here. Can this work at scale? Can it not work at scale? It's okay if the answer is no for scale, but is it still valuable enough to put in this niche. 

Right. Is it a legitimate stop-gap until we find the answer for scale? Does it just fit into one bucket? Whether that's LTL or truckload, versus the intermodal, versus the parcel. Hey, it works from two out of three. How do we get to all of them together? Yeah. I can see there being a lot of – it's not like a, "Hey, it's A, B, or C." There's a lot of D, A and B, E. All of those fun ones. You're like, "Oh crap. Why'd you give me so many options? Now I have to think about this." Yeah. 

A and B, F, D. 

I took the LSAT. Do not trigger me again. That was terrible. 

I did as well. That's a story for another time. That was what I was supposed to do with my life. 

You and me both. That's what happens. You don't go into law. You figure. Somehow logistics finds you. Yeah. 

Logic games is the stuff of nightmares without a doubt. I try to explain that to people. Literally, there's nothing like it. Nothing – 

And I think it's relevant here because with AI it. And then some of these options, if you're not careful, it can look like the same answer from three different solutions from three different agents, but one of them is vastly different from the others. But as you were saying, you don't see the background. You just see that final number in front of you. And how do you know if it got lucky getting there or if the math is right without seeing that scratch pad? 

I think, and this is me going on a bit of a rant here, it's still logistics, relationship, and trust that have to be at the core beyond all of the – no matter how good this technology and automation and prediction gets, there still has to be that trust relationship even if it's with the solution that you're using. 

Fully agree. Fully agree. Again, I think that should be what I would consider the good news, right? This is still a relationship business. It's going to be a relationship business. We've got some great tools that are coming out. We've got some tools that are continuing to evolve. How do we use those to just continue to provide – again, we're providing a great service. And we're trying to become – again, I think, the other good news is the right deployment of AI and data will only make us have deeper partnerships with our customers. Now we can legitimately come to a place of a subject matter expert where maybe I do know this better than you now because I've got data from this plethora of sources that isn't just your business. And in my view, you've always won when a customer is coming to you to get insights. 

100%. That is something that I have lived by in my professional life. And something that when I go to look for someone that I need something for, I'm asking them questions, and I want to trust that they're going to give me a no. Not just a, "Yeah, I can do that," and then burn me on the back end. 

With all this conversation about where you are and what your goal is, we'll wrap up here because I've been talking so much, and people only take so much of me at a time. But where does AI and logistics go from here? As I've joked before, AI seems to be this piece that is going to fill in a lot of gaps. But until we get fully automated trucks or teleportation, what's in between AI and those? Because in my mind, in my sci-fi head, those are the next steps. 

Yeah. Look, I still think that the natural order of things will come into play here. And by that, I mean, there's going to be – look, right now, frankly, there's too many providers in the space because everybody's ran to that. They've been able to get VC money or PE money to throw at it. The winners are going to rise to the top. 

I think there'll continue to be some consolidation in that space. We're going to get some of our legacy providers tapping into this. Frankly, that hasn't happened yet, right? Our big providers in pricing and software hasn't – I mean, if it has, I feel like I would have seen that all over – they would be wise to commercialize that. 

Right. I haven't seen Oracle.ai being pushed to the sky-high right now, you know? No, it's a good point. 

Yeah. Kind of what I see as the next evolution of that is these big providers are going to start deploying solutions for that. Some of this stuff is going to become second nature to us. I mean, we forget that it wasn't that long ago that we weren't stuck to an iPhone or any kind of smartphone for eight hours a day. 

My kids don't understand what this this means, because nobody uses this for the phone. My son was like, "Surfs up?" And I'm like, "Oh, God. Oh." But yeah, it's a good point. Yeah, it wasn't that long ago. But yet, it's so quickly jumped from. 

Yeah. Again, I think in the short term, everyone will be using something like a ChatGPT. What will come shortly after that is people will quickly recognize a ChatGPT response versus a human response. The evolution of that will be learning how to create your own style within a generative prompt. 

I hope this email finds you well is something I can't quite get the AI to stop doing by default. Yes. 

Warm regards. 

Humans do not say this. Thank you. 

Yeah. Please don't hesitate to reach out. 

I use that one naturally. You're just calling me out on that one. 

Man, I'm convinced that is a logistic – it somehow just became emboldened within the logistics space. It's only here. But yeah. Anyway, yeah, I think we'll see some of that. I think we'll see some consolidation. And then, look, I think there'll be some things that might not work well. I mean, that happens. 

We're in the trial-and-error phase right now, for sure. 

Yeah. We're going to find that there's good solutions for things that just don't create enough value for the lift. And I think, again, that's what it will come down to, is what will be – to me, what's going to be most interesting. And again, I go back to the why on the market side of things. Who all was ready for this when the market flips again? It will. It never hasn't. When the market flips back and everyone is screaming for capacity again, will the folks that built their infrastructure around this, they're going to be able to 10x their opportunity versus all the other guys that are still having to pick up the phone and just hope, right? 

Again, I think that – man, I think there's just going to be this big gap. Your small providers, they're still going to have their book of business. They're kind of insulated from it already, right? Because that's still very – 

they've already carved out their niche. Yeah. 

Yeah. And then your big guy, it's the guys in the middle who are going to – 

Get either swallowed up whole or learn how to swim at a higher level. Yeah. 

Fast. They'll have to, right? Because the big guys will have already figured out how to do that at scale. And look, to me, that becomes – when you start seeing the Amazons, the Walmarts, right? At scale, the number becomes so hard to compete with. The economy of scale. What I'll be able to accomplish when I do have a customer who needs 30 shipments covered? Well, I'm doing that faster than a person could ever do that. Look, those are things we're doing now, right? There are things that we do already that one person could never do in a normal day by them self.

Right? In closing here, and that's a great piece right there is it's not just on the horizon. It's here right now, and it's being used. And again, kind of if you're not using it, start. And if you're not going to start, then look out. But what is your, and I always offer this, closing statement? Whether that's from Jason, whether that's from Jason, the SVP of Digital Enablement at MODE Global? Here's a little soap box here. What do you have to say to any and all of the listeners or poor desperate souls that got locked onto this and can't get their screen to change off? 

Yeah, I guess in closing on my side, I think that AI is exciting. I think it's something to embrace, not to be afraid of. I don't think that it is here to replace jobs. I think that it is here to enhance jobs. Now, look, if your only job is doing something very menial, then I'd start looking at how do I educate myself to do more. The other piece is I would be educating myself on this no matter what, because it is coming. It will be a part of our daily lives. The sooner that somebody adopts that, look, they're going to have clear advantages against their peers. And again, it behooves us to do that right. 

And AI or not, in my view, the folks that always separate themselves in this industry are the ones who are regularly educating themselves on the up and coming, right? That happens to be AI right now. That's where we should be focusing our extra energy, our nightly evenings, whatever that looks like, right? 

Yeah. No, it's a great call out. And Jason, thank you so much for all the insight and a little bit of a peek into what you're doing with MODE Global. And like I said, we're in this miniseries of how AI is impacting freight procurement. And this is really insightful. Thank you very much, Jason. I really appreciate it. 

Of course, I appreciate being on. And thanks again, as always, Patrick. 

Hey, and for everybody out there, thanks for watching. If you can give me a like, engage with us in any way, or I don't know if you can subscribe. I'm not that hip. But this has been Patrick Escolas with another Banyan Technology's Tire tracks. And specifically, we were doing our minieries on the AI impact in freight procurement. Thanks, everybody. We'll see you on the next one. And bye, again, Jason. And thanks again. Have a great one. 

Thanks. Bye.