How Envoy AI Is Helping Brokerages Take Action on Freight Data

In this episode of Everything is Logistics, Blythe talks with Michelle McBride, Head of Product at Envoy AI, about how AI is being used inside freight brokerages to reduce operational drag and help carrier sales teams move faster.

Envoy AI builds Ellie, an AI-driven orchestration layer for logistics operations. Ellie connects into the tools brokerages already use, including TMS, compliance platforms, load boards, and communication workflows, so reps can take action without jumping between several different screens.

They cover:

• Why carrier sales reps are still dealing with too many disconnected systems
• How Ellie helps with carrier outreach, compliance, load coverage, and exception handling
• Why master data and SOPs matter before rolling out AI
• How brokerages should think about change management during implementation
• Why trust is one of the biggest adoption challenges for AI in freight
• How human-in-the-loop workflows should have a clear path toward autonomy
• Why AI in freight is not about humans versus AI

This conversation is part of the CargoRex AI Use Cases in Logistics guide, featuring real examples of how logistics companies are using AI across freight, warehousing, procurement, visibility, and operations.

Read the full guide here:
https://cargorex.io/research/ai-use-cases-in-logistics/

LINKS:

Envoy AI:
https://tryonvoy.ai

CargoRex AI Use Cases in Logistics Guide:
https://cargorex.io/research/ai-use-cases-in-logistics/

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Transcript

SPEAKER_00 0:05

In my experience, I can't speak out for everybody, but I often find that the pushback isn't about the technology itself. The pushback is about the trust and the uncertainty of what it's going to do for the roles that essentially are getting asked to use this technology. Some of the experiences just in the time that I've been with some of our customers is really figuring out is this going to create more work, right? Is it, can I trust it? Is it going to know what to do? There's a lot of very valid questions. When these questions surface at the start of a project, let's say fast forward two weeks, three weeks, and you see the smiles on their face. You see the aha moment of, hey, you know, you told me, but I really wasn't sure. As a product leader, that's probably one of the most exciting parts of it.

SPEAKER_02 0:58

Welcome in to our Cargo Rex series on AI use cases in logistics. We've got another great conversation for you today. And we are talking with Michelle McBride. She is the head of product over at Envoy AI, where they are helping logistics service providers not just look at dashboards with a bunch of data, but take action on that data. So, Michelle, excited to have this conversation.

SPEAKER_00 1:20

I am so happy to be here. Thank you for having me.

SPEAKER_02 1:23

Absolutely. Now, now for we were talking before we hit record on your extensive experience within the industry. 16 years at Uber Freight. And then after 16 years, what made you take the leap over to Envoy?

SPEAKER_00 1:38

Great question. So, yeah, you know, spend 16 years in transportation management, implementation, solution designing, all of the above you can think of. And, you know, the opportunity where we're sitting at right now as it relates to what the industry is shifting to, the transition of how to perform at a faster scale, how to get fast, how to get more intelligent with the way that you do operations really excited me. So when I had an opportunity to meet Robert Mason at Envoy, it was just, it was awesome. It got me excited. The vision, but also the problems that we're looking to solve through the usage of this tag was really, really exciting for me. And yeah, here I am. I'm currently responsible for product strategy, uh, the product roadmap behind our application Ling, which we'll get into here in just a minute. Um, but yeah, our focus is just to ensure that we're able to reorchestrate the way operations get done. And so let's talk about from a high level, what does Envoy do? Yeah, absolutely. So we are think of think of Envoy as the orchestration layer, the intelligent orchestration layer for logistics. When you look at the workflow of a carrier cell screp or somebody sitting out on the logistics floor, some of the most common challenges are how do I get this load booked? How do I negotiate it? How do I price it? How do I make sure that I am using the right carrier? How do I keep up with exceptions? And I think traditionally, having been in the industry for 16 years, we have encountered an evolution of how those tests got done, right? In the past, early times when I was in operations, it would be phone call, it would be email. Uh, then we started to get into the era of EDI and APIs. And we started to see more digitization on how we communicated with carriers or other providers in the industry. Here with Envoy, as you think about it, we are essentially orchestrating a multiple uh tech stacks or multiple systems that users have already today, and we're essentially adding intelligence on top of it to help execute some of these tasks in a much more automated fashion. So it's really looking at it from an order to cash perspective and what are the various workflows that LA taps into. But yeah, ultimately what we're trying to establish and trying to fix is the operational, but taking away the operational pain in the operational burden into more of a scalable AI-driven workflow for operations.

SPEAKER_02 4:08

And so is that operations inside of a brokerage, inside of a you know a large carrier? What does that, I guess, sort of customer makeup look like?

SPEAKER_00 4:16

Great question. So right now, our current focus has been with the with the brokerage industry, um, really providing support to or providing this technology to the carrier sales rep. So those that are focusing on getting the loads covered, getting the freight moved, the brokerage, you know, managers, supervisors who are really looking for intelligence to operate their business better, right? So it it levels up to through our analytics and through different layers of information at that level. But if you think about it, this technology also can apply across other verticals as well. Companies that are running automated business processes or BPOs type type of environments where they are currently sitting with repetition and task. The only way to scale is to add additional labor into their into their operation. This technology essentially has the ability to get embedded in the ecosystems that they're operating on today and help them execute at a faster and better scale.

SPEAKER_02 5:15

I want to go back to the the carrier sales role because I'm curious what are some of those maybe uh workflows or processes that they are dealing with today and and where are they kind of struggling where Ellie fits in?

SPEAKER_00 5:30

Yeah, a great question. So if you think about it, let's first put our hat on of the carrier rep. How do we reduce the operational burden, the drag, right? Finding carriers, verifying compliance for those carriers, managing the outreach. We know today that not everybody's at the same level. Some carriers in terms of outreach are going to be email-based. Others to this day and age are also phone calls. Then you have the ones that are leveraging WhatsApps or other um methods of communication. So managing the outreach, the management or the handling of repetitive follow-ups. Hey, are you able to pick up? Hey, did you arrive? Hey, what's your ETA? Things that are just fundamentally critical to the execution of freight that happen all the time. There's no there's no secret sauce on this, but it's just really the method of how we empower and how we up-level through the usage of products like Ellie to get that, get those functions executed. It's essentially removing that friction. And then at the same time, through these stages that I just described, you run into the um into a situation where many of these reps have five to six to seven different screens where they're having to do these functions across a group of multiple uh tools that they already exist. Ellie brings all that together.

SPEAKER_02 6:53

And so what does that look like? Is it maybe like a dashboard that you manage? Is it you know an integration into their existing TMS? How do you how do you interact with Ellie?

SPEAKER_00 7:04

Yeah, so think of it as a browser-based extension. So what it does is the way that it operates is essentially our users will have an extension that is downloaded into Chrome or to wherever browser of their choice. And the back end of Ellie essentially is queued up to be able to interface with the TMS. It's able to integrate and interface with compliance software, some of the, to name a few, some of the ones in the market, highway, my carrier packets with from Descartes, from a posting and um and negotiation layer. You also have integrations with companies like DAT. Um, so yeah, through this extension, essentially it's connecting an ecosystem and creating this infrastructure where the carrier rep doesn't have to take advantage or use all seven screens to do and essentially perform each of these steps.

SPEAKER_02 7:56

And so it's really bringing in together all of those different because I I think if I take a step back a little bit for a lot of brokerages, there's been just an onslaught of tech, you know, being introduced to different brokerages and teams over the last decade. And maybe some have overinvested in in certain areas, and now they're trying to figure out how to make sense of it all. And that's what really drew me to the you know, the opening line of how to not just deal with data and dashboards, but how to take action. So once you're you're you're in the the system with Ellie and it's tell it's getting rid of some of the tabs. What I guess how are you interacting with the platform outside of that? Or is it streamlining that part of the process? And then it kind of frees you up to have you know more direct conversations or source, you know, better carriers. Uh what I guess are maybe are the perks.

SPEAKER_00 8:50

Yeah, great question. And and I I want to start first with something that you just stated, because I spoke about it earlier about the transformation that we've been through in in freight operations, right? First, you're spot on with the the gaps were how do I get data, right? Where where do I source it? We all experience the area of digitization. And you know, now we're sitting absolutely like you just mentioned, what do I do with it? How do I execute better? How do I take away so much fragmentation? Tooling, accessibility to tooling today is not a problem, is not the problem. Many great products out there, great options for brokerage reps to be using um, you know, at any other level to be taking advantage of, but it's how you go about the execution of that sets of tools that you have that are really going to make you become more efficient in your daily operations. So when you think about it with Ellie, the idea is that by being embedded into the workflow and removing the fragmentation that today already exists across these dispersed set of tools, it's essentially being able to orchestrate, right? If we were at a symphony, it's the it's the conductor, it's orchestrating all of those actions together instead of the rep having to manually, you know, switch between systems to perform these functions. Now, when reps do that, because let's be real, that's part of what they've done today. That's how they've been doing operations. You have to think about the impact of time and the impact of efficiency that tools like Ellie surface out on ops for.

SPEAKER_02 10:26

So is it, I guess, uh kind of like an LLM, or is it maybe like uh, I don't know, like an analytics tool, or is it maybe a combination of the two?

SPEAKER_00 10:36

It's a combination. So as you think about the the DNA of Ellie, right? It is a AI-driven orchestrate. So there's three layers to it. There's an orchestration layer that happens, which allows the functions and the different tools to come together and execute against those tasks. Today, there's a lot of tooling out there that exists. Well, you'll see, hey, we could automate your track and trace. That's a specific workflow. Hey, we could automate your carrier verification, that's a specific workflow. So at the top, as you're building out the architecture of Ellie, there is a layer that takes place of task orchestration. The second level of Ellie's DNA is the semantic layer, which is power through LMM. Um, and essentially it's compute compounding value over time. Every action that is that is occurring on behalf of the user within the tool, Ellie learns from it, right? So next time you have a load that you have to cover from Chicago to Dallas, as it's providing outputs back to the user through the outreach workflows that we have on which carriers are the good ones, not only the carriers that have capacity, but the carriers that can cover the load right there and then, it also is able to provide the users with intelligence of a couple of things. One, exclusion, which are the carriers that maybe have had bad service on that lane in recent history, which are uh carriers that perhaps have not been approved through the most recent verification changes. So it is it has the ability to learn from the functions that a person has taken, but also on its own is able to learn over time. So that's where you start to see some of the semantic AI layer technology and training of these of these models become very powerful. One of the things that we are really focusing on right now is building out not just the operational memory layer, but also how we're incorporating the feedback loops to retrain, to retrain these models over time through each through each action that happens.

SPEAKER_02 12:34

Is that retraining at the at maybe the at the Envoy level or the LE level? Or is it maybe at the company level or kind of a mix of two?

SPEAKER_00 12:45

You know, it's it's a mix of two, right? It's a particularly specific, you know, and most specifically when you're looking at load level detail, lane level detail. So that is to to the training of functions of our customers, right? Because you're gonna have users where they're gonna have different uh guardrails and different methods of how they manage the carriers that are approved within their network, what what exceptions are deemed to be exceptions, right? That's also very common in operations. You're not gonna have identical SOPs across our customer base. So Ellie has to ensure that not only it learns over time, but is able to execute against those SOPs that are unique to the customer base that we are servicing. For us, where we're also learning and what where Ellie, from a from a um technology perspective, is also ensuring things like speed, right? It learns behaviors and patterns of how we get faster. If today it took, you know, 1.8 minutes to conduct this outreach to get a carry on the load, how what actions do we need to take? Or in this case, it observes what actions a user may take to be able to continue to get better over time.

SPEAKER_02 13:52

Within a brokerage, are there other use cases or other departments that are are finding value in LE or Envoy?

SPEAKER_00 14:01

So in current state, we've actually been having a lot of conversations with leaders at brokerages, particularly in the area of compliance. You know, there's a lot out in the industry that is solid and provides really good solutions in terms of verifications and how to keep up, you know, with your hundreds and hundreds of rows of databases, of carriers, documentations, how to go about onboarding, et cetera. But one of the things that we're trying to do is change the or bring the conversation to the table of how do you ensure that all this work and all this investment that you're doing through these platforms gets executed against, right? So that is one interesting piece for us that we're seeing in the evolution of ruling this out, where originally you could say the thesis was on the ops floor, the impact that this has with operational efficiency and productivity. We're taking it, we're continuing to evolve that conversation and having those dialogues with the compliance teams. The compliance teams are now looking at it and saying, wait a minute, this is a really good way of being able to one, drive continuity and how operations is getting executed, but two, it's able to provide a higher level of confidence that if they have carriers, let's say, marked in a uh do-not-use bucket, right, that they're not going to be going, you know, pursued after just for the sake of covering a load. So I find that very interesting.

SPEAKER_02 15:25

And so from I I guess um an onboarding perspective, you know, for a lot of companies, I would imagine that they're dealing with, you know, messy data or unorganized. You know, they they talk about how you know dirty data is expensive. And I I'm curious as to what a brokerage could do to maybe start cleaning up some of that data so that they can get themselves prepared to start taking advantage of tools like Envoy and Ellie.

SPEAKER_00 15:51

Yeah. So some of the things as as you know, prospects or our brokerages are evaluating implementation of technologies such as ours, some of the areas to look at for sure, you know, evaluating how clean and how current the data set is. We all know that what happened, you know, in in our industry 30, 30 days ago may already be stalled. So what are a lot of this has to do with using these types of technologies also as an opportunity to establish, renew, or create operating procedures at these companies. So one of the first areas is what is your what is your audit timelines with your carriers in regards to compliance? Number two, what are the different thresholds and customer level information that you have built into your TMS? Some of these things could be special, uh, you know, special instructions, special requirements that key customers may have that impact the way that a load is getting booked. And we start to see this, you know, master data is such an important part of my career, also in transportation management, but it's it's also very relevant where I'm sitting today because if you think about it, the knowledge of I'm gonna ship to the, you know, I'm shipping this from my key customer, they're gonna need X, Y, and Z. That lives in people's heads. To be able to train models like these to understand and to be able to perform in the same function that a human would, you have to make sure that the master data and core systems like TMSs are up to date. So we do a lot of conversation ahead of a launch or you know, even before a full implementation happens, where we sit and go through a framework of master data across compliance, operations, track and trace. And then we also ensure that working with the operators, if there's different guardrails or SOPs that are need to be implemented, that we also are aware of what those are.

SPEAKER_02 17:51

Yeah, because I I think I heard uh you know recently, and I correct me if I'm wrong, but you know, in order to make sure that you have proper clean data, not just you know, from all uh gathering it in and initially setting that up, but also making sure that almost like if this, not a if this then that, but this equals, you know, this column title is the same thing as this column title and this spreadsheet, and just making sure that those two things match and what uh the carrier is is quantifying in a certain column is the same thing that the finance department wants to quantify in another, you know, spreadsheet report that they might be looking at. But the bare minimum is that those columns and that that data is labeled the same thing. Am I understanding that correctly?

SPEAKER_00 18:36

So the the yeah, the data normalization is is, I think, a key to success, right? You need to make sure that you, you know, brokerage leaders or any leaders that are evaluating how to implement and adopt these types of technologies, that they also understand that, yes, they're very easy to deploy. That, you know, the the timeline really varies on when they want this live, but to do it right does take some pre-work, right? That's where we want to make sure our customers understand this is not a rollout for the sake of rollout. We want to make sure that as part of these deployments, the conversations are happening with finance, with compliance, with your leaders across different uh shared service groups like track and trace. There's also a big piece of this that not a lot of people are talking about, which is the change management side of it, right? Getting the technology right, getting the maps right, getting the data normalized, all of that is important. It all will fail if there's no change management strategies that are discussed at the table and not just with your key stakeholder that is looking to bring this in, but cross-functionally at corporations to ensure that everybody buys in not only into the outcome and the success that this is going to bring from a tactical operational side, but also what it's gonna do for them as a company.

SPEAKER_02 19:55

Yeah, that that makes a ton of sense. It's just getting everybody on board and speaking the same language is sometimes the hardest part of the entire process, but you can't get to that end goal and you can't get to those actionable insights unless everybody is is rowing together in in the same direction. Uh I'm curious from the I guess the psychological level of it where you know there's a lot of fear around AI. And that that's one thing that I'm discovering in a lot of these conversations is like, yes, that fear exists, but there's also an incredible amount of demand for people that need help with this massive amount of data, these massive, you know, tech not or I guess tech budgets that are, you know, purchasing all of these different tools. How to how to figure out where to spend your time and energy? And so creating that level of clarity. So when you're you're first implementing and and or first onboarding a client, how important is it to have you know those carrier sales reps, the the in-the-trenches employees who are gonna buy into it and not push back against some of this change management that's required of them?

SPEAKER_00 21:01

Yeah. Um, very, very interesting question um because it's very much real. The pushback in my experience, I can't speak up for everybody, but I often find that the pushback isn't about the technology itself. The pushback is about the trust and the uncertainty of what it's going to do for the roles that essentially are getting asked to use this technology. And some of the some of the experiences just in in the time that I've been with some of our customers is really figuring out is this gonna create more work, right? Is it can I trust it? Is it gonna know what to do without me having to tell it to what to do? Uh, what happens if there's a mistake? Who's gonna be held accountable? So there's a lot of very valid questions that take place when you start the conversations, which I love and I find fascinating because what I love to see, and I think you know, you look at our team, and what we're most proud of is When these questions surface at the start of a project, let's say fast forward two weeks, three weeks, you know, as an example, and you see the smiles on their face, you see the aha moment of, hey, you know, you told me, but I really wasn't sure. As a product leader, that's probably one of the most exciting parts of it. The success of this is to be very intentional in how you're rolling it out. So the conversations that we have with our executive sponsors and our leaders that are evaluating the investment, it's also ensuring that their team is set up for success by being intentional in the rollout. Anybody that's coming with these types of projects or tools that say, hey, you know, we could, we're turning it on and we're turning it off for everything, that's just a miserable, you know, way to allow the people that are impacted by these changes to fail because it's overwhelming, it's it's new, it's not calibrated. So for us, the way we approach it is ensure that we're intentional about what's the workflow that we're gonna be addressing, then what happens next, and then mutually, how do we define success? And I think where we have seen a big shift in the willingness to adopt these tools is when we're able to sit down with the folks that are gonna be held accountable to use it and also ask, what do they care about? What's success to them look like, and being able to prove with the real results that they're getting those outcomes.

SPEAKER_02 23:27

Oh, that that's super interesting. And so as you know, the the employees are starting to use the platform and they're they're finding success with it, what happens from I I guess the the training part of it, the feedback part of it, because there is you know a lot of concern around, you know, well, what if it hallucinates and what if it you know it gets something wrong? And you know, uh what does the process look like then? Is it their team letting you know and then you you you fix it in the system, or is it maybe like a root cause issue where there's you know something is wrong with their data? What does that process look like?

SPEAKER_00 24:01

Yeah, so our our particular I'll tell I'll talk to you a little bit about our specific process because everybody may be approaching this um differently. The first thing is during our implement our our implementation framework, we spend a lot of time preparing for production deployment. So there's already a level of security and assurance of what the product experience is going to look like. So we do go through various user testing stages um but prior prior to full rollout. Once it's rolled out, we also ensure that there are resourcing that is allocated to monitor not only the activity that is happening, but also that we have well-defined plans if for whatever reason we need to we we need to shift our approach. And that shift would be turning it off in production. We recognize the severity of impacting freight in production. So we've established protocols where we can, if we need to up to this point, knock on wood. We would not have to do that, but if we needed to pull back, we can. Now, what is a day in a life and experience look like? Think of week one of your goal life. You're gonna have somebody that is on the floor with your team members, not only being there for training or also for any type of potential troubleshooting that may come on. We have a support team that is also, you know, on standby, waiting to monitor any defects or any issues that may surface. But strategically, the way that we approach this is we're doing customer engagement sessions throughout the hyper care period of the goal life, where we're working with our stakeholders, not only providing them with metrics of what we're seeing, how the product's behaving, um, you know, some of the outcomes that we go in with is, you know, how many loads have been covered, how many, how many offers have been accepted, how many, for example, the the the time to cover, some of those metrics where they're seeing week one, week two metrics. So it helps them get confidence. Now, you mentioned something about is it, is it, you know, are the users able to provide your feedback? Absolutely. For us, we believe that the people that use it are the ones that are going to give you the best source of feedback. So we have established channels where they could email us and say, hey, you know, today I was working on this and I wish Ellie could do this. Or hey, Michelle, you know, I was working on this and I wasn't really sure about this button. Of course, hey, Michelle, it doesn't come to me, it goes to our support group. But things like that where you'd be surprised through those feedback channels, we get some really exciting ideas that we can even incorporate into a broader roadmap.

SPEAKER_02 26:33

That yeah, I think that that's where, personally speaking, that that's where I find the biggest issue with, you know, uh ever on the large language models is that it allows me to do so much. So now focus becomes the the primary objective instead of being pulled in a million different directions because I can do so much more. And so I I I'm curious from the I guess the the setup of an ideal brokerage. Is there one person in charge of sort of AI operations, or is it maybe a new department that that's forming within the organization, or is it the traditional employee, traditional departments, but they have that sort of AI superpower added onto them?

SPEAKER_00 27:14

I wouldn't say that it has to be somebody that is focused. We are seeing where there are uh companies that are designating you know special projects, uh liaisons to take on projects like these, which I think is great because they're focused. They're, you know, it's that part, they don't have to choose between rolling out AI and covering freight, right? Because that's also could be risky if they're not um focused on the deployment and the implementation and deployment of the of the technology. But in the case that they don't have the benefit of these resources, that's where we come in, right? We're able to be their um their point of contact. We're able to help them with strategy, not only in change management, but also how to how to implement it. And you know, you have to make sure that we also establish clear protocols of, and you've heard this before, our strategies of human in the loop. At the end of the day, the end goal is to be able to drive type plus automation, right? Be able to trust that that the tool is performing where it needs to be to be fully autonomous. It takes time to get there, right? And up to this point, we need to make sure that there is buy-in on how for the language for the language model, what feedback we need from the humans, right? To be able to train. And we talked about semantically, or we talked about how it observes those phases in these types of projects become very important because then you also have to make sure you define it when the human in the loop ends and it falls to full uh autonomy.

SPEAKER_02 28:43

Yeah, I I think that that's a really great point because it's it's one thing to adopt these technologies, but it's it's a completely different other to not, I guess, ignore it or use all of the features. Because I think, you know, for a modern uh SAS product, it's something crazy where only like 20% of the features are actually utilized, and that's a good case scenario. Um I think you know, getting them on boarded, getting them those early light bulb moments, I think that that that's really key. And I for a lot of companies, it might be really hard for them to document, initially document their processes where they they may have never done this before. And, you know, thinking about it in a different lens because of this, you know, XYZ reasons why. And so I think that that that's a challenge too for a lot of companies. And I'm I'm curious if if that is a prevalent issue, or maybe it's you know, kind of case by case.

SPEAKER_00 29:34

No, I think it's you know, it's a it's a case by case. It also you but it's all gonna be dependent on the mature and the operational maturity that some of these companies have, right? And the the key is to be able to be flex as a as a um technology provider, to be able to be flexible in any of those environments, right? Being able to have the right strategy and frameworks to support companies that have their data up to date, have all the SOPs documented, understand each and every single one of their customer profiles. But just as important to walk in the door with somebody that says, I haven't looked at this and you know, I need a partner. Who do you guys partner with? Who can help me improve my compliance? Who can help me, you know, create a standard for track and trades, like things like that, where the fact that, you know, when you look at the team at Envoy, and one of the reasons that also drove me to move in is these are folks that have done the job. I'm I'm sitting peers of mine, uh, partners and team members that I can have a conversation as we're thinking product strategy that bring the lens of what it was like many years ago for them to do the job. And I think that also changes. And when we have those conversations where we present those frameworks, it's not just tech-based, right? It's really getting into the weeds almost like we're speaking their language because many of those have done the job. And that also adds to a greater level of credibility of saying, okay, these guys are not, you know, understand what it's like to jeopardize and miss pickup, right? They're not gonna come in here and mess me up because their tool is not gonna work. So that's just a little extra to add because I do think that it that it changes the conversation. And at the end of the day, for us and and as we look at our product, everything that we're doing, we understand one, relationships matter. Two, you can't mess up freight. You have to, one bad load could could could jeopardize the relationship with the shipper. And we don't want our brokerage partners to go through that, right? So that's why we're very intentional, very cautious in how we roll it out. If a customer comes in and you know wants to have a very aggressive timeline of how to deploy this, we're gonna be the ones that are gonna say, hey, listen, have you thought about this other angle for the sake of your operation? And that's where we move from just being another provider to becoming a partner in their journey of AI.

SPEAKER_02 31:49

Yeah, I I think that that that's a a really brilliant way to I got a couple more questions for you, but I think that that's a perfect way to kind of, you know, come to the the finish line of the of this conversation because there's so many tech companies that are seeing transportation and logistics, and they think that it's such a, oh, well, we can just go in there and disrupt it and you know, uh become a unicorn business. And it's it's you gotta have people who speak the language who know how critical different aspects of the transportation process is. And it sounds like you know you you guys have really built you know a strong team over there. And I'm curious, um, I mentioned last couple questions. Is it are you focused mainly on enterprise you know, brokerages right now, or maybe mid-size? Is there room for maybe you know some SMBs to kind of take advantage of this? Or is that maybe in the product roadmap?

SPEAKER_00 32:37

So right now, you know, mid-size enterprise is is really, you know, we have we have a brilliant team, a commercial team that is looking at all the different angles, right, to be able to tackle. And that's as a product leader, the cool part of my job, right? That my product doesn't have to be isolated to just one user journey and being able to plug and play where our customers need. That's that's the exciting part.

SPEAKER_02 33:03

All right, Michelle. What is one thing that you think is important to mention that we haven't already talked about? The one thing, man, that's a hard one.

SPEAKER_00 33:11

Um, for your audience and your viewers, this is not about humans versus AI. And I know that sounds a little nerdy of me to say, but I mean it from the heart. The future is not humans versus AI. It's really around what operational teams can do with AI and how AI can become a competitive advantage in the market when they're servicing, whether you're servicing a shipper, whether you're a BPO that's servicing a logistics provider, um, or you're just a broker too in your own, right? It's about how you could use these technologies to help you outperform your other call, your other buddy.

SPEAKER_02 33:53

No, I I love it. I I am uh a tech optimist, and so I I agree. And that was one of the things with coming into this series. I just wanted to uncover is is are people actually scared, or is that what I'm hearing in the news? And I think it's the former rather than the latter. So really, really appreciate your your time and your your expertise and sharing it with the audience today. Where can folks connect with you? Uh, book a demo with with Envoy and Ellie and and all that good stuff.

SPEAKER_00 34:21

Yes, come meet Ellie. We want to hear from you guys. Our website, um, tryonvoy.ai. And then um, of course, reach me out, reach out out to me on LinkedIn if you have questions, if you just want to challenge some of the points that that they heard about today, I'm always up for a healthy debate. And uh, we have a great team of experts as well, right? I uh I don't tend to be the AI know it all. Um, 16 years does give me some credibility. So I'm excited to have the conversation and help address some questions on change management. It's not just about our specific tech, but if there are people that are starting to think, you know, whether you're in the early journey of sourcing a technology product or you're in the middle of figuring out how to best implement it, we're an open book and we're happy to help.

SPEAKER_02 35:07

That is a perfect place to end it. So, Michelle, thank you so much. Thank you. Appreciate it.

SPEAKER_01 35:17

Thanks for tuning in to another episode of Everything Is Logistics where we talk all things supply chain for the thinkers in freight. If you like this episode, there's plenty more where that came from. Be sure to follow or subscribe on your favorite podcast app so you never miss a conversation. The show is also available in video format over on YouTube just by searching Everything is Logistics. And if you're working in freight logistics or supply chain marketing, check out my company Digital Dispatch. We help you build smarter websites and marketing systems that actually drive results, not just vanity metrics. Additionally, if you're trying to find the right freight tech tools or partners without getting buried in buzzwords, head on over to CargoRex.io where we're building the largest database of logistics services and solutions. All the links you need are in the show notes. I'll catch you in the next episode and go Jags.