Hi, everybody. I'm Bob Bowman, Editor-in-Chief of SupplyChainBrain. Welcome to this special presentation, The AI Exchange: Inside the Last Mile, AI, Delivery Engagement, and the New Standard for On Time and In Full. First of four webinars on the theme of The AI Exchange to be presented by Descartes this year. Quick reminder, folks, there will be an audience question and answer session at the end of this presentation. Audience members are encouraged to submit their questions at any time during this presentation by clicking on that Q&A icon at the bottom of your screen. We encourage you to do so. What if your routes could learn from every delivery? In this AI Exchange session today, we're going to explore how moving beyond static service time assumptions leads to a new level of fleet performance. Traditional routing treats each stop as predictable, but in reality, each one is shaped by order size, product mix, site conditions, unloading requirements, and crew readiness. AI and machine learning change the model by learning from actual delivery behavior and continuously applying that intelligence to future routes. Let's see what our experts have to say about it. I'd like to introduce our speakers for today. Sergio Torres is Senior Vice President of Product Management with Descartes. He's responsible for managing strategy and vision for the company's entire portfolio of routing mobile and telematics solutions. Prior to joining Descartes, Sergio worked as a director of business development and consulting in Europe for CAPS Logistics. Cyndi Brandt is Vice President, Fleet Solutions with Descartes. Cyndi has deep expertise across product management, product marketing, sales enablement, solutions engineering, and marketing communications. At Descartes, she leads strategic efforts to bridge customer needs with innovative solutions for last- and final-mile operations. Welcome to both of you. Welcome, audience. With that, I'd like to turn it over to Cyndi for a brief presentation. Cyndi, take it away. Thank you, Bob, for that great introduction for both of us. You know, at Descartes, we're all about technology that moves the world. We're a global leader in logistics technology that really helps over about 26,000 customers completely around the world help get things from point A to point B. We wanna help do that in a smarter, faster, more efficient way. For me, it doesn't matter whether it's raw materials heading to a factory or finished products that land at your door. Our technology's behind the scenes making all of that happen seamlessly. At the very, very heart of it all, we are a talented team of 2,200+ logistics pros who love, love solving tough problems and working side by side with our customers to help them succeed. Lastly, as a company, we've had about 20 straight years of record performance. We consistently reinvest about 15% of our revenue back into R&D because we wanna keep that innovation flowing. In our particular pillar, which is Fleet Solutions, you know, we have a delivery and performance management platform that optimizes wholesale distributors to optimize routes, streamline dispatch, execute daily operations with precision and confidence. We do that while making sure that there's real-time driver and customer engagement happening out in the field as well. We wanna make sure that we're capturing valuable feedback, fuel continuous improvement, and power that improvement through AI. It's a great topic for us to talk about today. The way I look at it is every route, every driver, every delivery, it's optimized, connected, and built to exceed expectations. Thank you, Cyndi, and thank you both for that wonderful introduction. Artificial intelligence at Descartes, this is a good representation of what it is. Look, it starts with a very simple idea, you know, using data to make smarter decisions and automate complex logistics workflows. At the broadest level here in this picture, you can see AI is the overall field. Within that, we have machine learning that focuses more on systems that learn from data, improve over time. This is where we start to see the real operational impact, especially in areas like route optimization, real-time optimization, and continuously improve ETA predictions. We'll be talking about more during this session. Going deeper, deep learning uses neural networks to handle more complex patterns. This enables capabilities like OCR, image recognition, and predictive modeling, turning unstructured data into actionable insights. On top of that, you have generative AI that introduces a new layer of value. It can create content, summarize information for us, and power conversational experience. You know, at Descartes, this shows up in things like text classification, customer insights, and chatbot interactions that enhance our user engagement. Finally, you see at the very bottom agentic AI which represents the next evolution. These are autonomous systems that don't just analyze. They actually observe, plan, and act. The idea here is to we will be orchestrating multiple tools and leveraging GenAI that they can execute workflows end to end with minimal human intervention. As you can see here, the power of this is basically the data that we are collecting. Combining all this comprehensive data, name it, route planning, route execution again, how the drivers are actually doing when they are delivering a service to your customers, driver safety or driver behavior when they are actually driving a truck and we wanna know if they are actually doing harsh braking or speeding, sudden acceleration. All this data combined in something we call the Fleet Data Intelligence actually allows us to provide data insights to our customers, provide analytics, and also provide GenAI agents. This is important because the GenAI agents is what makes it actually the smart part of it. It actually looks into insights, summarizes. It actually suggests and also it provides the ability to execute tasks. We're kind of closing the loop, and we call that René. From Fleet Data Intelligence, basically getting all that data, closing the loop to make improvements. Think about this example. If you're actually receiving your planning and your execution data, and you wanna see how your plan compares to your actual execution data, René will be looking into your data to figure out where are the opportunities for, to improve efficiencies and to improve the utilization of your fleet. René will not only say, "By the way, it looks like you're actually overestimating your service time durations. You should probably look into that. Would you like me to do that for you?" That will basically enable and René to activate machine learning for service time predictions, and that is executing an automated task for you. At the same time, to be able to say, "Now, I want you to utilize these service time predictions at the moment of the optimization of a route." That will trigger the configuration of your route planning solution to start consuming the service time predictions, and that is closing the loop. Little by little, closing that gap between planning and execution. Thank you very much, Sergio, and bring Cyndi Brandt back at this point as well. We now move into the panel discussion portion of our presentation in which I have the privilege of asking Sergio and Cyndi Brandt to help us dive deeper into this whole question of AI. Audience, do remember that we're gonna be leaving room at the end for an audience Q&A, so please do be submitting your questions all along by clicking on that Q&A button at the bottom of your screen. Let's start out here, guys. Let's start with kind of a discussion of AI in real- world delivery. Cyndi Brandt, you know, AI is becoming embedded in day-to-day delivery operations, not just long-term strategy. What do you see as the most immediate challenges that fleet leaders face today, and how have those pressures evolved over, say, past two or three years as expectations for on- time and in- full delivery continue to rise? Sure. I mean, that's a really interesting question because, you know, what we're seeing right now is that AI is really landing kind of right in the middle of some very, very real operational pressures, right? Not just these long-term transformational goals. People can't wait that long to kinda have real impact. You know, I, when I look at what every fleet leader is dealing with today, it's just a really tough combination, right? Rising costs, unplanned rising costs at an exponentially fast rate. A lot of labor challenges right now. Customer expectations continue to change every single day. We see, you know, over the last couple of years and even the last couple of months, right, fuel, insurance, equipment, wages, everything's increased. These inefficiencies now are just really starting to double up. Some little things that might have gone unnoticed, they're now really hitting your margins almost immediately. At the same time, expectations around delivery have completely changed again, right? You know, two or three years ago, customers were okay with a nice broad time window. Today, because of the B2B expectations or B2C expectations that have been set rather, they expect precise, super accurate ETAs, a lot of real-time visibility to what's happening with their order, and a seamless experience, right? Again, they want it to mirror that home delivery they're getting from a retailer. I think the biggest true shift is, you know, it's how last mile delivery is starting to be perceived, right? It's no longer just a function. Get the trucks out the door, make the customers happy, right? Not just a simple logistics function. It's become a mission critical part of the customer experience as well as the cost structure of the business. You know, it's forcing different fleet operators to really try to strike a better balance, if you will, between efficiency and service quality than they've ever had to look at before. You remember two or three years ago, we said things like, "Customer demand is greater than ever before." Little did we know. Yeah ... what it was gonna be like today. You really gotta step up, and AI is just absolutely essential to that, capability, obviously. Let me ask you to drill down a little bit further, Cyndi. You know, many fleets, they're still experiencing a 10%-20% gap between planned routes and actual execution. Again, this real world kinda thing. Where are these breakdowns happening most often, and what does that mean for service reliability and customer experience? Sure. I mean, that's still a pretty big gap, right? Yeah ... you know, we could all do better and pull it tighter. We do consistently see that 10% - 20%. To us, it really comes down to the planning system is still operating on assumptions. The real world is more dynamic and far more precise, right? The breakdown really tends to happen when there's a disconnect between what was planned based on all those assumptions, right, those educated guesses, and what actually unfolds in real time on the road. You know, think about traffic variability, service time inconsistencies, service time estimates, blocked docks, even last-minute order changes or driver behaviors, you know? They all introduce friction into that delivery process that you can never really fully capture in planning, especially when you're making generalized assumptions. Fleets have to start to feed execution data. What actually happened, actually to that planning process. If they don't, those same inaccuracies are gonna continue day after day. Routes might look optimal on a piece of paper, but they really don't reflect those real word, world conditions, and that's why you consistently see that 10%-20% gap. When we dig into what this really means for service reliability, it becomes super significant. The plan, ungrounded in reality, routes aren't realistic, ETAs become less accurate, on time performance starts to suffer, your customers start to feel it, and that just makes your dispatch teams, so your employees then start to become firefighters and feel that pain as well throughout the day. Yeah, yeah. You know, missed time windows, poor communication, inconsistent delivery experience. These are absolute no-gos for customers these days. There's an opportunity here, right? There's an opportunity. Mm-hmm ... use AI to really make an impact on this, and close that loop and pull the data in, so that you can continually learn from that execution piece, and make your planning more adaptive and realistic over a longer time set. Okay, a real need for better planning and operational precision. Mm-hmm. Sergio, with that in mind, you know, these traditional routing models, they rely on static service time assumptions. Always have up to this point. Why has that approach persisted, however? How does it fall short when it's applied to real world, again, that phrase real- world delivery conditions like job site variability, product mix, and unloading requirements? Yeah. You know, static service times really persist because it's very simple and easy to put in operation. I mean, if you just take averages and fit them into your data integration into your routing system, that's a simple way to actually get everything going, right? The problem is that your plans may not really reflect the reality. If every stop is assumed to take roughly the same time, you know, it's very straightforward to build routes and standardize your planning. The problem is that that worked reasonably well when delivering environments where there is no complexity. However, our deliveries are not complexity, right? We all know that. Today, that simplicity on the data really becomes a limitation, and we have always said it in systems, you know, garbage in, garbage out. Yeah. The quality of data is super important. Where you get that data is also super important. Think of this. If a delivery to a retail location is fundamentally different from a construction site, why would you use the same service times? Or for a multi-drop in a commercial center, right? When those difference really are not captured, then you're actually introducing a systematic error into every single route. And what happens is that you're gonna have very low compliance from your drivers to actually execute the routes that you planned. Over time, this will be leading to, you know, missed delivery windows or inefficient use of your fleet, and a lot of midday adjustments. You're actually always reacting to what is really happening in with your drivers, and sometimes you really have to be almost like a firefighter looking into every single route to see where you can actually avoid any or propagate more exceptions. While these static models are convenient, you know, they really no longer match the complexity of modern delivery operations because now we have data that we can take advantage of that we, before we didn't have. We were just working on statistics. Now we can learn from it. That's where the AI and machine learning comes into play. Okay, what are the results though? You know, we're talking here about fleets shifting to machine learned service time predictions. They continuously learn from actual delivery durations. Again, real world. What changes in planning accuracy, and how does that enable improvements like up to, what, 30% greater route density without adding trucks or drivers, Sergio? Yeah, roughly. I mean, that's a great question because, look, you'll see that's kind of the low hanging fruit. Let's look at our service times, how they are actually represented in our planning systems so that we can actually give, you know, routes that are actually feasible or they actually are close to what the reality is. You know, when fleets are actually shifting to a machine learned service time predictions based on the actual data, you know, the biggest change is planning accuracy, you know, basically is grounded more in a real world behavior. Now you have a closing relationship between planning and execution. Instead of relying on the static assumptions, now the model will continually be learning about, you know, what the driver is actually doing when he's delivering your goods. That data factors many variables like, you know, in assuming you're talking about order size, you mentioned it earlier, product types, customer location, geographies, equipment requirements. You know, the same historical patterns that you have every time you deliver to a given customer, that comes into play as well. You know, that allows us to produce routes that are much closer to what actually happens in execution. You will see very quickly that drivers stay on schedule more consistently, your dispatchers, your people that are actually managing operations will spend less time reacting to issues, and overall operations will become more stable. You know, importantly, very importantly, the accuracy, this accuracy actually translate into better utilization. That's where the 30% comes into place. In some cases, improving route density significantly without adding any trucks or drivers. You see that right away. The, you know, the data insights and the machine learning will tell you, "actually you are actually closer to your execution, and you will be probably saving time, or maybe because you're estimating your drivers are gonna be closer to the SLAs that you have promised to your customers." It's both a service improvement and an efficiency gain, in my opinion. You're making life better for your drivers too. What about the human element of that? I mean, obviously the customers are experiencing better service. You're doing more efficient operations from your end, but all of this running around and firefighting could drive these drivers crazy. Sounds like this is really helping in the way of making the driving experience better, especially at a time when it's so hard to find good drivers. Yeah, thanks for that. Okay. One of the big truths about this whole world is said many, many times, you cannot manage what you cannot measure. Cyndi, there's an awful lot of noise around AI these days, but where are you seeing a measurable impact today in delivery operations, whether that is fewer missed delivery windows, reduced idle time, improved asset utilization like we were talking about? Tell me about that? Bob, I think you just used my favorite term, which is there's so much noise in the market about AI. Yeah. You know, most of it's, you know, people talk a lot about AI-based routing. What they're really talking about is AI-based execution, where I'm gonna move stops around, as the route transpires. They're really not talking about using AI in planning. At Descartes, we've taken a different position in saying, "Hey, look, we've gotta close the gap in a way that's unique and different." We think the gap exists today, again, between the planning and execution. We've talked about this, both Sergio and I earlier, right? When you apply AI to the execution data, what really happened throughout the day, things like your actual route service times, driver behavior, it becomes much more grounded in reality. If it's the plan that's much more grounded in reality, then the execution and comparing the execution to the plan becomes more interesting, right? If I have a bad plan and I compare execution to it, I'm not showing where I can really improve because I had a bad plan. When I have a precise plan and I can show what I did, I now have the ability to isolate where I can make those improvements, which leads to here's how you get to those very measurable, if you will, outcomes. First a couple of real easy things, right? More accurate ETAs, more accurate communications, less missed delivery windows, less extended route runtime, less overtime. These are things that are very clear that we can execute on from a metrics standpoint. We shouldn't, you know, let this kind of real-time decisioning through the day of operations get away either. You know, dispatch teams are constantly redirecting manually disruptions, right? AI can dramatically adjust the routes to do what's safe based on what's actually happening, right? Traffic delays, exceptions, you know, a note that the dock door has been blocked. You know, you're gonna try to reduce the idle time, so the wait time of drivers, improve that route adherence and keep drivers just moving along the road more efficiently throughout the day. We also see a lot of measurable gains in the categories of asset and labor utilization, right? When I learn from that historical real-time data, I can better sequence stops, reduce the unnecessary miles, you know, and make smarter use of the available capacity, whether it's vehicles or human beings. This becomes really important because of the cost pressures, right? How do I use my people and my equipment more efficiently? Just to put a bow on it and tie it all together, you know, the customer experience is also going to improve. Your customer satisfaction rates, your customer retention rates. You know, when they get better ETAs, less surprises, and better communication, magical things happen, and it becomes a real competitive advantage. It's not one or two metrics. There's a broad swath of metrics, but it takes work to get there. Well, Sergio, you know, service times, they can vary by 20%-40% based on factors like customer type, order size, geography, vehicle constraints, and the like. How does machine learning account for that variability in a way that static models cannot? How does that translate into more predictable delivery performance? Yeah. It's very interesting from all the data we have analyzed, you know, how much discrepancy, you know, you can have by having these static service times. Really, you know, you come to realize that the service times are not depending only on one or two factors. They actually can be variable. This is where it becomes really relevant. Look, service time variability is probably one of the biggest challenges in any delivery operation. You see that because of the percentages that you were just mentioning, Bob. It's exactly with where we can actually use machine learning to understand what are the factors that are actually affecting that delivery time. Like we said, we were talking about product types, we were talking about order sizes, we're talking about, you know, the location that you're delivering. Again, you know, the accessibility that you may have to that location. If you're visiting that customer constantly and you realize that that customer always gives you extra, you know, basically introduces extra service time, you need to take that into account, and the machine learning will learn from that. Even instructions that you may have on a shipment will definitely affect your service time. The beauty of artificial intelligence is not like you actually prescribe and say, "This is the way you have to actually predict service times." The beauty of it is that it's actually learning based on the context of what is actually happening in operations with the actual documentation and data that you're sending in into a routing system. All this combined is basically allowing us to have a prediction of service time that reflects more accurately and account for variability, you know, across our different types of deliveries that you're executing. The net-net is, look, a plan that has a built-in uncertainty in it basically will lead you to better productivity, predictability, execution. This will basically reduce your downstream disruptions. Why? Because now your people are actually gonna be working on more a proactive mode rather than a reactive mode, and that's the whole idea. It unleashes a lot of benefits. Like we said, one of them is basically better utilization of your fleet. Your drivers, as we mentioned earlier, they're gonna be happier because they're actually getting routes that are actually realistic instead of getting routes that they may not realistic. You see drivers coming back to the depot saying, talking to their dispatchers, "I can do this route in more time because you're actually underestimating the service times." Vice versa, "I can do it in a faster way." You know, now your SLAs that you're actually, you know, committing to your customers will be actually better met by having better, predictable routes that look more like an execution. Machine learning is definitely making a difference in this case, Bob. Thanks for helping me understand the whole concept of machine learning versus AI. That can be very difficult to parse those different terms. I guess we're learning that real world, the term real world equals unpredictability. They're almost uninterchangeable words, are they not? I want to talk to you guys about the whole, this concept of connected operations, because so many of this stuff has been fragmented up to this point. You know, Sergio, many fleets are still operating across these disconnected systems for routing. Mm-hmm ... for dispatch, for customer communication. What does a more connected data-driven delivery environment look like, and how does that improve decision-making across the day? Yeah, that's a, that's a very good question. You know, I'm actually privileged to have, to participate on this, on this products that we have at Descartes. It allows us to basically have that fully integrated solution footprint. That the script, that's kind of called that this division between system is non-existent at all. A connected delivery environment is really about bringing obviously your planning, which before it was actually separate, your execution, that again, it used to be separate, and the communication into a single operational flow, communication to your customers as well. When something happens here, you basically need to make sure that those three systems stay connected and the propagation of data works seamlessly. Look, again, historically, they used to be separate. They were managed in different systems. We used to have routing in one place and dispatching in another, and then customer communication somewhere else. You know, you're probably sending data between systems that is probably stale or is actually old or is not real time. That really creates a lot of gaps in visibility and, you know, it slows down the decision-making that you need to, that you need to have in a real time, a real time environment. When these systems are integrated, you know, now the data's actually flowing seamlessly across the operation. Fleets can see what is actually happening in real time and respond quickly to any exceptions. You know, that level of connectivity basically allows them to manage the entire delivery process for, again, and I mentioned this earlier, from a more proactively and consistently manner. You know, the fact that you have this data flowing consistently and being able to send proactively notifications to your customer if there is a delay or they could be basically you'll be actually maybe actually arriving earlier. This actually improves the efficiency of your fleet, you know, avoiding any delays or avoiding any missed deliveries because the customer is not available, et cetera, et cetera. It's just a cascade of benefits that comes with it. I'm glad you brought up the idea of responding because a lot of this conversation up to this point has been about how you can better predict what's going to happen. Mm. Not the most sophisticated AI model in the world, let alone the most intelligent human being, can 100% predict what is actually going to happen because real world is unpredictable. Real world, there's a certain element of chaos, let's face it. You have to be able to respond accordingly. Sergio, as plans inevitably change in the process of execution, how does combining planning data with real-time visibility let fleets adjust routes dynamically while maintaining service commitments? Sergio, I can't hear you all of a sudden. Are you? I think we've lost your. No? Cyndi, can we hear you? I'm here. Okay, you sound like you're muted, but I don't see a mute form. Let's see what we can do here real quick, or we can turn to Cyndi for that if he, if we can't hear Sergio anymore. Sergio, you might try muting and unmuting on your audio there to see if that brings it back. All right, how about now? Can you hear me? There we go. There we go. Sorry about that. That's better. Yeah, probably my audio switched to something. Yeah, no. before. This happen. Did I say about unpredictability? Okay, folks. Unpredictability. Exactly. Here we go. Here we are, a real-world demonstration of what we're talking about today. Again, let's talk about the whole idea of response, real-time visibility, planning data, combining with that. Respond to that. Thank you, Bob. Yeah. Look, in any delivery operation, we know plans will be changing during the day, right? That's just unavoidable. This is the nature of the beast. The key is how we effectively respond to those changes. When, you know, when you see planning data that is connected to real-time full visibility, you know, fleets can actually make informed adjustments as conditions are changing, right? That might mean rerouting or, you know, updating ETAs as we mentioned earlier, or even reallocating resources to work on any exceptions. For example, if you must resequence a route to minimize the risk of missing time windows, you must know where your drivers are at any point in time. You don't want to be resequencing and driving them crazy when they are probably en route to a delivery. You know, then at that point you can decide what and what cannot be changed. You know, and the changes that you're actually making to your delivery plan have to be communicated, obviously, to your drivers in real time, and also to your customers to avoid any future exceptions. You know, having that planning data connected to the real-time visibility is what enables you to make educated and effective, systematic decisions. Instead of reacting after service levels are impacted, okay, they can make control adjustments that help you maintain the performance that you're expecting. That's the real shift. You know, it's from, like I said, it's actually from moving from this reactive firefighting to a more proactive and this is important, data-driven decision management. This is where the power of data coming from planning, coming from execution, is actually collaborating to make sure that you have that at your fingertips in real time so that you can actually do proactive decisions and educated decisions. Yet another truth, it's all about the data. We hear that in any technology initiative, and especially when it comes to AI applications and the like. I wanna talk now though about what it really is all about, and that is the customer experience. Cyndi, once a route is in motion, delivery becomes the customer experience. Mm-hmm. How are leading fleets using real-time visibility and predictive insights to manage the full delivery journey from dispatch through final confirmation, rather than just through a series of individual stops? You know, you're right, Bob. It's not a series of individual stops anymore, and it was for a very long time. It is really a continuous customer journey full of expectations that continue to change, that we talked about earlier, right? You know, what this looks like in real practice though is leveraging a customer experience or a customer engagement platform. This platform has to work in conjunction with your plan and real-time execution. There's two pieces to this. You know, it's pulling in information about the plan, but also pulling in live route data and tracking progress throughout the day, right? Then it's gonna use some predictive insights to understand not only where is my driver, but how is the rest of that day actually unlikely to unfold. You know, I might be sent a notification instead of saying, "I plan to be there at 2:00," and sending a notification at 1:50 that says, "I'm 10 minutes away." Now I'm not gonna arrive till 2:05, so I might not send that notification till 1:55 with that 10 minutes there, right? It's being a little bit more precise there. You know, when I look at how do companies create a competitive advantage today, it really is communication via customer notifications, right? That's the way to create that competitive advantage. That's really the crux of everything. You know, if you're trying to find that advantage where margins continue to get smaller as costs increase, you have to provide kind of these dynamic, event-driven communications around the entire order journey. From the minute I've taken the order to the moment that it's delivered to the final moment where I provide feedback on that delivery or that driver. You know, leaders in the space are going to be adapting technology that's not just, where's my truck, but where's my truck with context. Right. Every time you make a communication with a customer, you are in fact raising a customer expectation to meet that promise. Mm-hmm ... of that communication, so you better be right about what you're telling them, what time you're gonna show up, or you're gonna have some very angry customers. Absolutely. Sergio, I apologize if I'm sounding a little bit obsessed about this whole concept of response, because that is really what we're talking about here today. More, even more than prediction, how you actually respond to the, again, to the real world. Sorry about keep saying that. As plans change throughout the day, and of course they do, how does connecting routing, execution, and customer communication in one platform help fleets to manage exceptions in real time while still protecting service commitments? Yeah, I mean, again, this is where the passion becomes. I mean, you guys have all used Uber, and the ability to actually see real time what is happening with the driver when it's coming to you know, allows you to make decisions as a, as the passenger. Even if you, for whatever reason you need to move locations, or maybe you don't want it anymore, you can actually do that in real time. That's exactly what we're talking about here, because, you know, exceptions are definitely gonna happen no matter what, in every delivery operations. You know, talk, you name it, delays, changes, unexpected conditions, new orders coming in, new pickups that you need to do and you need to accommodate. These type of things basically change your plan. The good news is if you have real-time communication end to end from the planning all the way to execution and to the end customer, it allows you to actually be more efficient and advise people ahead of time to actually remove that noise and avoid any inefficiencies that actually may actually, you know, be, generated from it. The difference now is the ability to manage those exceptions in real time. That's the reality. When route execution again and the communication to the customer are fully connected. You know, fleets can quickly adjust to changes. You know, update ETAs and notify our customers if there is any exceptions. Like, "Hey, I'm probably gonna be. We're gonna be 30 minutes late from the ETA." If the customer basically tells you, "Hey, I'm not gonna be there, don't even show up," then you know that immediately you have to adjust your plan. Vice versa, "I'd like actually now to move up my delivery to the afternoon because we're not gonna be present that day." That actually, that information that comes back, and the moment that the customer actually does that, and if it is connected to your planning and execution, systematically you can make a decision that is based on the data that you have received up to that point in real time. This is just basically a way of minimizing the impact on any disruptions or delays, missing deliveries, no-shows, et cetera. It does help us to maintain, you know, service commitments even when things don't really go as expected, Bob. Yeah. It's funny 'cause we talk about how demanding customers are, but at the same time they're kind of forgiving if you're level with them. If you tell them what's really happening. If you don't, that, the worst thing you can do is not communicate, have something show up late and you didn't tell them it was gonna be late. Speaking as a, you know, we've all been customers in that area, and we don't like it, so it, it's a good point to make. Okay, Cyndi Brandt, you know, we were just talking about what's happened with our customer expectations in the last two or three years to the point now where on-time delivery is no longer enough. Customers also want full transparency and coordination. How do capabilities like dynamic ETAs, estimated times of arrival, proactive notifications, like you were saying, and centralized communication, how do these things reduce friction, improve job site planning, and enable faster service recovery when issues actually do arise? No, like you said, customer expectations really and truly have evolved because we have so much access to data, right? You know, it's gone from simply being, quote, unquote, on time to a fully informed, fully coordinated, and predictable experience, right? I wanna have the same experience every time. I wanna know all the information that I can about that delivery piece. You know, if I can coordinate all this, that's where all these capabilities can really start to make a significant and meaningful difference within organizations. You know, when I think about dynamic ETAs and proactive notifications and centralized communication, fleets have the ability now to kinda stop reacting to managing that entire delivery journey in real time. In the past, we'd get a phone call, "Where's my truck?" We would just immediately drop everything and react and make seven phone calls. I'm pulling all the friction out of that by allowing the data to work for us, right? You know, if something changes super early in the route, I'm getting data information in about that. The system can start to really anticipate what's that downstream impact. There was a traffic jam. It slowed things down by 10 minutes. Nobody's gonna be upset if your ETA is impacted by 10 minutes as long as you know about it. You know? Adjust the ETAs, notify those customers. You know, I like to say trigger operational interventions before they happen or escalate within there. By reducing the friction and with all this transparency, right? When you think about job site coordination, specifically, like freeing up people to meet the truck, making sure you have enough labor to meet the truck, making sure that your workers get to a building site to, you know, meet the truck that has a tremendous amount of, or expensive materials on it, right? I can plan and anticipate better. When customers know when to expect deliveries, they can make adjustments to their day so that they can take those deliveries. Remember, if they don't have the right people waiting around, well, first of all, it's expensive to have people waiting around for a delivery, but if they don't have the right people, ultimately you as the delivery company is going to be delayed, and it's going to impact other customers down the road. The last thing I would say is that it doesn't really stop at the delivery itself. When you connect proof of delivery with feedback capture, you're also closing that loop, and then you turn your execution piece not only into insight for planning, but insight into your customer that feeds continuous improvement, right? Does Cyndi Brandt's restaurant always reject half of the fresh vegetables that are delivered, right? You know, there's all kinds of things that you can start to look at to create insights, to create insights into ordering patterns. That's a whole nother way to apply AI. At the end of the day, let's work on creating a controlled, consistent customer experience with fewer surprises for those customers, less firefighting internally for our dispatch and operations team, and, you know, a delivery journey that's really more managed as opposed to kinda going back to that one stop at a time. Yeah. Okay. Sergio, I do wanna quickly touch on this thing that any time technology comes into play and you have humans using it, the systems become more intelligent. How do you ensure that planners, dispatchers, and drivers actually trust the outputs and use them consistently in day-to-day operations? Yeah, look, a successful adoption comes down to, you know, to making this part of how the operation runs every day. It's not just about. You know, technology is an enabler, Bob. You know, we humans make it happen if we actually run in every single day during our operations activity. You know, the fleets, in my opinion, that see the most value, they really don't treat this type of things as a technology project. It should be aligned with operational objectives, goals. You know, such as, I mean, Cyndi Brandt has been talking about it in her responses, like, improving on-time performance, increasing, I don't know, route efficiency, enhancing your customer experience, and that's how we wanna see it. We're going to be using machine learning for those purposes. Also, we have to involve the people that are going to be using it in every, in a, in a daily basis, such as your planners, your dispatchers, your drivers. Feedback is super important, right? It's, as long as you actually, you know, involve them early in the process, the system will fit naturally into their workflow. You know, when that happens, you know, technology really becomes a tool for better decision maker. It's just not just another system you're going to be actually managing. It's actually giving you the freedom and the knowledge and the foundation to actually base your decisions on. Mm Be more effective when this happens. It actually creates confidence at the same time. It creates confidence on your planners, dispatchers, and drivers, but also on your customer base as well, that you know that you're gonna be, you know, compliant with their SLAs that you have promised in the past. That's where the real successful adoption comes from. Okay. We're short on time, but I just wanna get a quick answer from you, Cyndi, on this one. Sure. Over the next 12-18 months, what capabilities will define high performing delivery fleets? You know, on the planning side, let's get away from static optimization. Let's get the plans that are informed by historical execution data and become more dynamic, right. You know, customer engagement, you've gotta fully integrate that process into what you're doing. It can't just be notifications. It's really gotta be information about the entire order journey. If you bring these two things together well, you're gonna get better route adherence, so better route run times, more reliable ETAs, and better asset utilization. Well, thank you very much for that, and thank you guys for your great answers and this great panel. I've learned a lot myself in the last hour about the applying AI and machine learning to this whole issue. It's now time, as I promised, to bring the audience in. We do have some audience questions already submitted, if we, you know, we'll get to as many of those as we can, time permitting, but please do continue to submit your questions by clicking on that Q&A icon at the bottom of your screen. I'm just gonna throw these out and see who wants to take them. This question is, what's the most practical first step for a fleet that wants to move from static planning to more data-driven service time predictions? I see you nodding, Cyndi, so I'm gonna throw this one in your direction. Well, I think the practical first step is just start capturing and using actual service time data that's coming in from the field. A lot of fleets have the data. You know, they can pull it in from telematics, cameras, other driver apps, but it's not systematically being used. If you aggregate that data and apply it to planning, even in a super simple way, you don't even have to use machine learning, although it's better, you can start to replace assumptions. Okay. Thanks for that. Now this question says, do you need a large amount of historical delivery data to benefit from machine learning, or can smaller fleets still see value? Sergio, why don't you take that one? Sure. Thank you, Bob. You don't really need that much, you know, amount of data. That, you know, even smaller fleets can benefit from this. You can see that the models are gonna be improving over time. Look, what we're always talking to our customers. We say, "Look, let's start executing your routes, and we're gonna learn a wealth of information that we can actually utilize to improve your, you know, to improve basically your route planning." That's where the value starts. We, you know, Cyndi mentioned it, measuring the actual service times. You cannot improve what you don't understand. By starting to capture and using the data that you already have is probably the best source of wealth that you can have to actually improve your operation. Let's start there. It, and even, like I said, even it doesn't matter what fleet size you have. It is important that you actually start capturing that data to learn from it and use it in your planning operations. Thanks. Questioner wants to know what is the most common mistake that fleets make when they're trying to modernize delivery operations with AI. Sergio, you look like you're ready to answer that question. Sure. Sure. I'll jump in. Look, trying to do too much at once. That's the biggest problem, and, you know, trying to actually boil the ocean. The best approach, in my opinion, is to start with a focused use case. Then once you actually have that case defined, now you move on to prove the value and then scale from there. You can actually start with a small operation in a few vehicles to figure out, okay, this is what I'm gonna be doing. I'm gonna be learning from these drivers. I'm actually gonna start, you know, communicating with customers to see what the value of doing such action will actually bring to my business. Is it actually reducing time? Is it actually improving the fleet utilization? Is it improving our customer satisfaction? Once you have actually that proof, then you have a platform to actually scale from there. Okay. This question says, how can drivers get involved with how their data capture is being made and used? I like that question, 'cause we have to keep bringing drivers back into the picture. We can't forget them. Cyndi, why don't you take that one? You know, I think that people do forget that drivers are such a critical piece of this, right? Mm. Involving them in the conversation first and foremost. These are the people driving the trucks, and quite frankly, sometimes they have the best ideas. Again, what looks good on paper may not be practical in execution. One, explain to them what you're doing. Two, ask them what the barriers are to collecting the data. Three, ask them if there's a better way to do it, right? They may say, "You know what? Pushing buttons is really hard. Could you turn on auto-arrive, auto-depart?" They may say, you know, "If you collected this metric or this piece of information while I was at a customer, I can reduce the amount of tribal knowledge that's needed to be able to service customers." Have, just sit down and have the conversations with them. We don't do that often enough, and so many great ideas come from drivers. Okay. A real quick one on this one. Maybe I'll give this one to Sergio, just very quickly. How will machine learning continue to evolve in this context? You know, it helps tremendously because it helps us actually learn from what actually the driver is doing. In that context, as Cyndi was explaining, look, if you involve the driver right from the beginning and you tell him why you're actually, you know, collecting data when he's utilizing a mobile device, and you're telling him, "A, it is important that, you know, why we're actually measuring when you arrive versus when you complete your services is actually to provide you with better routes. That will basically improve your driver satisfaction in a long shot. At the same time, that data will be basically used and the driver will be able to see how that actually effect is impacting their the routes. If you actually, with data, again, with data visibility. Mm-hmm Hey, Mr. Driver, look at this. This is actually the way you're actually securing. This is the way we planned it. It's actually very close. We're actually doing well." The driver can come back and say, "You know what? Now that you're actually coming back with that, I told you I can do this route in 6.5 hours instead of the 8 hours that you gave me. Mm-hmm. Let's put on more because probably that improves also the driver satisfaction to actually do more work. Maybe you can incentivize driver to be more efficient in that manner. Okay. Regrettably, I must cut us off at this point. This is such a fascinating discussion. I wish we had another hour to talk, but we don't. Thank you for that answer, Cyndi, as well. Thank you both of you guys for these, for your great participation. Right. We do have time for just one final question, and here it is. We know that delivery teams are being asked to improve stop density, on time, and in full performance, customer visibility without adding cost or complexity. We know that. What is the one shift though that leaders need to make now to move from reactive execution to more precise data-driven operations, and what is the one action they should take in the next 90 days? Cyndi, why don't you go first on that one. Gee, Bob, that was a pretty tough question to land on there right there. Thank you. Sorry. You know, I think here's the reality. You have to make a shift from kind of these strategic static plans to continuous data-driven operations. You have to use that real execution data to improve decisions not only in real time, but in planning time. In the next 90 days, focus on a single use case. Something I'm gonna say simple, but it's not simple. Like ETA accuracy, right? Start turning that execution data into action. If you focus on one, that's how you can get impact fast. Sergio, what do you think? Again, as Cyndi mentioned, you know, planning, excuse me. Your shift is planning from assumptions. Instead of planning on assumptions, you actually plan on actual delivery behavior. That's the number one shift that I will mention. That if you have something to do, I'm gonna give you an action item on the next 90 days. It's start capturing and using that real delivery data. That's it. Capture it. That's the very first foundation to actually start looking into service time predictability and execution variability. That's the foundation for improving your planning accuracy, Bob. Again, guys, this has been a great discussion. Thank you so much, Sergio and Cyndi, for this excellent presentation, for your answers to my questions as well as those of the audience, and thank you, audience, for posing them. This has been wonderful. Thank you so much. We have for you an interactive demo, real-time visibility that drives delivery performance. You can access it by taking a quick shot of that QR code. You'll be able to see deliveries in action. Scan that to explore the experience in real time. We also want to bring to your attention this episode two in this four webinar series this year from Descartes under the common theme of AI Exchange. This time it'll be about AI agents for fleet performance management. The date is June 23rd. The time is 11:00 A.M. Eastern. Again, there's a QR code for you to take a shot of if you want more information to save your spot. However, if you don't have time to take that QR code or the one before, which has already disappeared, obviously, don't worry about it. All that will be provided to you attendees at the conclusion of this webinar, which of course is right now. Thanks again, Cyndi. Thanks again, Sergio. Thank you so much, audience. Everybody, have a great day. Appreciate it, Bob. Thank you.