AI & Fleets: The Shocking Truth About Your Data!

try. Okay. All try. Okay. All >> right. Morning everyone. Uh my name is >> right. Morning everyone. Uh my name is >> right. Morning everyone. Uh my name is Yonghu, also commonly known as uh SH. Yonghu, also commonly known as uh SH. Yonghu, also commonly known as uh SH. I'm an analyst at Bloomberg New Energy I'm an analyst at Bloomberg New Energy I'm an analyst at Bloomberg New Energy Finance and it is my absolute pleasure Finance and it is my absolute pleasure Finance and it is my absolute pleasure to moderate this panel today. Uh so in to moderate this panel today. Uh so in to moderate this panel today. Uh so in my day-to-day work, I cover my day-to-day work, I cover my day-to-day work, I cover transportation trends and how technology transportation trends and how technology transportation trends and how technology is reshaping mobility and fleet is reshaping mobility and fleet is reshaping mobility and fleet operation. And fleets today, as you operation. And fleets today, as you operation. And fleets today, as you know, are dealing with more complexity know, are dealing with more complexity know, are dealing with more complexity than ever. rising cost, supply chain than ever. rising cost, supply chain than ever. rising cost, supply chain volatility, uh shifting customer volatility, uh shifting customer volatility, uh shifting customer expectations. So it is not surprising expectations. So it is not surprising expectations. So it is not surprising then that AI is increasingly being used then that AI is increasingly being used then that AI is increasingly being used uh been integrated into fleet operations uh been integrated into fleet operations uh been integrated into fleet operations to enhance safety, efficiency, to enhance safety, efficiency, to enhance safety, efficiency, resilience, you name it. So to help us resilience, you name it. So to help us resilience, you name it. So to help us unpack this topic, we have today uh unpack this topic, we have today uh unpack this topic, we have today uh Brendon Wiggins from Congress, uh Jamie Brendon Wiggins from Congress, uh Jamie Brendon Wiggins from Congress, uh Jamie Boxstrom from Step Energy Services, and Boxstrom from Step Energy Services, and Boxstrom from Step Energy Services, and Michael Bernice from Motive. Welcome Michael Bernice from Motive. Welcome Michael Bernice from Motive. Welcome everyone. So, let's start maybe with a

everyone. So, let's start maybe with a everyone. So, let's start maybe with a quick round of introductions. Uh, tell quick round of introductions. Uh, tell quick round of introductions. Uh, tell us your your title, your role, your us your your title, your role, your us your your title, your role, your company. And for first timers in company. And for first timers in company. And for first timers in Nashville like myself, can you recommend Nashville like myself, can you recommend Nashville like myself, can you recommend one thing to do before I fly out of one thing to do before I fly out of one thing to do before I fly out of Nashville tomorrow, please? Brendon, you Nashville tomorrow, please? Brendon, you Nashville tomorrow, please? Brendon, you want to go first? want to go first? want to go first? >> This is not in the notes. >> This is not in the notes. >> This is not in the notes. >> Yeah, I know, right? So, that was that >> Yeah, I know, right? So, that was that >> Yeah, I know, right? So, that was that wasn't wasn't wasn't >> throwing us a curveball. >> throwing us a curveball. >> throwing us a curveball. >> Um, yes. I'm I'm Brendan Wiggins. I'm >> Um, yes. I'm I'm Brendan Wiggins. I'm >> Um, yes. I'm I'm Brendan Wiggins. I'm the the vice president of fleet and the the vice president of fleet and the the vice president of fleet and equipment at Congruix. Um we uh do fiber equipment at Congruix. Um we uh do fiber equipment at Congruix. Um we uh do fiber optic construction. So in in ground and optic construction. So in in ground and optic construction. So in in ground and aerial fiber optic construction. Um but aerial fiber optic construction. Um but aerial fiber optic construction. Um but just been at Congruix uh maybe two just been at Congruix uh maybe two just been at Congruix uh maybe two months now to since taking over the months now to since taking over the months now to since taking over the fleet there. Um but been an industry guy fleet there. Um but been an industry guy fleet there. Um but been an industry guy for a long time. So um in fleet about 15 for a long time. So um in fleet about 15 for a long time. So um in fleet about 15 years. years. years. >> Nice. >> Nice. >> Nice. >> I'm Jamie Borstrom. I'm the DOT manager. >> I'm Jamie Borstrom. I'm the DOT manager. >> I'm Jamie Borstrom. I'm the DOT manager. So, Department of Transportation Manager So, Department of Transportation Manager So, Department of Transportation Manager with Step Energy Services, uh, Canadian with Step Energy Services, uh, Canadian with Step Energy Services, uh, Canadian division. So, help with the fleet and division. So, help with the fleet and division. So, help with the fleet and transportation, uh, helping the field transportation, uh, helping the field transportation, uh, helping the field operations. So, I've been with STEP for operations. So, I've been with STEP for operations. So, I've been with STEP for 15 years, been in the DOT department for 15 years, been in the DOT department for 15 years, been in the DOT department for like 20 now. So, like 20 now. So, like 20 now. So, >> yeah. >> yeah. >> yeah. >> Nice. Nobody's telling you what to do in >> Nice. Nobody's telling you what to do in >> Nice. Nobody's telling you what to do in Nashville. Nashville. Nashville. >> I know. >> I know. >> I know. >> Come on, give me one at least. I skipped >> Come on, give me one at least. I skipped >> Come on, give me one at least. I skipped it. it. it. >> Well, I have one. I'll I'll give him >> Well, I have one. I'll I'll give him >> Well, I have one. I'll I'll give him one. Don't worry. Okay. So, hi everyone. one. Don't worry. Okay. So, hi everyone. one. Don't worry. Okay. So, hi everyone. I'm Mike. Uh I lead the AI team here at I'm Mike. Uh I lead the AI team here at I'm Mike. Uh I lead the AI team here at Motive and all those exciting uh product Motive and all those exciting uh product Motive and all those exciting uh product features and things that you heard about features and things that you heard about features and things that you heard about in the keynote. My team builds those. So in the keynote. My team builds those. So in the keynote. My team builds those. So I'm very excited to hear from all of you I'm very excited to hear from all of you I'm very excited to hear from all of you what is top of mind and to talk to you a what is top of mind and to talk to you a what is top of mind and to talk to you a little bit about uh what's going on at little bit about uh what's going on at little bit about uh what's going on at Motive and SH. There's one thing I know Motive and SH. There's one thing I know Motive and SH. There's one thing I know you need to do in Nashville. It's the you need to do in Nashville. It's the you need to do in Nashville. It's the Rock and Roll History. Oh, no. Country Rock and Roll History. Oh, no. Country Rock and Roll History. Oh, no. Country Music History Museum. Music History Museum. Music History Museum. >> Done on Monday. Next. >> Done on Monday. Next. >> Done on Monday. Next. >> What's that? >> What's that? >> What's that? >> I've done it on Monday. >> I've done it on Monday. >> I've done it on Monday. >> You did it already? >> You did it already? >> You did it already? >> I'm out then. >> I'm out then. >> I'm out then. Maybe someone else in the audience has Maybe someone else in the audience has Maybe someone else in the audience has one. one. one. >> All right, we can talk. >> All right, we can talk. >> All right, we can talk. >> We'll take in the Q&A later. >> We'll take in the Q&A later. >> We'll take in the Q&A later. >> Yeah. So, so and then Jamie, it sounds >> Yeah. So, so and then Jamie, it sounds >> Yeah. So, so and then Jamie, it sounds like both of your companies are like both of your companies are like both of your companies are operating in very different operating in very different operating in very different environments. Maybe you can walk us

environments. Maybe you can walk us environments. Maybe you can walk us through how exactly you're using AI in through how exactly you're using AI in through how exactly you're using AI in your in your day-to-day operations. Just your in your day-to-day operations. Just your in your day-to-day operations. Just on a high level, on a high level, on a high level, >> do you want to first? >> do you want to first? >> do you want to first? >> Yeah, you start. >> Yeah, you start. >> Yeah, you start. >> I think that the the most obvious place >> I think that the the most obvious place >> I think that the the most obvious place we're using it dayto-day is is the mode we're using it dayto-day is is the mode we're using it dayto-day is is the mode of cameras, right? is is the safety of cameras, right? is is the safety of cameras, right? is is the safety stack. Um, and how we're able to scale stack. Um, and how we're able to scale stack. Um, and how we're able to scale up, you know, a program like that as up, you know, a program like that as up, you know, a program like that as quickly as we did. Um, really leans on quickly as we did. Um, really leans on quickly as we did. Um, really leans on the the the AI portion of the safety the the the AI portion of the safety the the the AI portion of the safety cam. And so I would say that's the the cam. And so I would say that's the the cam. And so I would say that's the the biggest place where we're using it right biggest place where we're using it right biggest place where we're using it right now, but also starting to use a lot of now, but also starting to use a lot of now, but also starting to use a lot of generative AI in all kinds of places generative AI in all kinds of places generative AI in all kinds of places across um the fleet organization. So, across um the fleet organization. So, across um the fleet organization. So, it's just saving us a lot of time on uh it's just saving us a lot of time on uh it's just saving us a lot of time on uh repeat tasks and um and also just when repeat tasks and um and also just when repeat tasks and um and also just when we have big documents that we need to we have big documents that we need to we have big documents that we need to distill or um getting us a head start on distill or um getting us a head start on distill or um getting us a head start on creating documentation for other creating documentation for other creating documentation for other stakeholders inside the organization. Um stakeholders inside the organization. Um stakeholders inside the organization. Um it's just starting to give my team kind it's just starting to give my team kind it's just starting to give my team kind of a leg up uh to to get us started on of a leg up uh to to get us started on of a leg up uh to to get us started on uh on some of that work that used to uh on some of that work that used to uh on some of that work that used to take people a long time. and and so I'm take people a long time. and and so I'm take people a long time. and and so I'm very focused on the right people doing very focused on the right people doing very focused on the right people doing the right work and uh and if we can have the right work and uh and if we can have the right work and uh and if we can have AI take some of that and then take my AI take some of that and then take my AI take some of that and then take my folks my subject matter experts and folks my subject matter experts and folks my subject matter experts and pivot them towards more value added work pivot them towards more value added work pivot them towards more value added work um anywhere that we're able to do that um anywhere that we're able to do that um anywhere that we're able to do that is is kind of my focus. is is kind of my focus. is is kind of my focus. >> Yeah. So I should say uh step energy >> Yeah. So I should say uh step energy >> Yeah. So I should say uh step energy services is an oil field company so services is an oil field company so services is an oil field company so fracturing now cementing uh coil tubing fracturing now cementing uh coil tubing fracturing now cementing uh coil tubing nitrogen fluid. So um our logistics nitrogen fluid. So um our logistics nitrogen fluid. So um our logistics department is where we use our AI department is where we use our AI department is where we use our AI cameras right now and our new uh merger cameras right now and our new uh merger cameras right now and our new uh merger company uh also uses them and uh so yeah company uh also uses them and uh so yeah company uh also uses them and uh so yeah before it was just like relying on before it was just like relying on before it was just like relying on lagging data right and now we it's lagging data right and now we it's lagging data right and now we it's helping us with our reports and our um helping us with our reports and our um helping us with our reports and our um like our fuel consumptions and that and like our fuel consumptions and that and like our fuel consumptions and that and creating reports things we just didn't creating reports things we just didn't creating reports things we just didn't know before. you know, we have our know before. you know, we have our know before. you know, we have our safety protocols in order, but were they safety protocols in order, but were they safety protocols in order, but were they actually being done, right? So, now it actually being done, right? So, now it actually being done, right? So, now it just helps us see things like this now just helps us see things like this now just helps us see things like this now and definitely something we want to get and definitely something we want to get and definitely something we want to get more into our fleets to get more data, more into our fleets to get more data, more into our fleets to get more data, more reporting and to help our more reporting and to help our more reporting and to help our professionals to keep them safe. professionals to keep them safe. professionals to keep them safe. >> Yeah. And Michael, from M's perspective,

>> Yeah. And Michael, from M's perspective, >> Yeah. And Michael, from M's perspective, where are you seeing AI create uh the where are you seeing AI create uh the where are you seeing AI create uh the biggest value across today? I mean, when biggest value across today? I mean, when biggest value across today? I mean, when I when I hear from customers and I talk I when I hear from customers and I talk I when I hear from customers and I talk to customers like Jamie and others, um, to customers like Jamie and others, um, to customers like Jamie and others, um, what we hear is that there's there's a what we hear is that there's there's a what we hear is that there's there's a bunch of trends and a lot of that you bunch of trends and a lot of that you bunch of trends and a lot of that you heard in the keynote this morning. So, heard in the keynote this morning. So, heard in the keynote this morning. So, companies tell us all the time they have companies tell us all the time they have companies tell us all the time they have so much data they don't know what to do so much data they don't know what to do so much data they don't know what to do with it. So, they're data rich but time with it. So, they're data rich but time with it. So, they're data rich but time poor. And so, um, we're trying to help poor. And so, um, we're trying to help poor. And so, um, we're trying to help unlock the value of that data. So people unlock the value of that data. So people unlock the value of that data. So people who are literally too busy to even who are literally too busy to even who are literally too busy to even analyze it, they can get the benefit of analyze it, they can get the benefit of analyze it, they can get the benefit of it because it's their data and it it it because it's their data and it it it because it's their data and it it should be something they can uh should be something they can uh should be something they can uh leverage. Uh we hear about you know the leverage. Uh we hear about you know the leverage. Uh we hear about you know the need to reduce manual work across the need to reduce manual work across the need to reduce manual work across the fleet operations and physical operations fleet operations and physical operations fleet operations and physical operations economy all all over the place. Um economy all all over the place. Um economy all all over the place. Um filling out reports and writing filling out reports and writing filling out reports and writing documents or um ingesting documents or documents or um ingesting documents or documents or um ingesting documents or having workflows that can be automated. having workflows that can be automated. having workflows that can be automated. So giving people back that time. so that So giving people back that time. so that So giving people back that time. so that yourmemes can do the work that is most yourmemes can do the work that is most yourmemes can do the work that is most value added for them. Um and then safety value added for them. Um and then safety value added for them. Um and then safety right like everybody wants to make their right like everybody wants to make their right like everybody wants to make their operations as safe as possible and I operations as safe as possible and I operations as safe as possible and I think with the advances in AI that we think with the advances in AI that we think with the advances in AI that we see all of us feel it like every day in see all of us feel it like every day in see all of us feel it like every day in in our day-to-day life but uh I believe in our day-to-day life but uh I believe in our day-to-day life but uh I believe and I know that there are ways we can and I know that there are ways we can and I know that there are ways we can take that those um huge advances that take that those um huge advances that take that those um huge advances that are happening and bring them to are happening and bring them to are happening and bring them to operations that can allow them to be operations that can allow them to be operations that can allow them to be safer to provide like accident safer to provide like accident safer to provide like accident prevention or uh safety monitoring other prevention or uh safety monitoring other prevention or uh safety monitoring other things. And so we're really focused on things. And so we're really focused on things. And so we're really focused on unlocking that capability for motives unlocking that capability for motives unlocking that capability for motives customers. customers. customers. >> Right. So Brandon, you mentioned earlier >> Right. So Brandon, you mentioned earlier >> Right. So Brandon, you mentioned earlier on about work that used to take a lot of on about work that used to take a lot of on about work that used to take a lot of time and you Michael just mentioned uh time and you Michael just mentioned uh time and you Michael just mentioned uh your customers are basically data rich your customers are basically data rich your customers are basically data rich but time pool. So I like to dig deeper but time pool. So I like to dig deeper but time pool. So I like to dig deeper into the topic of productivity. Maybe

into the topic of productivity. Maybe into the topic of productivity. Maybe let's just kick start of discussion in let's just kick start of discussion in let's just kick start of discussion in the topic. So Jamie, how how is AI the topic. So Jamie, how how is AI the topic. So Jamie, how how is AI actually reducing manual work for you actually reducing manual work for you actually reducing manual work for you today? And are you seeing real gains in today? And are you seeing real gains in today? And are you seeing real gains in terms of efficiency or response times? terms of efficiency or response times? terms of efficiency or response times? Something that's really measurable Something that's really measurable Something that's really measurable across the board? across the board? across the board? >> Across the board. Um it's it's I mean for all of us, right? Like it's I mean for all of us, right? Like it's I mean for all of us, right? Like it's just that real time data. It's being just that real time data. It's being just that real time data. It's being able to start that conversation sooner able to start that conversation sooner able to start that conversation sooner with our drivers and stuff. with our drivers and stuff. with our drivers and stuff. um pulling pulling all this the safety pulling pulling all this the safety off the AI and uh getting the reports off the AI and uh getting the reports off the AI and uh getting the reports done in that. Um done in that. Um done in that. Um yeah, I mean we went from just ELDs and yeah, I mean we went from just ELDs and yeah, I mean we went from just ELDs and tracking devices to now having this AI tracking devices to now having this AI tracking devices to now having this AI technology and it's just grown and technology and it's just grown and technology and it's just grown and helped us so much. Right. helped us so much. Right. helped us so much. Right. >> Right. And and you Brenda, are you >> Right. And and you Brenda, are you >> Right. And and you Brenda, are you seeing workflows basically move from seeing workflows basically move from seeing workflows basically move from let's say a reactive problem solving let's say a reactive problem solving let's say a reactive problem solving angle to a more proactive approach? Is angle to a more proactive approach? Is angle to a more proactive approach? Is that happening in your space? that happening in your space? that happening in your space? >> Yes. And and and I think that there's >> Yes. And and and I think that there's >> Yes. And and and I think that there's like you said there's uh things that like you said there's uh things that like you said there's uh things that used to take us a long time manual used to take us a long time manual used to take us a long time manual things. So the everybody wants some, you things. So the everybody wants some, you things. So the everybody wants some, you know, big sexy solution, but just things know, big sexy solution, but just things know, big sexy solution, but just things like when we get an invoice from a third like when we get an invoice from a third like when we get an invoice from a third party vendor and we can take a picture party vendor and we can take a picture party vendor and we can take a picture of it and it will create a work order in of it and it will create a work order in of it and it will create a work order in my maintenance system. Those kinds of my maintenance system. Those kinds of my maintenance system. Those kinds of things save a ton of time where I used things save a ton of time where I used things save a ton of time where I used to have an admin who would have to go to have an admin who would have to go to have an admin who would have to go and key all that in. And you know and key all that in. And you know and key all that in. And you know there's a risk that the AI sometimes there's a risk that the AI sometimes there's a risk that the AI sometimes gets that that transcription wrong, but gets that that transcription wrong, but gets that that transcription wrong, but there was also a chance that the human there was also a chance that the human there was also a chance that the human got it wrong. So we had plenty of data got it wrong. So we had plenty of data got it wrong. So we had plenty of data integrity challenges before and I integrity challenges before and I integrity challenges before and I actually think that that's one place actually think that that's one place actually think that that's one place that AI is helping us quite a bit is the that AI is helping us quite a bit is the that AI is helping us quite a bit is the data integrity. So you said like we're data integrity. So you said like we're data integrity. So you said like we're data rich um and that was true but also data rich um and that was true but also data rich um and that was true but also when you dig into it when you dig into it when you dig into it >> was all of that data useful and and the >> was all of that data useful and and the >> was all of that data useful and and the AI is able to help us kind of distill AI is able to help us kind of distill AI is able to help us kind of distill that as well and go and do some data that as well and go and do some data that as well and go and do some data clean up. We're doing a lot of data clean up. We're doing a lot of data clean up. We're doing a lot of data integrity work and then also things like integrity work and then also things like integrity work and then also things like scoping documents when we're looking at scoping documents when we're looking at scoping documents when we're looking at hey I need to go build a new integration hey I need to go build a new integration hey I need to go build a new integration across two different systems in the across two different systems in the across two different systems in the business. before would require us to get business. before would require us to get business. before would require us to get severalmemes in the room and hash that severalmemes in the room and hash that severalmemes in the room and hash that out of what what data is available in out of what what data is available in out of what what data is available in that system, what data is available that system, what data is available that system, what data is available here, what are the API connectors that here, what are the API connectors that here, what are the API connectors that are available versus we can get a are available versus we can get a are available versus we can get a scoping document, the rough cut of a scoping document, the rough cut of a scoping document, the rough cut of a scoping document for a project like that scoping document for a project like that scoping document for a project like that internally done in a couple of hours now internally done in a couple of hours now internally done in a couple of hours now and then we're all working on okay, how and then we're all working on okay, how and then we're all working on okay, how do we go take the next step? So, it's do we go take the next step? So, it's do we go take the next step? So, it's this force multiplier that's taken some this force multiplier that's taken some this force multiplier that's taken some of those complex projects that would of those complex projects that would of those complex projects that would have taken us several weeks to scope and have taken us several weeks to scope and have taken us several weeks to scope and collapse that. So now we're having collapse that. So now we're having collapse that. So now we're having productive conversations almost productive conversations almost productive conversations almost immediately. immediately. immediately. >> Yeah. As it pertains to u productivity,

>> Yeah. As it pertains to u productivity, >> Yeah. As it pertains to u productivity, I'm hearing a lot about the use of edge I'm hearing a lot about the use of edge I'm hearing a lot about the use of edge AI as opposed to cloud AI. Can you AI as opposed to cloud AI. Can you AI as opposed to cloud AI. Can you clarify the distinction between the two clarify the distinction between the two clarify the distinction between the two and how they used to reduce manual work? and how they used to reduce manual work? and how they used to reduce manual work? >> Sure. Sure. So I mean as you all know >> Sure. Sure. So I mean as you all know >> Sure. Sure. So I mean as you all know right we have our our devices that are right we have our our devices that are right we have our our devices that are in the vehicles or or on the um tractors in the vehicles or or on the um tractors in the vehicles or or on the um tractors wherever and those have compute power in wherever and those have compute power in wherever and those have compute power in them. they have sensors in them and they them. they have sensors in them and they them. they have sensors in them and they have the ability to notify, alert or u have the ability to notify, alert or u have the ability to notify, alert or u speak to the driver or speak to speak to the driver or speak to speak to the driver or speak to whoever's operating that vehicle. Um and whoever's operating that vehicle. Um and whoever's operating that vehicle. Um and so that we call that the edge AI. That's so that we call that the edge AI. That's so that we call that the edge AI. That's the the um edge of the system where it's the the um edge of the system where it's the the um edge of the system where it's actually on the the kind of the road or actually on the the kind of the road or actually on the the kind of the road or the the work site. And you know, if you the the work site. And you know, if you the the work site. And you know, if you want to do things that are going to be want to do things that are going to be want to do things that are going to be timely and notify someone before an timely and notify someone before an timely and notify someone before an incident occurs, like instantly or incident occurs, like instantly or incident occurs, like instantly or within seconds, there's no time for that within seconds, there's no time for that within seconds, there's no time for that to kind of go all the way back to a to kind of go all the way back to a to kind of go all the way back to a server that's in a data center and then server that's in a data center and then server that's in a data center and then come back to the device and then warn come back to the device and then warn come back to the device and then warn the driver. That would take too long. So the driver. That would take too long. So the driver. That would take too long. So everything that we want to do that's everything that we want to do that's everything that we want to do that's very, you know, instantaneous or very, you know, instantaneous or very, you know, instantaneous or reactive has to be on that edge device reactive has to be on that edge device reactive has to be on that edge device and running on the actual hardware in and running on the actual hardware in and running on the actual hardware in the vehicle. And so there's lots of the vehicle. And so there's lots of the vehicle. And so there's lots of features you heard about, you know, features you heard about, you know, features you heard about, you know, collision prevention and lane swerving, collision prevention and lane swerving, collision prevention and lane swerving, other things that our devices need to other things that our devices need to other things that our devices need to process and reconcile on the actual process and reconcile on the actual process and reconcile on the actual hardware that's in the car. Then there hardware that's in the car. Then there hardware that's in the car. Then there are things that can happen after the are things that can happen after the are things that can happen after the fact like AI coaching, right? that fact like AI coaching, right? that fact like AI coaching, right? that doesn't need to be in the moment within doesn't need to be in the moment within doesn't need to be in the moment within seconds. It's typically going to be seconds. It's typically going to be seconds. It's typically going to be something that happens after the shift something that happens after the shift something that happens after the shift is over. That can happen in the cloud is over. That can happen in the cloud is over. That can happen in the cloud and that's what we call cloud AI. So and that's what we call cloud AI. So and that's what we call cloud AI. So those are huge big models, very advanced those are huge big models, very advanced those are huge big models, very advanced AI systems that can use lots of power AI systems that can use lots of power AI systems that can use lots of power and lots of data, but they can't be and lots of data, but they can't be and lots of data, but they can't be real- time, reactive, instantaneous. And real- time, reactive, instantaneous. And real- time, reactive, instantaneous. And so the cloud AI complements the edge AI. so the cloud AI complements the edge AI. so the cloud AI complements the edge AI. They work together and they create this They work together and they create this They work together and they create this unified experience. we use whichever one unified experience. we use whichever one unified experience. we use whichever one is most appropriate for the the product is most appropriate for the the product is most appropriate for the the product that we're trying to to deliver. Um, and that we're trying to to deliver. Um, and that we're trying to to deliver. Um, and you know, I think all of these you know, I think all of these you know, I think all of these technologies are offering ways to save technologies are offering ways to save technologies are offering ways to save time both the edge and cloud-based time both the edge and cloud-based time both the edge and cloud-based systems. Uh, you know, things like um systems. Uh, you know, things like um systems. Uh, you know, things like um processing documents, for example, that processing documents, for example, that processing documents, for example, that doesn't happen have to happen on the doesn't happen have to happen on the doesn't happen have to happen on the edge. It's not a split-second thing. edge. It's not a split-second thing. edge. It's not a split-second thing. It's like se several minutes fine. Um, It's like se several minutes fine. Um, It's like se several minutes fine. Um, and that saves tons of time. And then and that saves tons of time. And then and that saves tons of time. And then you saw some of the things where our you saw some of the things where our you saw some of the things where our dash cam in the keynote is able to tell dash cam in the keynote is able to tell dash cam in the keynote is able to tell people, hey, you know, you've got a um a people, hey, you know, you've got a um a people, hey, you know, you've got a um a good deal on gas coming up in three good deal on gas coming up in three good deal on gas coming up in three miles, you should probably stop. And miles, you should probably stop. And miles, you should probably stop. And that saves money and time for that that saves money and time for that that saves money and time for that driver. So, a combination of all these driver. So, a combination of all these driver. So, a combination of all these things together is what makes our things together is what makes our things together is what makes our platform powerful. The integration of platform powerful. The integration of platform powerful. The integration of all of it, all of it, all of it, >> right? Yes. Saving money is just the

>> right? Yes. Saving money is just the >> right? Yes. Saving money is just the perfect segway to our next topic of perfect segway to our next topic of perfect segway to our next topic of discussion which is cost optimization. discussion which is cost optimization. discussion which is cost optimization. Maybe again for you Michael when Maybe again for you Michael when Maybe again for you Michael when evaluating the ROI of AI investments evaluating the ROI of AI investments evaluating the ROI of AI investments what metrics should senior management what metrics should senior management what metrics should senior management look into? look into? look into? >> Yeah, I mean in my mind like money and >> Yeah, I mean in my mind like money and >> Yeah, I mean in my mind like money and time they're the the main thing we need time they're the the main thing we need time they're the the main thing we need to and safety obviously but like when to and safety obviously but like when to and safety obviously but like when we're talking about you said talk about we're talking about you said talk about we're talking about you said talk about profit and money. So in that context, I profit and money. So in that context, I profit and money. So in that context, I would say time and money are really the would say time and money are really the would say time and money are really the things that we need to optimize for and things that we need to optimize for and things that we need to optimize for and be measuring carefully and accurately. be measuring carefully and accurately. be measuring carefully and accurately. And so um when we launch a new product And so um when we launch a new product And so um when we launch a new product or when we work with one of you, partner or when we work with one of you, partner or when we work with one of you, partner with you, we are obsessive about with you, we are obsessive about with you, we are obsessive about understanding the workflow, the understanding the workflow, the understanding the workflow, the optimizations that we can provide and optimizations that we can provide and optimizations that we can provide and how that translates directly into the how that translates directly into the how that translates directly into the bottom line for companies like you guys. bottom line for companies like you guys. bottom line for companies like you guys. So um that's something we put a lot of So um that's something we put a lot of So um that's something we put a lot of effort into and I would encourage you effort into and I would encourage you effort into and I would encourage you all to take the same view of hey the all to take the same view of hey the all to take the same view of hey the hard results are what matter right if hard results are what matter right if hard results are what matter right if this technology is exciting and fun and this technology is exciting and fun and this technology is exciting and fun and fancy but it doesn't actually save time fancy but it doesn't actually save time fancy but it doesn't actually save time or money then it's probably not worth it or money then it's probably not worth it or money then it's probably not worth it and so what we want to focus on is those and so what we want to focus on is those and so what we want to focus on is those real business metrics we want to move real business metrics we want to move real business metrics we want to move the needle for our customers. the needle for our customers. the needle for our customers. >> Yeah. So at the operational level, >> Yeah. So at the operational level, >> Yeah. So at the operational level, Brandon, are you seeing any real Brandon, are you seeing any real Brandon, are you seeing any real reduction in expenses for things like reduction in expenses for things like reduction in expenses for things like maintenance, fuel consumption, asset maintenance, fuel consumption, asset maintenance, fuel consumption, asset utilization? utilization? utilization? >> Uh so I was probably maintenance is the >> Uh so I was probably maintenance is the >> Uh so I was probably maintenance is the one that's that's we're seeing real one that's that's we're seeing real one that's that's we're seeing real results from and and I also don't want results from and and I also don't want results from and and I also don't want to pollute. There's there's machine to pollute. There's there's machine to pollute. There's there's machine learning and AI and you know they get learning and AI and you know they get learning and AI and you know they get conflated often, but we've been doing a conflated often, but we've been doing a conflated often, but we've been doing a lot of that in in the maintenance space lot of that in in the maintenance space lot of that in in the maintenance space for a long time. So getting out ahead of for a long time. So getting out ahead of for a long time. So getting out ahead of some of these failures and triaging some of these failures and triaging some of these failures and triaging fault codes on which ones are severe and fault codes on which ones are severe and fault codes on which ones are severe and um and starting to prioritize work um and starting to prioritize work um and starting to prioritize work definitely pays dividends. Um but I definitely pays dividends. Um but I definitely pays dividends. Um but I think that you know to Michael's point think that you know to Michael's point think that you know to Michael's point earlier also when we're looking at AI as earlier also when we're looking at AI as earlier also when we're looking at AI as a tool, I'm going to evaluate it from a a tool, I'm going to evaluate it from a a tool, I'm going to evaluate it from a business perspective the same way I business perspective the same way I business perspective the same way I evaluate any other tool I bring into the evaluate any other tool I bring into the evaluate any other tool I bring into the garage. Um and it has to meet the same garage. Um and it has to meet the same garage. Um and it has to meet the same metrics. we have to go, you know, we're metrics. we have to go, you know, we're metrics. we have to go, you know, we're going to go look at um technician time going to go look at um technician time going to go look at um technician time and you know, I I have a master diesel and you know, I I have a master diesel and you know, I I have a master diesel tech. I want him turning wrenches as tech. I want him turning wrenches as tech. I want him turning wrenches as much as possible. Any time he's spending much as possible. Any time he's spending much as possible. Any time he's spending on the laptop or anywhere else in the on the laptop or anywhere else in the on the laptop or anywhere else in the garage is not really leveraging him as a garage is not really leveraging him as a garage is not really leveraging him as a subject matter expert. So, um really subject matter expert. So, um really subject matter expert. So, um really looking at, you know, am I getting time looking at, you know, am I getting time looking at, you know, am I getting time back from from that mechanic? And back from from that mechanic? And back from from that mechanic? And usually I'm going to go reinvest that usually I'm going to go reinvest that usually I'm going to go reinvest that time. So, it can be tough to go put a an time. So, it can be tough to go put a an time. So, it can be tough to go put a an exact dollar value on what was that exact dollar value on what was that exact dollar value on what was that worth. So, we're really just looking at worth. So, we're really just looking at worth. So, we're really just looking at am I reducing nonvalue added time from am I reducing nonvalue added time from am I reducing nonvalue added time from that employee and and increasing his that employee and and increasing his that employee and and increasing his value added time and I view that as as a value added time and I view that as as a value added time and I view that as as a win and we can kind of pencil what is win and we can kind of pencil what is win and we can kind of pencil what is that worth, that worth, that worth, >> right? >> right? >> right? >> What about uh unexpected disruptions >> What about uh unexpected disruptions >> What about uh unexpected disruptions like lost cargo or unplanned downtime? like lost cargo or unplanned downtime? like lost cargo or unplanned downtime? Do you think AI is capable uh in Do you think AI is capable uh in Do you think AI is capable uh in addressing those issues? addressing those issues? addressing those issues? Anyone? Anyone? Anyone? >> Lost cargo for sure. Yeah, I mean we >> Lost cargo for sure. Yeah, I mean we >> Lost cargo for sure. Yeah, I mean we have the asset trackers that we provide have the asset trackers that we provide have the asset trackers that we provide and like if something is lost, we were and like if something is lost, we were and like if something is lost, we were talking about it just before $15,000 talking about it just before $15,000 talking about it just before $15,000 magic wand. magic wand. magic wand. >> Yeah, >> Yeah, >> Yeah, >> some you can ask him about it later. >> some you can ask him about it later. >> some you can ask him about it later. >> I lost a couple recently. >> I lost a couple recently. >> I lost a couple recently. >> He lost a few. >> He lost a few. >> He lost a few. >> Let's not talk. >> Let's not talk. >> Let's not talk. >> So, you know, there's lots of these >> So, you know, there's lots of these >> So, you know, there's lots of these things that um where you know, a simple things that um where you know, a simple things that um where you know, a simple solution, but something powerful can solution, but something powerful can solution, but something powerful can unlock a lot of savings and uh recoup unlock a lot of savings and uh recoup unlock a lot of savings and uh recoup lost cost. lost cost. lost cost. >> Yeah, that's good to know. Yeah. So we >> Yeah, that's good to know. Yeah. So we >> Yeah, that's good to know. Yeah. So we cover productivity and cost cover productivity and cost cover productivity and cost optimization. Let's now turn to another optimization. Let's now turn to another optimization. Let's now turn to another key area where AI plays a critical role

key area where AI plays a critical role key area where AI plays a critical role and that's safety. Maybe we'll start and that's safety. Maybe we'll start and that's safety. Maybe we'll start with you Jamie. Can you perhaps uh give with you Jamie. Can you perhaps uh give with you Jamie. Can you perhaps uh give us some insights as to how the AI dash us some insights as to how the AI dash us some insights as to how the AI dash cams have has uh helped you improve cams have has uh helped you improve cams have has uh helped you improve safety for your teams and drivers? safety for your teams and drivers? safety for your teams and drivers? >> Yeah. So >> Yeah. So >> Yeah. So yesterday in one of my interviews I said yesterday in one of my interviews I said yesterday in one of my interviews I said that the new AI has 15 alerts watching that the new AI has 15 alerts watching that the new AI has 15 alerts watching that this morning there's actually 20. that this morning there's actually 20. that this morning there's actually 20. Um so that's even better. But yeah just Um so that's even better. But yeah just Um so that's even better. But yeah just like for our logistics and that that like for our logistics and that that like for our logistics and that that where we use the the dual facing cameras where we use the the dual facing cameras where we use the the dual facing cameras now with the AI technology. It's um now with the AI technology. It's um now with the AI technology. It's um it's teaching our drivers that the the it's teaching our drivers that the the it's teaching our drivers that the the cameras are more like a co-driver, cameras are more like a co-driver, cameras are more like a co-driver, right? Like it's just a conscious thing right? Like it's just a conscious thing right? Like it's just a conscious thing and that's what you have to like get and that's what you have to like get and that's what you have to like get through to them. And so, yeah, just through to them. And so, yeah, just through to them. And so, yeah, just having that having that having that um that awareness and um that awareness and um that awareness and I love that it like alerts the drivers I love that it like alerts the drivers I love that it like alerts the drivers first. So, they get a few alerts first first. So, they get a few alerts first first. So, they get a few alerts first and of course you can set everything and of course you can set everything and of course you can set everything yourself, right, as a company because we yourself, right, as a company because we yourself, right, as a company because we all have our own standards. So, yeah, we all have our own standards. So, yeah, we all have our own standards. So, yeah, we all have our safety standards, but we all have our safety standards, but we all have our safety standards, but we just didn't know before if they were just didn't know before if they were just didn't know before if they were actually being followed, right? And like actually being followed, right? And like actually being followed, right? And like I said, now we can and we have all these I said, now we can and we have all these I said, now we can and we have all these alerts. So the drivers are we we all alerts. So the drivers are we we all alerts. So the drivers are we we all have a lot of new newer drivers. And so have a lot of new newer drivers. And so have a lot of new newer drivers. And so it's teaching them our older drivers it's teaching them our older drivers it's teaching them our older drivers that have been driving for 25 to 30 that have been driving for 25 to 30 that have been driving for 25 to 30 years, getting them out of their bad years, getting them out of their bad years, getting them out of their bad habits, maybe things that they didn't habits, maybe things that they didn't habits, maybe things that they didn't realize they they did, right? And now realize they they did, right? And now realize they they did, right? And now with these new alerts in the dash cams, with these new alerts in the dash cams, with these new alerts in the dash cams, um, yeah, it's just the training that it um, yeah, it's just the training that it um, yeah, it's just the training that it gives them. And gives them. And gives them. And it's not a tattletail thing. It gives it's not a tattletail thing. It gives it's not a tattletail thing. It gives them the alerts first before it tells a them the alerts first before it tells a them the alerts first before it tells a manager, right? And one thing with the manager, right? And one thing with the manager, right? And one thing with the AI is the more the more the supervisors AI is the more the more the supervisors AI is the more the more the supervisors and managers use it, the more it learns and managers use it, the more it learns and managers use it, the more it learns our company standards and what we our company standards and what we our company standards and what we actually want to see, what we make actually want to see, what we make actually want to see, what we make critical, high, medium risk things. So critical, high, medium risk things. So critical, high, medium risk things. So just like any AI, as we're all learning, just like any AI, as we're all learning, just like any AI, as we're all learning, we have to put in the time and the we have to put in the time and the we have to put in the time and the effort to get the results back to help effort to get the results back to help effort to get the results back to help us with our reporting to help us with us with our reporting to help us with us with our reporting to help us with the alerts and that. So, the alerts and that. So, the alerts and that. So, >> right, I'm really intrigued though as

>> right, I'm really intrigued though as >> right, I'm really intrigued though as the AI system is really getting embedded the AI system is really getting embedded the AI system is really getting embedded into the system. Do we run the risk of into the system. Do we run the risk of into the system. Do we run the risk of uh driver distrust of alert fatigue uh driver distrust of alert fatigue uh driver distrust of alert fatigue happening out in the field? Is that an happening out in the field? Is that an happening out in the field? Is that an issue? You think issue? You think issue? You think >> I I can like speak to that a little bit. >> I I can like speak to that a little bit. >> I I can like speak to that a little bit. I think one of the things we are very I think one of the things we are very I think one of the things we are very careful about is that when we launch one careful about is that when we launch one careful about is that when we launch one of these products or one of these of these products or one of these of these products or one of these features that we are very um diligent features that we are very um diligent features that we are very um diligent about not having false alerts and you about not having false alerts and you about not having false alerts and you heard about that in the keynote as well heard about that in the keynote as well heard about that in the keynote as well that every false alert erodess driver that every false alert erodess driver that every false alert erodess driver trust trust trust >> and can lead to them actually disabling >> and can lead to them actually disabling >> and can lead to them actually disabling or ignoring some of these things. And so or ignoring some of these things. And so or ignoring some of these things. And so we have a very complex system that we have a very complex system that we have a very complex system that processes every event that we detect processes every event that we detect processes every event that we detect from in the vehicle on the edge all the from in the vehicle on the edge all the from in the vehicle on the edge all the way to the cloud like we talked about. way to the cloud like we talked about. way to the cloud like we talked about. And before it reaches a safety manager's And before it reaches a safety manager's And before it reaches a safety manager's dashboard, it's been vetted by all of dashboard, it's been vetted by all of dashboard, it's been vetted by all of these different stages. And the idea is these different stages. And the idea is these different stages. And the idea is that uh by doing that we will prevent that uh by doing that we will prevent that uh by doing that we will prevent that kind of fatigue that you mentioned that kind of fatigue that you mentioned that kind of fatigue that you mentioned and people will trust and people will trust and people will trust >> the technology more. The last thing we >> the technology more. The last thing we >> the technology more. The last thing we want is for someone to disable a want is for someone to disable a want is for someone to disable a life-saving technology because it's life-saving technology because it's life-saving technology because it's annoying, right? We want to make sure annoying, right? We want to make sure annoying, right? We want to make sure that it it only fires when it should or that it it only fires when it should or that it it only fires when it should or as much as possible and provides the as much as possible and provides the as much as possible and provides the benefits to them. benefits to them. benefits to them. >> I see in its current form, so it can it >> I see in its current form, so it can it >> I see in its current form, so it can it can be disabled can be disabled can be disabled >> anything. Our platform is fully >> anything. Our platform is fully >> anything. Our platform is fully configurable, right? So, anybody can configurable, right? So, anybody can configurable, right? So, anybody can choose to to do whatever they want. A choose to to do whatever they want. A choose to to do whatever they want. A fleet manager can configure the system fleet manager can configure the system fleet manager can configure the system how they please. Uh I don't like an how they please. Uh I don't like an how they please. Uh I don't like an individual driver may not be able to do individual driver may not be able to do individual driver may not be able to do that but at the high level uh fleet that but at the high level uh fleet that but at the high level uh fleet manager can configure and disable enable manager can configure and disable enable manager can configure and disable enable whatever they'd like whatever they'd like whatever they'd like >> right >> right >> right >> maybe I shut five off that's why I only >> maybe I shut five off that's why I only >> maybe I shut five off that's why I only thought there was 15 thought there was 15 thought there was 15 turn turn turn >> I I I view it as similar to again any >> I I I view it as similar to again any >> I I I view it as similar to again any kind of new technology we're bringing kind of new technology we're bringing kind of new technology we're bringing into the cab for the drivers. So there's into the cab for the drivers. So there's into the cab for the drivers. So there's the initial push back on the cameras at the initial push back on the cameras at the initial push back on the cameras at all. Right? This is and then you know all. Right? This is and then you know all. Right? This is and then you know you'll have one big exoneration and the you'll have one big exoneration and the you'll have one big exoneration and the whole driver core is asking like hey whole driver core is asking like hey whole driver core is asking like hey when are you installing my camera and uh when are you installing my camera and uh when are you installing my camera and uh so you know it's it takes time you have so you know it's it takes time you have so you know it's it takes time you have to build that trust and I think AI is to build that trust and I think AI is to build that trust and I think AI is going to go the same way right that going to go the same way right that going to go the same way right that they'll learn um what once that we're they'll learn um what once that we're they'll learn um what once that we're not seeing those false positives as not seeing those false positives as not seeing those false positives as often then I think that driver core will often then I think that driver core will often then I think that driver core will learn to accept it the same way we did learn to accept it the same way we did learn to accept it the same way we did with the cameras and other things. I I with the cameras and other things. I I with the cameras and other things. I I think honestly the bigger risk is for us think honestly the bigger risk is for us think honestly the bigger risk is for us maybe at the manager level of trusting maybe at the manager level of trusting maybe at the manager level of trusting it too much too early when it's when it too much too early when it's when it too much too early when it's when it's not ready. Um and and that that's it's not ready. Um and and that that's it's not ready. Um and and that that's the risk of any technology but um but the risk of any technology but um but the risk of any technology but um but yeah I think that yeah I think that yeah I think that >> the drivers will will come around once >> the drivers will will come around once >> the drivers will will come around once they see the benefits of of us they see the benefits of of us they see the benefits of of us continuing to tune out the noise. continuing to tune out the noise. continuing to tune out the noise. >> Right. Yeah. So on the technical side,

>> Right. Yeah. So on the technical side, >> Right. Yeah. So on the technical side, how is building AI for physical how is building AI for physical how is building AI for physical environments different than say building environments different than say building environments different than say building AI for knowledge work applications since AI for knowledge work applications since AI for knowledge work applications since there there's real world consequences? there there's real world consequences? there there's real world consequences? >> Yeah, I mean so you know a lot of the AI >> Yeah, I mean so you know a lot of the AI >> Yeah, I mean so you know a lot of the AI we talk about these days is you know we talk about these days is you know we talk about these days is you know kind of I would say low stakes kind of I would say low stakes kind of I would say low stakes applications you know to like a chatbot applications you know to like a chatbot applications you know to like a chatbot or uh trying to decide where to go for or uh trying to decide where to go for or uh trying to decide where to go for dinner. those kinds of things. A mistake dinner. those kinds of things. A mistake dinner. those kinds of things. A mistake there, you know, might lead to a bad there, you know, might lead to a bad there, you know, might lead to a bad meal, but it's not going to be meal, but it's not going to be meal, but it's not going to be catastrophic for anybody. Uh, in our catastrophic for anybody. Uh, in our catastrophic for anybody. Uh, in our world, we're dealing with much more high world, we're dealing with much more high world, we're dealing with much more high stakes situations. And so, there are stakes situations. And so, there are stakes situations. And so, there are several things we need to kind of do several things we need to kind of do several things we need to kind of do differently there. But at the same time, differently there. But at the same time, differently there. But at the same time, we need to bring to bear the most we need to bring to bear the most we need to bring to bear the most advanced technology we can because it's advanced technology we can because it's advanced technology we can because it's such a important problem. But when we such a important problem. But when we such a important problem. But when we develop technology, we have a much develop technology, we have a much develop technology, we have a much higher bar for validation higher bar for validation higher bar for validation and testing. Uh making sure that we and testing. Uh making sure that we and testing. Uh making sure that we don't just solve for the 80 90% cases, don't just solve for the 80 90% cases, don't just solve for the 80 90% cases, but the 99.99999% but the 99.99999% but the 99.99999% cases and we have a very rigorous system cases and we have a very rigorous system cases and we have a very rigorous system to monitor and ensure nothing's going to monitor and ensure nothing's going to monitor and ensure nothing's going wrong at any time. And the combination wrong at any time. And the combination wrong at any time. And the combination of all of that means that we can deliver of all of that means that we can deliver of all of that means that we can deliver products fast and safely. And that's products fast and safely. And that's products fast and safely. And that's really what we aim to do. Um, never really what we aim to do. Um, never really what we aim to do. Um, never sacrificing any element of safety but sacrificing any element of safety but sacrificing any element of safety but moving as quickly as we can with our moving as quickly as we can with our moving as quickly as we can with our development. development. development. >> Yeah. So for our next segment, I'd like

>> Yeah. So for our next segment, I'd like >> Yeah. So for our next segment, I'd like to touch on cross industry impacts. But to touch on cross industry impacts. But to touch on cross industry impacts. But before I do that, maybe just uh to do a before I do that, maybe just uh to do a before I do that, maybe just uh to do a quick survey, show of hands if anyone quick survey, show of hands if anyone quick survey, show of hands if anyone here is not involved with fleet here is not involved with fleet here is not involved with fleet operations. Hands up. operations. Hands up. operations. Hands up. No one. No one. No one. >> Wow. Let the record show nobody raised >> Wow. Let the record show nobody raised >> Wow. Let the record show nobody raised their hand. their hand. their hand. >> So, everyone here is involved with fleet >> So, everyone here is involved with fleet >> So, everyone here is involved with fleet operations. Okay, that's cool. So, we operations. Okay, that's cool. So, we operations. Okay, that's cool. So, we can make this really quick because this can make this really quick because this can make this really quick because this is just to indulge in my curiosity is just to indulge in my curiosity is just to indulge in my curiosity because I'm in the news and media uh uh because I'm in the news and media uh uh because I'm in the news and media uh uh sector and I'm really intrigued by sector and I'm really intrigued by sector and I'm really intrigued by everything I've I've uh uncovered thus everything I've I've uh uncovered thus everything I've I've uh uncovered thus far through this conference through far through this conference through far through this conference through going through uh the numerous case going through uh the numerous case going through uh the numerous case studies that you have and talking with studies that you have and talking with studies that you have and talking with folks in motive. So in my mind uh the folks in motive. So in my mind uh the folks in motive. So in my mind uh the the motive product offering really the motive product offering really the motive product offering really hinges on integration and automation as hinges on integration and automation as hinges on integration and automation as mentioned during the keynote this mentioned during the keynote this mentioned during the keynote this morning and that's something that's morning and that's something that's morning and that's something that's applicable across different segment of applicable across different segment of applicable across different segment of the industries but I I put up a list the industries but I I put up a list the industries but I I put up a list that basically look at all the paradigm that basically look at all the paradigm that basically look at all the paradigm shifts that are possible with uh AI and shifts that are possible with uh AI and shifts that are possible with uh AI and automation in general. So here's a list automation in general. So here's a list automation in general. So here's a list that I've put together. Maybe you guys that I've put together. Maybe you guys that I've put together. Maybe you guys can complement it. So I only have six on can complement it. So I only have six on can complement it. So I only have six on my list. It feels insufficient because my list. It feels insufficient because my list. It feels insufficient because typically for a list there should be at typically for a list there should be at typically for a list there should be at least seven to it or 12. least seven to it or 12. least seven to it or 12. >> Seven. >> Seven. >> Seven. >> Yeah, it sounds a lot better. But number >> Yeah, it sounds a lot better. But number >> Yeah, it sounds a lot better. But number one, obviously you got to move uh the one, obviously you got to move uh the one, obviously you got to move uh the system from being fragmented to fully system from being fragmented to fully system from being fragmented to fully integrated. That's one. Number two, it integrated. That's one. Number two, it integrated. That's one. Number two, it must be you must move the workflow from must be you must move the workflow from must be you must move the workflow from one that is manual to fully automated. one that is manual to fully automated. one that is manual to fully automated. Number three, the process has to be uh Number three, the process has to be uh Number three, the process has to be uh proactive rather than reactive. Number proactive rather than reactive. Number proactive rather than reactive. Number four, you need to be able to measure four, you need to be able to measure four, you need to be able to measure precise incoming data as opposed to precise incoming data as opposed to precise incoming data as opposed to vague subjective uh data. There's the vague subjective uh data. There's the vague subjective uh data. There's the adage that you cannot manage what you adage that you cannot manage what you adage that you cannot manage what you don't measure. So don't measure. So don't measure. So >> that falls under this. Number six, as it >> that falls under this. Number six, as it >> that falls under this. Number six, as it pertains to administrative work, you pertains to administrative work, you pertains to administrative work, you need to try to have as much real time need to try to have as much real time need to try to have as much real time processing as possible as opposed to processing as possible as opposed to processing as possible as opposed to batch or deferred processing. And number batch or deferred processing. And number batch or deferred processing. And number six, as opposed to having, you know, a six, as opposed to having, you know, a six, as opposed to having, you know, a stepbystep uh uh process, you need to stepbystep uh uh process, you need to stepbystep uh uh process, you need to have sort of like an end to end agentic have sort of like an end to end agentic have sort of like an end to end agentic process in place. So maybe from your process in place. So maybe from your process in place. So maybe from your perspective, Michael, is there anything perspective, Michael, is there anything perspective, Michael, is there anything that I should add that's applicable to that I should add that's applicable to that I should add that's applicable to other, you know, industries out there? other, you know, industries out there? other, you know, industries out there? >> Well, I think what I've what I've been >> Well, I think what I've what I've been >> Well, I think what I've what I've been kind of surprised to find is how similar kind of surprised to find is how similar kind of surprised to find is how similar these problems are across different these problems are across different these problems are across different industries and different applications, industries and different applications, industries and different applications, even within fleet management. You know, even within fleet management. You know, even within fleet management. You know, you heard about some of like the waste you heard about some of like the waste you heard about some of like the waste management applications where we were management applications where we were management applications where we were able to use models which frankly are able to use models which frankly are able to use models which frankly are like very very similar to the models we like very very similar to the models we like very very similar to the models we use in the vehicle, you know, uh, dash use in the vehicle, you know, uh, dash use in the vehicle, you know, uh, dash cams and we're able to detect overages cams and we're able to detect overages cams and we're able to detect overages in waste management. We're able to in waste management. We're able to in waste management. We're able to detect contaminants. We're able to detect contaminants. We're able to detect contaminants. We're able to detect missing PPE on work sites. All of detect missing PPE on work sites. All of detect missing PPE on work sites. All of this using basically the same six this using basically the same six this using basically the same six principles that you mentioned shared principles that you mentioned shared principles that you mentioned shared across all these different applications, across all these different applications, across all these different applications, use cases and features. And that's been use cases and features. And that's been use cases and features. And that's been remarkable to me to see. I didn't expect remarkable to me to see. I didn't expect remarkable to me to see. I didn't expect it to be so generalizable, these it to be so generalizable, these it to be so generalizable, these principles in the way that this works, principles in the way that this works, principles in the way that this works, but really there are so many but really there are so many but really there are so many similarities between these different similarities between these different similarities between these different physical problems in the world. And so physical problems in the world. And so physical problems in the world. And so what we've been able to do is leverage what we've been able to do is leverage what we've been able to do is leverage like basically the same technology to like basically the same technology to like basically the same technology to solve all of those problems. and the six solve all of those problems. and the six solve all of those problems. and the six items you mentioned, they go into each items you mentioned, they go into each items you mentioned, they go into each of those different um applications. So, of those different um applications. So, of those different um applications. So, uh that's been my observation. Very uh that's been my observation. Very uh that's been my observation. Very interesting. interesting. interesting. >> Very good. Jamie, you have anything to >> Very good. Jamie, you have anything to >> Very good. Jamie, you have anything to add to this? Anything that's add to this? Anything that's add to this? Anything that's transferable to other industries from transferable to other industries from transferable to other industries from your experience? your experience? your experience? it. I mean, working in Canada, we have very harsh working in Canada, we have very harsh weather and that and so I think um weather and that and so I think um weather and that and so I think um Motive has come a long way in their Motive has come a long way in their Motive has come a long way in their asset trackers and that that it'll work asset trackers and that that it'll work asset trackers and that that it'll work in our minus 40 temperatures in our minus 40 temperatures in our minus 40 temperatures up there. Uh yeah, I mean up there. Uh yeah, I mean up there. Uh yeah, I mean we're we're all going to have something we're we're all going to have something we're we're all going to have something different, right, that we have in our different, right, that we have in our different, right, that we have in our industries or with our fleets or industries or with our fleets or industries or with our fleets or whatever our standards and uh it it's whatever our standards and uh it it's whatever our standards and uh it it's yeah, following those rules, there's yeah, following those rules, there's yeah, following those rules, there's just adjustments that can be made and it just adjustments that can be made and it just adjustments that can be made and it they're easily like you can easily do they're easily like you can easily do they're easily like you can easily do it, it, it, >> right? I think I've, you know, had the >> right? I think I've, you know, had the >> right? I think I've, you know, had the I've been lucky enough to work with a I've been lucky enough to work with a I've been lucky enough to work with a lot of different fleets in my in my lot of different fleets in my in my lot of different fleets in my in my career and uh you like everybody's fleet career and uh you like everybody's fleet career and uh you like everybody's fleet is a little bit different and I always is a little bit different and I always is a little bit different and I always joke that you know you're you're a joke that you know you're you're a joke that you know you're you're a special snowflake just like everyone special snowflake just like everyone special snowflake just like everyone else, right? Um and you know trucks are else, right? Um and you know trucks are else, right? Um and you know trucks are trucks like there's a lot of things that trucks like there's a lot of things that trucks like there's a lot of things that are going to be applicable effectively are going to be applicable effectively are going to be applicable effectively across the board and then what I do across the board and then what I do across the board and then what I do think the AI is going to allow us to do think the AI is going to allow us to do think the AI is going to allow us to do is some of those industry specific niche is some of those industry specific niche is some of those industry specific niche use cases that we want to go do. um use cases that we want to go do. um use cases that we want to go do. um we're going to be able to go dig in on we're going to be able to go dig in on we're going to be able to go dig in on that, right? I want to I want to that, right? I want to I want to that, right? I want to I want to automate asbuilts for us after we're automate asbuilts for us after we're automate asbuilts for us after we're done with a project and those kinds of done with a project and those kinds of done with a project and those kinds of things. We can go dig into that, but things. We can go dig into that, but things. We can go dig into that, but there's a huge amount of the the there's a huge amount of the the there's a huge amount of the the groundwork that we're laying right now groundwork that we're laying right now groundwork that we're laying right now as an industry that we're going to be as an industry that we're going to be as an industry that we're going to be able to then go lift and shift and and able to then go lift and shift and and able to then go lift and shift and and will give us, you know, a head start on will give us, you know, a head start on will give us, you know, a head start on some of these other, you know, kind of some of these other, you know, kind of some of these other, you know, kind of more siloed projects. But there's still more siloed projects. But there's still more siloed projects. But there's still a ton to ton of work to go do and value a ton to ton of work to go do and value a ton to ton of work to go do and value to add that's going to be applicable, I to add that's going to be applicable, I to add that's going to be applicable, I think, to basically everybody. think, to basically everybody. think, to basically everybody. >> Mhm. Yeah. But beyond all this hype

>> Mhm. Yeah. But beyond all this hype >> Mhm. Yeah. But beyond all this hype surrounding AI and automation, do you surrounding AI and automation, do you surrounding AI and automation, do you think there's a specific area where AI think there's a specific area where AI think there's a specific area where AI has not yet lived up to its expectation? Well, I mean then again with the the Well, I mean then again with the the keynote maybe put a pin in some of my keynote maybe put a pin in some of my keynote maybe put a pin in some of my original thought of how I was going to original thought of how I was going to original thought of how I was going to answer this question, but um the uh I answer this question, but um the uh I answer this question, but um the uh I would say for me at least that one of would say for me at least that one of would say for me at least that one of the pain points that we still deal with the pain points that we still deal with the pain points that we still deal with a lot is on the logs and and so you know a lot is on the logs and and so you know a lot is on the logs and and so you know having AI be able to detect when a having AI be able to detect when a having AI be able to detect when a driver didn't log in or didn't log out driver didn't log in or didn't log out driver didn't log in or didn't log out or just those kinds of things and and or just those kinds of things and and or just those kinds of things and and not just having a we're still having to not just having a we're still having to not just having a we're still having to have a person go and and validate logs have a person go and and validate logs have a person go and and validate logs versus just flagging versus just flagging versus just flagging not just there's something that looks not just there's something that looks not just there's something that looks like it's wrong with this log, but like it's wrong with this log, but like it's wrong with this log, but here's what I would do to fix it. Um, I here's what I would do to fix it. Um, I here's what I would do to fix it. Um, I think that would be a really great use think that would be a really great use think that would be a really great use case that we haven't quite got there case that we haven't quite got there case that we haven't quite got there yet. But also, I know it was discussed, yet. But also, I know it was discussed, yet. But also, I know it was discussed, so um, it's coming. so um, it's coming. so um, it's coming. >> All right. Okay. I think we have 15 >> All right. Okay. I think we have 15 >> All right. Okay. I think we have 15 minutes left. Um, we're saving the, uh, minutes left. Um, we're saving the, uh, minutes left. Um, we're saving the, uh, the best for the last. So, the last the best for the last. So, the last the best for the last. So, the last section here will be on actionable section here will be on actionable section here will be on actionable takeaways for everyone in the room. So takeaways for everyone in the room. So takeaways for everyone in the room. So what what do you guys think is the best

what what do you guys think is the best what what do you guys think is the best place for folks to start in using AI if place for folks to start in using AI if place for folks to start in using AI if they're new to this journey? What would they're new to this journey? What would they're new to this journey? What would be the first place that they should be the first place that they should be the first place that they should start? start? start? >> Look at me. Okay. Um well, I think what >> Look at me. Okay. Um well, I think what >> Look at me. Okay. Um well, I think what you heard in the keynote, I'm going to you heard in the keynote, I'm going to you heard in the keynote, I'm going to go back to that again. I think that go back to that again. I think that go back to that again. I think that Atlas tool that we're la that we've Atlas tool that we're la that we've Atlas tool that we're la that we've launched is an incredible AI assistant launched is an incredible AI assistant launched is an incredible AI assistant for you all. Uh if you're already on for you all. Uh if you're already on for you all. Uh if you're already on Motive Platform, it's got access to all Motive Platform, it's got access to all Motive Platform, it's got access to all of your data, your context, everything. of your data, your context, everything. of your data, your context, everything. uh that'll be a gateway I think into uh that'll be a gateway I think into uh that'll be a gateway I think into this world and I think from there you this world and I think from there you this world and I think from there you can connect it to claude like you saw or can connect it to claude like you saw or can connect it to claude like you saw or or chat GPT or whatever um and then you or chat GPT or whatever um and then you or chat GPT or whatever um and then you can start to pull information from can start to pull information from can start to pull information from everything right the web your data your everything right the web your data your everything right the web your data your email your uh your your system so um email your uh your your system so um email your uh your your system so um that is a very compelling place I would that is a very compelling place I would that is a very compelling place I would start from and I think that will take start from and I think that will take start from and I think that will take you on the journey and lead you to new you on the journey and lead you to new you on the journey and lead you to new and exciting applications automations and exciting applications automations and exciting applications automations things like that which um frankly I things like that which um frankly I things like that which um frankly I think probably I can't even come up with think probably I can't even come up with think probably I can't even come up with honestly without knowing your your honestly without knowing your your honestly without knowing your your business but you guys should be able to business but you guys should be able to business but you guys should be able to do that and we're giving you now the do that and we're giving you now the do that and we're giving you now the tools in your hands. tools in your hands. tools in your hands. >> On the flip side of the coin though, >> On the flip side of the coin though, >> On the flip side of the coin though, what are some of the mistakes that folks what are some of the mistakes that folks what are some of the mistakes that folks should avoid? should avoid? should avoid? >> Well, I think one thing that AI can't do >> Well, I think one thing that AI can't do >> Well, I think one thing that AI can't do no matter what is take responsibility no matter what is take responsibility no matter what is take responsibility and I think we can all agree that there and I think we can all agree that there and I think we can all agree that there is no AI today that can take is no AI today that can take is no AI today that can take responsibility if something goes wrong. responsibility if something goes wrong. responsibility if something goes wrong. if something goes wrong, one of us is if something goes wrong, one of us is if something goes wrong, one of us is going to be on the hook. And so the going to be on the hook. And so the going to be on the hook. And so the mistake I see is people not mistake I see is people not mistake I see is people not understanding that and maybe trusting understanding that and maybe trusting understanding that and maybe trusting too much or not checking or not too much or not checking or not too much or not checking or not double-checking things. And I don't double-checking things. And I don't double-checking things. And I don't think we're in a place where that is think we're in a place where that is think we're in a place where that is we're ready for that today. And so if we're ready for that today. And so if we're ready for that today. And so if we're in a environment or an application we're in a environment or an application we're in a environment or an application where you know safety and critical where you know safety and critical where you know safety and critical things can occur, uh it's on us to things can occur, uh it's on us to things can occur, uh it's on us to doublech checkck things that come from doublech checkck things that come from doublech checkck things that come from the AI. And so I think it's a great the AI. And so I think it's a great the AI. And so I think it's a great tool. I'm like obviously 100% AI bought tool. I'm like obviously 100% AI bought tool. I'm like obviously 100% AI bought in, but I also want to be honest about in, but I also want to be honest about in, but I also want to be honest about the fact that, you know, it's not the fact that, you know, it's not the fact that, you know, it's not perfect all of the time. And I think we perfect all of the time. And I think we perfect all of the time. And I think we all have seen that happen. And so we're all have seen that happen. And so we're all have seen that happen. And so we're going to uh be careful when we use it. going to uh be careful when we use it. going to uh be careful when we use it. >> Right. Brendan, Jimmy, anything to add >> Right. Brendan, Jimmy, anything to add >> Right. Brendan, Jimmy, anything to add to that? to that? to that? >> No, I agree. We can't just focus on the >> No, I agree. We can't just focus on the >> No, I agree. We can't just focus on the the false, but also focus on the the false, but also focus on the the false, but also focus on the positives. And Exactly. If if you're positives. And Exactly. If if you're positives. And Exactly. If if you're going to start integrating more into it, going to start integrating more into it, going to start integrating more into it, just know it does work with a lot of the just know it does work with a lot of the just know it does work with a lot of the programs and systems that we have in programs and systems that we have in programs and systems that we have in place already. Like that's the purpose place already. Like that's the purpose place already. Like that's the purpose of the AI is to integrate it all of the AI is to integrate it all of the AI is to integrate it all together. And it and it does work and it together. And it and it does work and it together. And it and it does work and it helps you actually see maybe some of the helps you actually see maybe some of the helps you actually see maybe some of the the flaws you had before too, right? the flaws you had before too, right? the flaws you had before too, right? Like it's just that extra tool to help Like it's just that extra tool to help Like it's just that extra tool to help you and but again you have to work with you and but again you have to work with you and but again you have to work with it. So don't Exactly. Don't just rely on it. So don't Exactly. Don't just rely on it. So don't Exactly. Don't just rely on it to pick up everything. You still have it to pick up everything. You still have it to pick up everything. You still have to do the monitoring. You still have to to do the monitoring. You still have to to do the monitoring. You still have to go in and work with it so that it helps go in and work with it so that it helps go in and work with it so that it helps you grow and gives you all the proper you grow and gives you all the proper you grow and gives you all the proper data and stuff. So, it's uh it's a data and stuff. So, it's uh it's a data and stuff. So, it's uh it's a growing process, but AI is just it's growing process, but AI is just it's growing process, but AI is just it's it's taking over and we just have to all it's taking over and we just have to all it's taking over and we just have to all get into it and work with it and just get into it and work with it and just get into it and work with it and just integrate it, integrate it, integrate it, >> right? >> right? >> right? >> And then the the only thing I'd add is >> And then the the only thing I'd add is >> And then the the only thing I'd add is just the back to what I mentioned just the back to what I mentioned just the back to what I mentioned earlier on the data integrity, right? earlier on the data integrity, right? earlier on the data integrity, right? It's like anything data related, you're It's like anything data related, you're It's like anything data related, you're garbage in, garbage out in in a way. And garbage in, garbage out in in a way. And garbage in, garbage out in in a way. And um so really owning your data, know what um so really owning your data, know what um so really owning your data, know what data you're collecting, make sure that data you're collecting, make sure that data you're collecting, make sure that it's accurate, have it keyed into all it's accurate, have it keyed into all it's accurate, have it keyed into all your systems, accurate, the vehicles your systems, accurate, the vehicles your systems, accurate, the vehicles have all their spec information in have all their spec information in have all their spec information in there. And you know, the AI can help you there. And you know, the AI can help you there. And you know, the AI can help you clean it up, but but also, you know, clean it up, but but also, you know, clean it up, but but also, you know, just having that good basis of data for just having that good basis of data for just having that good basis of data for it to work with is is going to be it to work with is is going to be it to work with is is going to be critical to your success. critical to your success. critical to your success. >> Yeah. >> Yeah. >> Yeah. >> Very interesting. Last question for the

>> Very interesting. Last question for the >> Very interesting. Last question for the day. What do you think the next three to day. What do you think the next three to day. What do you think the next three to five years is going to look like for five years is going to look like for five years is going to look like for fleets? fleets? fleets? >> Yeah, I mean, I think what what I'm most >> Yeah, I mean, I think what what I'm most >> Yeah, I mean, I think what what I'm most excited about in the next several years excited about in the next several years excited about in the next several years is the the safety side. Frankly, I think is the the safety side. Frankly, I think is the the safety side. Frankly, I think the the automations, the workflows, the the automations, the workflows, the the automations, the workflows, that's that's all great, but really what that's that's all great, but really what that's that's all great, but really what we're here for is to make sure that the we're here for is to make sure that the we're here for is to make sure that the roads get safer and that people get home roads get safer and that people get home roads get safer and that people get home to their families. And I think what I I to their families. And I think what I I to their families. And I think what I I mentioned earlier is that there's been mentioned earlier is that there's been mentioned earlier is that there's been incredible advances in AI models even in incredible advances in AI models even in incredible advances in AI models even in the last several months, but they the last several months, but they the last several months, but they haven't translated yet into this haven't translated yet into this haven't translated yet into this application that we are looking at. I application that we are looking at. I application that we are looking at. I think that's going to take a few years, think that's going to take a few years, think that's going to take a few years, right? And there's a series of right? And there's a series of right? And there's a series of challenges. And I think Motive is challenges. And I think Motive is challenges. And I think Motive is incredibly well positioned to be a incredibly well positioned to be a incredibly well positioned to be a leader in that uh transformation, but leader in that uh transformation, but leader in that uh transformation, but it's still going to take some time. And it's still going to take some time. And it's still going to take some time. And I think over the next several years, I think over the next several years, I think over the next several years, what you will see is improved predictive what you will see is improved predictive what you will see is improved predictive collision avoidance capabilities. So we collision avoidance capabilities. So we collision avoidance capabilities. So we reduce the number of accidents and we reduce the number of accidents and we reduce the number of accidents and we prove that reduce the number of severe prove that reduce the number of severe prove that reduce the number of severe accidents. will have better alerting accidents. will have better alerting accidents. will have better alerting when things are going wrong in the when things are going wrong in the when things are going wrong in the vehicle and automatically notify you or vehicle and automatically notify you or vehicle and automatically notify you or the driver. Um, and things will just the driver. Um, and things will just the driver. Um, and things will just become smoother and easier to use. And become smoother and easier to use. And become smoother and easier to use. And so that's basically the the trend I'm so that's basically the the trend I'm so that's basically the the trend I'm excited about over the next several excited about over the next several excited about over the next several years. Um, we'll see what happens. years. Um, we'll see what happens. years. Um, we'll see what happens. >> Yeah. >> Yeah. >> Yeah. >> Brandon, any future predictions? >> Brandon, any future predictions? >> Brandon, any future predictions? >> I I I view it, like we said earlier, as >> I I I view it, like we said earlier, as >> I I I view it, like we said earlier, as a force multiplier. So, you know, I I a force multiplier. So, you know, I I a force multiplier. So, you know, I I tend to I don't want to fight a war on tend to I don't want to fight a war on tend to I don't want to fight a war on 10 fronts. I'm going to fight a war on 10 fronts. I'm going to fight a war on 10 fronts. I'm going to fight a war on two fronts on any given year, right? two fronts on any given year, right? two fronts on any given year, right? We're going to go pick our projects and We're going to go pick our projects and We're going to go pick our projects and go lean in and get them done. And I go lean in and get them done. And I go lean in and get them done. And I think that maybe that'll allow us to do think that maybe that'll allow us to do think that maybe that'll allow us to do three. I think um so I I I think our three. I think um so I I I think our three. I think um so I I I think our goals are going to stay the same, but goals are going to stay the same, but goals are going to stay the same, but we're just going to have uh a lot of new we're just going to have uh a lot of new we're just going to have uh a lot of new tools u to react to the change that's tools u to react to the change that's tools u to react to the change that's going to be coming. going to be coming. going to be coming. >> Totally. Anything Jamie? Do you want to go live? Anything Jamie? Do you want to go live? >> No, I just agree like I I want to get >> No, I just agree like I I want to get >> No, I just agree like I I want to get motive more into into our fleet and just motive more into into our fleet and just motive more into into our fleet and just um Exactly. the safety side that's um Exactly. the safety side that's um Exactly. the safety side that's that's huge for us and uh that's huge for us and uh that's huge for us and uh yeah, just helping the drivers. So, yeah, just helping the drivers. So, yeah, just helping the drivers. So, >> okay, very good. Thank you. So, we're >> okay, very good. Thank you. So, we're >> okay, very good. Thank you. So, we're going to open it up now to the audience.

going to open it up now to the audience. going to open it up now to the audience. So, if you have any questions, uh we're So, if you have any questions, uh we're So, if you have any questions, uh we're going to bring the mic to you. So going to bring the mic to you. So going to bring the mic to you. So questions anyone? We have 10 minutes uh questions anyone? We have 10 minutes uh questions anyone? We have 10 minutes uh left for the session. left for the session. left for the session. Okay, we have one here. You are talking about predicting the You are talking about predicting the trajectory for example when you identify trajectory for example when you identify trajectory for example when you identify something coming in the way of a truck. something coming in the way of a truck. something coming in the way of a truck. Uh I understand that you are making Uh I understand that you are making Uh I understand that you are making different scenarios. Would you be able different scenarios. Would you be able different scenarios. Would you be able in the future also to react a couple of in the future also to react a couple of in the future also to react a couple of to predict a couple of scenarios of a to predict a couple of scenarios of a to predict a couple of scenarios of a driver based on the dynamic of a truck? driver based on the dynamic of a truck? driver based on the dynamic of a truck? For example, you see that somebody is For example, you see that somebody is For example, you see that somebody is coming one way. So you can anticipate coming one way. So you can anticipate coming one way. So you can anticipate that the driver will veer or go left or that the driver will veer or go left or that the driver will veer or go left or right, but it depends very much of the right, but it depends very much of the right, but it depends very much of the speed of the trucks and things like speed of the trucks and things like speed of the trucks and things like that. So would you be able to have that. So would you be able to have that. So would you be able to have something like a interactive scenario something like a interactive scenario something like a interactive scenario that would allow you to further improve that would allow you to further improve that would allow you to further improve your prediction of what should be done? your prediction of what should be done? your prediction of what should be done? >> Yes, I think I understand. Let me let me >> Yes, I think I understand. Let me let me >> Yes, I think I understand. Let me let me try and repeat uh like the question so I try and repeat uh like the question so I try and repeat uh like the question so I understand. So if we can predict what is understand. So if we can predict what is understand. So if we can predict what is going to happen maybe in the other going to happen maybe in the other going to happen maybe in the other actors in the scene but can we also actors in the scene but can we also actors in the scene but can we also incorporate incorporate incorporate >> knowledge about what the driver might do >> knowledge about what the driver might do >> knowledge about what the driver might do >> as a reaction to what's going to happen >> as a reaction to what's going to happen >> as a reaction to what's going to happen >> and characteristic of a truck. I think >> and characteristic of a truck. I think >> and characteristic of a truck. I think uh I mean the short answer is yes. We it uh I mean the short answer is yes. We it uh I mean the short answer is yes. We it can be done. I think the way these can be done. I think the way these can be done. I think the way these models work is that it's all about data models work is that it's all about data models work is that it's all about data and it comes back to this point that if and it comes back to this point that if and it comes back to this point that if you have data and you can teach the AI you have data and you can teach the AI you have data and you can teach the AI about these scenarios, if you have about these scenarios, if you have about these scenarios, if you have enough examples of drivers avoiding enough examples of drivers avoiding enough examples of drivers avoiding accidents or swerving out of the way of accidents or swerving out of the way of accidents or swerving out of the way of something, then the models will learn to something, then the models will learn to something, then the models will learn to anticipate that and incorporate that anticipate that and incorporate that anticipate that and incorporate that into their reasoning. And so really what into their reasoning. And so really what into their reasoning. And so really what it comes down to is do you have examples it comes down to is do you have examples it comes down to is do you have examples of this in your data set? And this is of this in your data set? And this is of this in your data set? And this is where I think Motive is really well where I think Motive is really well where I think Motive is really well positioned with millions of devices or a positioned with millions of devices or a positioned with millions of devices or a million plus devices out there and million plus devices out there and million plus devices out there and millions of vehicles. We're able to millions of vehicles. We're able to millions of vehicles. We're able to detect and analyze data from hundreds of detect and analyze data from hundreds of detect and analyze data from hundreds of thousands of different events that are thousands of different events that are thousands of different events that are near collisions, collisions or dangerous near collisions, collisions or dangerous near collisions, collisions or dangerous scenarios. And so in some of those there scenarios. And so in some of those there scenarios. And so in some of those there will be driver reactions and other ones will be driver reactions and other ones will be driver reactions and other ones there won't and the AI will learn that there won't and the AI will learn that there won't and the AI will learn that in this kind of environment in this in this kind of environment in this in this kind of environment in this scenario this is what's likely to happen scenario this is what's likely to happen scenario this is what's likely to happen and so I need to alert this or I need to and so I need to alert this or I need to and so I need to alert this or I need to not alert and that's how we would not alert and that's how we would not alert and that's how we would incorporate that kind of knowledge into incorporate that kind of knowledge into incorporate that kind of knowledge into the system. So definitely possible the system. So definitely possible the system. So definitely possible dependent on having data is what I would dependent on having data is what I would dependent on having data is what I would say say say >> and I'm not saying that there will be >> and I'm not saying that there will be >> and I'm not saying that there will be convergence between what you're doing convergence between what you're doing convergence between what you're doing and autonomous driving but there are and autonomous driving but there are and autonomous driving but there are simil similar similarities so to speak simil similar similarities so to speak simil similar similarities so to speak and and and I I think there is room there is I I think there is room there is I I think there is room there is obviously room for both right because obviously room for both right because obviously room for both right because what you're doing is for 99.99% of the what you're doing is for 99.99% of the what you're doing is for 99.99% of the trucks and autonomous is.1.1% trucks and autonomous is.1.1% trucks and autonomous is.1.1% but there should be more but there should be more but there should be more >> uh integration so to speak what you're >> uh integration so to speak what you're >> uh integration so to speak what you're doing is more like uh preventive because doing is more like uh preventive because doing is more like uh preventive because it will teach people and to the M is it will teach people and to the M is it will teach people and to the M is just it just happens just it just happens just it just happens >> definitely. Yeah, good point. >> definitely. Yeah, good point. >> definitely. Yeah, good point. >> Okay, we have questions back. >> Excuse me. >> Excuse me. >> Okay, so my question is when we first >> Okay, so my question is when we first >> Okay, so my question is when we first set up Motive, we have independent set up Motive, we have independent set up Motive, we have independent contracts with different cities. So we contracts with different cities. So we contracts with different cities. So we couldn't put everything into one system couldn't put everything into one system couldn't put everything into one system to start out. We are working on a merger to start out. We are working on a merger to start out. We are working on a merger now. But my question is is would the now. But my question is is would the now. But my question is is would the different atlases be able to speak to different atlases be able to speak to different atlases be able to speak to each other so that we can get reporting each other so that we can get reporting each other so that we can get reporting out in that way not necessarily have to out in that way not necessarily have to out in that way not necessarily have to merge? merge? merge? >> That's a great question. I unfortunately >> That's a great question. I unfortunately >> That's a great question. I unfortunately don't know the answer to that. So I'm don't know the answer to that. So I'm don't know the answer to that. So I'm going to defer you to like your account going to defer you to like your account going to defer you to like your account account manager or someone who can account manager or someone who can account manager or someone who can >> probably find out the the specifics of >> probably find out the the specifics of >> probably find out the the specifics of that. That's it's a bit of an edge case. that. That's it's a bit of an edge case. that. That's it's a bit of an edge case. So So So >> yeah. Yeah, that's perfect. Like I said, I was Yeah, that's perfect. Like I said, I was just like, "Oh, but wait a minute. If just like, "Oh, but wait a minute. If just like, "Oh, but wait a minute. If they all have atlases, could they talk they all have atlases, could they talk they all have atlases, could they talk to each other?" Right. to each other?" Right. to each other?" Right. >> We'll get back to you. >> We'll get back to you. >> We'll get back to you. >> Okay. Anyone else? >> Yeah. How do you foresee AI impacting >> Yeah. How do you foresee AI impacting headcount? I know we see that a lot in headcount? I know we see that a lot in headcount? I know we see that a lot in the news. currently with uh especially the news. currently with uh especially the news. currently with uh especially the tech industry. Um do you foresee the tech industry. Um do you foresee the tech industry. Um do you foresee that being a big impact to fleet that being a big impact to fleet that being a big impact to fleet operations? operations? operations? >> Well, I mean the headcount impacts that >> Well, I mean the headcount impacts that >> Well, I mean the headcount impacts that I'm seeing right now are in my field of I'm seeing right now are in my field of I'm seeing right now are in my field of engineering. So that's where most of engineering. So that's where most of engineering. So that's where most of this is happening today uh that I'm this is happening today uh that I'm this is happening today uh that I'm observing. I'm curious what you guys observing. I'm curious what you guys observing. I'm curious what you guys think about impacts in your industry. I think about impacts in your industry. I think about impacts in your industry. I don't really have visibility into that. don't really have visibility into that. don't really have visibility into that. So So So >> yeah, at least right now for me, I'm not >> yeah, at least right now for me, I'm not >> yeah, at least right now for me, I'm not looking at it impacting headcount looking at it impacting headcount looking at it impacting headcount significantly. Um I I like I said, I significantly. Um I I like I said, I significantly. Um I I like I said, I view it more as I'm getting more value view it more as I'm getting more value view it more as I'm getting more value out of the employees that I have. I out of the employees that I have. I out of the employees that I have. I don't know if right now that's changing don't know if right now that's changing don't know if right now that's changing how many I need. I I think it's just how many I need. I I think it's just how many I need. I I think it's just changing our effectiveness as an changing our effectiveness as an changing our effectiveness as an organization. Um so when you think about organization. Um so when you think about organization. Um so when you think about a mechanic or a fleet manager out in the a mechanic or a fleet manager out in the a mechanic or a fleet manager out in the field, um I've more work than time today field, um I've more work than time today field, um I've more work than time today for those guys. So, uh, is is just for those guys. So, uh, is is just for those guys. So, uh, is is just helping them get the work done. I I helping them get the work done. I I helping them get the work done. I I don't think I don't see in the near don't think I don't see in the near don't think I don't see in the near future, uh, AI materially changing my future, uh, AI materially changing my future, uh, AI materially changing my headcount needs on the fleet side. headcount needs on the fleet side. headcount needs on the fleet side. >> What about from a dispatch perspective >> What about from a dispatch perspective >> What about from a dispatch perspective or analytics perspective? I'm sure you or analytics perspective? I'm sure you or analytics perspective? I'm sure you have teams that do those perform those have teams that do those perform those have teams that do those perform those functions. functions. functions. >> Yeah. So, I do lean on the IT >> Yeah. So, I do lean on the IT >> Yeah. So, I do lean on the IT organization for for a lot of the organization for for a lot of the organization for for a lot of the reporting and and analytics side of reporting and and analytics side of reporting and and analytics side of things. So, um I there probably is things. So, um I there probably is things. So, um I there probably is somewhere that um you maybe see somewhere that um you maybe see somewhere that um you maybe see headcount slow. Uh I again I don't right headcount slow. Uh I again I don't right headcount slow. Uh I again I don't right now it's not impacting us enough that now it's not impacting us enough that now it's not impacting us enough that you have people sitting on their hands. you have people sitting on their hands. you have people sitting on their hands. So it's it's I view it as a productivity So it's it's I view it as a productivity So it's it's I view it as a productivity tool still. Um but yeah, dispatch for us tool still. Um but yeah, dispatch for us tool still. Um but yeah, dispatch for us is still kind of decentralized. So I is still kind of decentralized. So I is still kind of decentralized. So I haven't really stepped in that pond yet, haven't really stepped in that pond yet, haven't really stepped in that pond yet, but um yeah, I'd be interested to hear. but um yeah, I'd be interested to hear. but um yeah, I'd be interested to hear. Yeah. Yeah. Yeah. Not from an oil field perspective Not from an oil field perspective Not from an oil field perspective either. It's not going to change either. It's not going to change either. It's not going to change anything for headcount wise. We still anything for headcount wise. We still anything for headcount wise. We still need our our professionals. need our our professionals. need our our professionals. >> Yeah. >> Yeah. >> Yeah. >> There's your answer. Right. >> There's your answer. Right. >> There's your answer. Right. >> Yeah. >> Yeah. >> Yeah. >> Not sure if you see anything in the >> Not sure if you see anything in the >> Not sure if you see anything in the future though. I I understand currently future though. I I understand currently future though. I I understand currently that's how it is. But >> anyone else? >> anyone else? >> Anyone else? If not, I'm gonna sneak a quick one If not, I'm gonna sneak a quick one here. here. here. >> Where does what to do in Nashville? >> Where does what to do in Nashville? >> Where does what to do in Nashville? >> I'm allowed to do it. Come on. Where >> I'm allowed to do it. Come on. Where >> I'm allowed to do it. Come on. Where does autonomy fit into all of this? Have does autonomy fit into all of this? Have does autonomy fit into all of this? Have you guys looked into this you guys looked into this you guys looked into this >> level four fully driverless trucks? >> level four fully driverless trucks? >> level four fully driverless trucks? >> Are you guys preparing for this future? >> Are you guys preparing for this future? >> Are you guys preparing for this future? >> Obviously, like the dash cam doesn't >> Obviously, like the dash cam doesn't >> Obviously, like the dash cam doesn't need to be there if there's no driver, need to be there if there's no driver, need to be there if there's no driver, right? But I think the um the fleet right? But I think the um the fleet right? But I think the um the fleet operations, the platform itself is still operations, the platform itself is still operations, the platform itself is still going to be a very valuable asset in going to be a very valuable asset in going to be a very valuable asset in that world. And you know, I don't know that world. And you know, I don't know that world. And you know, I don't know where that is going to go or how long where that is going to go or how long where that is going to go or how long it's going to take to get there, but as it's going to take to get there, but as it's going to take to get there, but as you said, you know, we're focused on you said, you know, we're focused on you said, you know, we're focused on serving the needs of the 99.999% of miles driven today, which are done of miles driven today, which are done with human drivers in the seat who need with human drivers in the seat who need with human drivers in the seat who need this kind of technology to keep them this kind of technology to keep them this kind of technology to keep them safe. So, uh, who knows where the future safe. So, uh, who knows where the future safe. So, uh, who knows where the future will take us, uh, and how that looks, will take us, uh, and how that looks, will take us, uh, and how that looks, but it's something we're not we're not but it's something we're not we're not but it's something we're not we're not like focused on that at this point. like focused on that at this point. like focused on that at this point. >> I I would argue a little bit of the that >> I I would argue a little bit of the that >> I I would argue a little bit of the that you don't need the camera anymore or the you don't need the camera anymore or the you don't need the camera anymore or the telematics anymore once the vehicle's telematics anymore once the vehicle's telematics anymore once the vehicle's autonomous. And just that as a fleet autonomous. And just that as a fleet autonomous. And just that as a fleet operator, right, um when I have an operator, right, um when I have an operator, right, um when I have an autonomous vehicle driving around and autonomous vehicle driving around and autonomous vehicle driving around and there's an incident, which inevitably there's an incident, which inevitably there's an incident, which inevitably there will be, right, there will be, right, there will be, right, >> um the only source of truth I have is >> um the only source of truth I have is >> um the only source of truth I have is the person who built it, the person who built it, the person who built it, >> right? >> right? >> right? >> Versus me having my own third party >> Versus me having my own third party >> Versus me having my own third party device in the in the vehicle. So, I device in the in the vehicle. So, I device in the in the vehicle. So, I think, you know, as a as a fleet think, you know, as a as a fleet think, you know, as a as a fleet operator in the future of autonomous operator in the future of autonomous operator in the future of autonomous vehicles, I definitely see me still vehicles, I definitely see me still vehicles, I definitely see me still having my own device in that vehicle having my own device in that vehicle having my own device in that vehicle that that gives me a a different source that that gives me a a different source that that gives me a a different source of truth. of truth. of truth. >> Mhm. >> Mhm. >> Mhm. >> Yeah. >> Yeah. >> Yeah. >> That just scares me. >> That just scares me. >> That just scares me. >> Jamie's never ridden in an autonomous >> Jamie's never ridden in an autonomous >> Jamie's never ridden in an autonomous vehicle. vehicle. vehicle. >> You're going to get her arrival. >> You're going to get her arrival. >> You're going to get her arrival. >> Yeah. Just look. People are also >> Yeah. Just look. People are also >> Yeah. Just look. People are also terrifying. terrifying. terrifying. >> I know, but I know. Yeah, watch some of >> I know, but I know. Yeah, watch some of >> I know, but I know. Yeah, watch some of those videos, guys. those videos, guys. those videos, guys. People are bad. Bad in the 80s. People are bad. Bad in the 80s. People are bad. Bad in the 80s. >> All right. Do we have any last minute >> All right. Do we have any last minute >> All right. Do we have any last minute questions? >> Sh, since no one else will give you what >> Sh, since no one else will give you what to do in Nashville. I'm going to cut to to do in Nashville. I'm going to cut to to do in Nashville. I'm going to cut to the chase. Broadway is full of touristy the chase. Broadway is full of touristy the chase. Broadway is full of touristy stuff. Go enjoy it. But the thing you stuff. Go enjoy it. But the thing you stuff. Go enjoy it. But the thing you need to do in Nashville, if you're going need to do in Nashville, if you're going need to do in Nashville, if you're going to go to the um Honky Tons, we're from to go to the um Honky Tons, we're from to go to the um Honky Tons, we're from Nashville. Um, uh, Robert's Western Nashville. Um, uh, Robert's Western Nashville. Um, uh, Robert's Western Western World. Go there, but do it Western World. Go there, but do it Western World. Go there, but do it tomorrow night. Kelly's Heroes plays, tomorrow night. Kelly's Heroes plays, tomorrow night. Kelly's Heroes plays, best band on Broadway. Uh, and get a best band on Broadway. Uh, and get a best band on Broadway. Uh, and get a recession special. It's what you need. recession special. It's what you need. recession special. It's what you need. It's a bologn, a fried blowing sandwich, It's a bologn, a fried blowing sandwich, It's a bologn, a fried blowing sandwich, a bag of chips, and like a PBR, a bag of chips, and like a PBR, a bag of chips, and like a PBR, something like that. A PBR or a something like that. A PBR or a something like that. A PBR or a highlight one of those, a cheaper beer highlight one of those, a cheaper beer highlight one of those, a cheaper beer for like six or seven bucks. for like six or seven bucks. for like six or seven bucks. >> Yeah, it's crazy. Like, yeah. Yeah. So, >> Yeah, it's crazy. Like, yeah. Yeah. So, >> Yeah, it's crazy. Like, yeah. Yeah. So, you you can skip the other ones or go to you you can skip the other ones or go to you you can skip the other ones or go to them, whatever, but go hit Robert's them, whatever, but go hit Robert's them, whatever, but go hit Robert's Western World tomorrow night. Western World tomorrow night. Western World tomorrow night. >> Okay. >> Okay. >> Okay. >> Thank you so much. That's a pro tip. >> Thank you so much. That's a pro tip. >> Thank you so much. That's a pro tip. >> You're welcome. >> You're welcome. >> You're welcome. >> Most informative part of this. >> Most informative part of this. >> Most informative part of this. >> Are you in the band that's playing? >> Are you in the band that's playing? >> Are you in the band that's playing? >> No. >> No. >> No. >> Oh, okay. >> Oh, okay. >> Oh, okay. >> Little selfotion. >> Little selfotion. >> Little selfotion. >> I am friends with the guitar. So, but >> I am friends with the guitar. So, but >> I am friends with the guitar. So, but that's that's that's >> okay. I felt like there was a >> okay. I felt like there was a >> okay. I felt like there was a self-promotion. self-promotion. self-promotion. >> All right. I think we're out of time >> All right. I think we're out of time >> All right. I think we're out of time here. So, please join me in a round of here. So, please join me in a round of here. So, please join me in a round of uh calls for Hamlet. Thank you for uh calls for Hamlet. Thank you for uh calls for Hamlet. Thank you for coming and have a great rest of the day. coming and have a great rest of the day. coming and have a great rest of the day. See you guys around. on cube.