From Cameras to Insights: Building Real Value with Vision AI

We never give up. To fight is a must. We going in. We bringing the fire. We about to combust. We ready and willing to do what we gotta do. You cannot stop it. We put in the time. We put in our grind. Nobody can top it. Hard Times Blues Rise. Welcome. I'm Ryan Spur here at Connection, and I'm joined by Marshall Tyler from Vaidio. I'm joined by Robin Braun from HPE, and I'm also joined by Brian Gallagher, my peer in industry solutions at Connection.

Welcome to all of you. And today's session is Vision AI: From Camera to Insights. So Marshall, let's start with you. We've long had this conversation over the last one to two decades, organizations that are investing in surveillance and IP cameras and NVR and VMS solutions, and so most folks have some flavor of this in their environment. When it comes to how do we extract more value, how do we layer on other solutions that can tap into these existing camera tech estate, where are we at when it comes to vision AI and some of the table stakes in terms of extracting value through the use of AI with vision?

Great question, Ryan, and first off, thanks so much for having us on. Look, Vaidio considers the relationship with Connection and HPE as critical to helping customers unlock the power of their cameras, and that really is what we're talking about here. I think when you talk about where we are on the maturation curve is a growing realization that we've spent lots of money, and we continue to spend money on IP-enabled cameras, but we can never have people who are watching those at all times. And so if we start with that premise, then we say that there has to be something that we can do to bridge a gap, and then what is the power of bridging that gap? So that gap

is a whole lot of missed events, whether those are security events, whether those are safety events, whether those are operational inefficiencies or areas where customers experiencing our facility or our store in a way we wouldn't want to see, we wouldn't want to have happen. And what we want to do at Vaidio, and what I think that in a partnership with HPE and Connection, what we're able to do extremely well is really help unlock the power of people's cameras. So if you start with the pretext of the camera's there, it's an asset, think of it as a sensor.

It senses things that you want to know about your business in real time. It also senses things that are happening in your business over time. And so the way we do that is we want to take that camera, we want to turn it into data. Take that camera stream and turn it into data, and we do that leveraging GPU inferencing on the power of advanced platforms like HPU and with HPE, and with partnerships with advanced partners like Connection.

Can I ask a follow-up question? So you talked a little bit about some of the different personas and some of the typical things that we have in place, like cameras for use for cybersecurity and safety. When you get into businesses and you try to educate them on how to turn their camera and their tech estate into data, what are some of the other personas or types of functions that you start to have conversations with so they can understand the true value across the organization, not just from a security perspective?

Yeah. Great question. And the reality is that for many years, this industry has catered to the needs of security surveillance, and that makes a lot of sense. The owners of camera systems are largely the security teams. And of course, the security teams are not large enough to be able to watch every camera feed. And so there's a lot of need to be able to layer AI to interpret what's happening, to understand what's happening in the camera feed, to alert you when there's a security incident or a safety incident. But what we're seeing is the emergence of new buyers.

What we're seeing is the emergence of whether it's a departmental leader who says, "You know what? I really think there's more that I can do from an AI perspective, leveraging the cameras that are already monitoring my retail store or my manufacturing facility or my distribution center. If I had more sets of eyes seeing the same thing that I do when I'm looking at it myself, then I would be able to take more action, and I can build a business case around, 'Hey, we can really improve the way our manufacturing facility is operating or how our customers are experiencing our retail store.'" But what we also see is the emergence of, quite frankly, the C-suite, who

says, "Look, I have an AI agenda. I am expected by my board to be advancing how well we leverage AI, and I have an incomplete data set. By not having taken advantage of understanding the data that's available from the cameras, I don't have the data set I need to really take the type of actions that I would want to be able to take." And so what we see is the emergence of not just the security buyer, but departmental leaders, which sometimes are sophisticated security buyers as well, but also the emergence of the C-suite who says, "Hey, look, I need more data about what's happening with my customers, what's happening with my employees, what's happening with our facilities.

And we don't want to have multiple vendors. We want to have a platform that can understand and interpret what's happening in our product. And what we need is to be able to run that on advanced compute like HPE, and we need partners like Connection who have deep experience in our industries, who can help bring to life the power of what that camera data can do." Yeah. Brian, I want to just flip it over to you and- What Marshall's talking about is this idea of going from what we've always done with cameras, which is largely security related, and then shifting to different stakeholders in the organization.

But I think, Marshall, you were starting to touch on something in there, which is also this notion of going from a reactive use of the cameras and surveillance to something that's proactive. So outside of integrating this with operations, Brian, when it comes to your industry and retail, how important is being proactive and using technology not just to go and, I don't know, address something a day after an incident's happened, but to actually be proactive, whether it's facing a security measure, a safety measure, or perhaps even what Marshall's talking about?

How do we proactively inform different stakeholders? Yeah, the ability to move into store operations and apply these technologies to operational efficiencies and growth, that's really where the value is for the brand. When we're simply in loss prevention, it's a cost saving. You get to put it in the bank once or avoid it once. But as you cross into operational efficiencies and start tying it to activities that are happening in the store that make employees more productive, maybe more engaged and happier in the task that they have to provide, or you're laying something out specific to customers. We talk all the time in retail about the value of personalization on a

customer's journey. Well, how do you do that location by location as you go from one city, one state, to another city, another state, another mall? And then the cameras that you have sitting in the ceiling. And to your point, Marshall, taking that extra data and actually doing something that has brand value.

And that's what we're starting to see more and more is where do vision applications provide longstanding brand value to employees and customers? Robin, Marshall was talking a little bit about the power of AI, the partnership with HPE, and the need for accelerators to support artificial intelligence.

And so we've talked about the cameras and the tech estate that exists for most organizations. We talked a little bit about the workload, which is largely products like Vidyo and the ability to bring AI. What about the infrastructure? There's obviously different scaled-out versions of this. But if you're an organization who has this existing tech estate and you're looking to move from reactive to proactive with a partner like Vidyo, how do organizations step into that, and what does that infrastructure look like?

I think one of the great things is that when you think about the infrastructure, working with partners such as Vidyo, you can actually leverage a lot of the investment you've already made. You don't necessarily need new cameras. You don't need new networking. All of that stays in place so that it becomes a more sustainable way of starting to approach your vision AI that you're not having to do something fully new. Now that being said, yes, you can leverage your cameras, you can leverage sometimes in part of your network, or we find where your network may need improvements.

But really in the end what it comes down to is being able to bring that accelerated compute, that GPU, such as with our friends from NVIDIA, and being able to bring that, whether it's out at the edge or back in a data center, being able to look at what is that best architecture. Of course, we're very excited for what the HPE infrastructure can bring with an entire line of both edge and of course data center capabilities, being able to incorporate seamlessly that accelerated compute, and of course, being able to then have Vidyo integrated on top of that to be able to bring that intelligence to the fore.

Let's pivot into some of the operational aspects, because what we've kind of talked about is what we've used cameras for a long time, which is safety and security. Again, albeit typically reactive. And then we kind of talked a little bit about the proactive side. So let's kind of blend into examples of that. So Marshall, we did an implementation for a finance organization together, and one of the aha moments, even at just the typical stakeholder for security, was this notion of how their team's typically looking at video.

This could be a security operations center staring at lots of small boxes of hundreds of cameras. And there's things that are just naturally missed. We're human. And if you have this sort of environment, you're missing out on opportunities to even do what we might think is table stakes, like identify a threat, identify weapons, identify someone who's injured or fallen in a particular facility, any of those situations. But then you add in this notion of the proactive piece, like how you can layer Vidyo on top of that existing infrastructure, in some cases even integrate those real-time proactive detections with security platforms, and so that you can better inform even a traditional

stakeholder like security. How are you seeing this play out in some of these different functions, whether it's safety, security, and not only what your platform's doing, but how you're integrating with other solutions to bring more proactive operational benefits? Yeah, I think what you're describing in part is the aha moment many customers have, which is that the camera sees a lot more than just a safety or security event.

A camera sees the entire way you're running your business, the interaction of your customers with your business, the interaction of your employees with machinery, with equipment, whether or not they're doing things in the way that you hope that they're going to be able to do to be safe and productive. So the camera sees a lot more than that, and yet today, the camera is buried in a security operations center that's monitoring passively with somebody staring at a bank of camera feeds. And of course, that means there's this huge gap we talked about. And there's the opportunity then to really turn this into a proactive tool that says, "Hey, Mr. Store Operations Manager, by the way, you asked us to notify

you if the line got a certain length in your store so that you could call somebody actively to come to the front and help relieve the pressure, open up another point of sale display." Right? Or, "Hey, Mrs. Manufacturing Plant Supervisor, you wanted us to alert to any time a dock door is open, but there's not a truck pulled into it because that's an opportunity, one, for somebody from outside to come in, but it's also a safety threat." Or, "Hey, our forklifts, the drivers that are driving the forklifts, are they your actual licensed drivers or not?" Or, "Are they going up and down the aisles in the direction that you told them to go that's the safe way?"

We have one customer who we've worked with for quite some time where we've developed over 18 use cases over the last 18 months just in their manufacturing plants. They're a container board recycler. But the journey started with us looking at raw inputs to the container board that they were bringing in to then recycle and grading those.

And what we found is that we were more accurate and consistent than human operators were. And consistency is one of the big things, right? It's the attention to detail consistently that you can leverage with AI, which has saved that single plant over a million dollars a year just from better grading of raw inputs. And so that, in of itself, are just examples of where the camera's seeing and capable of doing a whole lot more than what you can get with a bank of security guards monitoring a passive camera stream.

Yeah, you bring up an interesting point, and I think this comes up in a lot of our conversations when we discuss this with customers, which is, I think a lot of customers don't realize they can actually detect multiple things on the same camera stream. And so what you're talking about is this notion of, hey, we can do the table stakes. We can detect slips, trips, and falls.

We can detect some of the security events, but we can also bring a whole slew of operational use cases, and those can all essentially consume the same camera feeds and identify any range of use cases. So it's this nice balance where you can start small, to your point. It might be one use case that's a horizontal use case, or it could be an operational use case that has real, tangible business outcomes. And then you could just layer on additional use cases.

And you're really just limited by infrastructure. Robin, just pulling the thread on that, as we start to think about that, because we have the cameras, we have the networking, we're streaming that to Vidyo. When we start to scale up, whether it's adding more cameras or adding more inferencing or detections on top of the same camera feeds, how do you think about how we support customers on that scale journey from where they start today and where they may need to go, and how do we simplify that for them?

Well, we would never want anyone to slow down because of infrastructure. Infrastructure we can do. When we start to look at it, and I think one of the most important things is that when you think about the cameras, when you think about the AI, it shouldn't be one point solution for this and one point solution for that.

When working with a partner such as Vidyo, they can bring multiple points of intelligence to that one camera feed at once so that you can start to look at what are the different ways that you can process it and be able to bring that in. So when we start to think about the infrastructure, really it comes down to how many streams and how many models are you going to be using, and being able to very flexibly scale out the capabilities by being able to add additional infrastructure as you go.

And I think that's actually one of the great reasons that we have something like Greenlake at HPE, where you can actually pay as you go and have it essentially as a service, and be able to look at how you want to invest over time and have that capacity there so that as you continue to build out the capabilities, that your ability to consume them is right there at the ready, and that you're not slowed down by infrastructure.

One of the last things that you ever want to do with AI is to start to parse it out and to treat it as this sacred commodity. The whole goal of this is to not just drive safety and security, which, of course, is tantamount. You can't really put a value on keeping people safe. But what you can do is look at what is that additional efficiency, what is that impact to the business that you can help them gain?

Like Marshall was touching on, being able to demonstrate a million dollars worth of savings because of improving a process. That is material to a business, and you don't want to start rationing your AI use because you don't have infrastructure. Infrastructure we can help with, of course, with the help of connection And being able to then scale out whether, again, it's looking at adding more edge line because you're scaling out at the edge or you're in a DL380, any of those type of platforms are great compute ways to bring the compute to the data. You don't have to take your data to the compute anymore.

We can bring the compute and the AI to the data to be able to help you process it in real time. Yeah, you make a good point. I think that this is sort of the natural progression, I think, Marshall, we see with vision AI is what Robyn's talking about. A lot of times we can leap into every single use case on day one, or we can pick a particular facility or a particular store branch location. We can deploy a small pilot, for example, and we have our own proof of value that we do very closely with HPE and with Vidyo, basically allowing us within as short as two days to bring on an entire site of, say, 15 to 25 cameras to deploy the technology, train them, and have them operational at the end of two days.

And this, of course, allows us to do all the out-of-the-box capabilities that we would typically think of for safety and security and some of those operational efficiencies. So I love it because I think that's the natural way people want to start. Most people don't realize what they can do in this space.

So starting, seeing it on a bounded number of camera streams around a mix of different stakeholders and interests and use cases allows us to get people realizing the power of this technology. And then Robyn, to your point, the ability to say, "Hey, this actually works. Wow, this is actually saving us time.

It's alerting us for the right things proactively. All right, let's scale this out across various sites. Let's scale it out at corporate facilities." And to your point, that could be a mixture of technologies that are a best fit for the customer. Brian, let's get your view on this because you and I talk a lot, especially from my perspective on the manufacturing strategy side and you on the retail side. You talk a lot about the operational side of this and customer value and brand. Can you go into this a little bit more about where retail organizations are looking at vision AI and how it's going to materially benefit their business?

Yeah, and Marshall touched on this a little bit. The fact that it crosses over to line-of-business leaders today. Yeah. It's the single biggest challenge that I put out to our customers when I'm engaging, which is, one, contact me, whether they're in loss prevention or maybe it's the IT team who has been asked to take over the camera solution from loss prevention because they've become a technology asset and not just safety and security.

But thinking about based on their business, their retail segment, and where the value is, who are the other stakeholders that have value? And never should it be one use case when it comes to vision. There's a million different use cases, each brand being a little different, whether I'm looking from traditional loss prevention and then moving to supply chain. It's sort of a natural one, and if I've lost something, well, that's part of the value is stopping theft. But there's also value in making sure I restock what was stolen before a person walks around the store and has to make a punch list or a pick list, and understanding when things are low on stock in the wrong place, and those types of

operational things that they're easy for line-of-business leaders to attach ROI to because they've done that forever. And so my challenge always is, one, find a partner in the business, whoever brings the first use case, and then the second is, think about your stores and anything that a business leader sees when they walk around the store and measures, ask yourself why you don't have a vision system doing the same thing. Planograms, stock issues.

I've been in this situation for years where I walk around a store and go, "Man, if this planogram was just set the correct way, I would sell more." Why aren't we calling that out? And so again, each brand being a little different, but if you can get those operational outcomes to sit on top of your loss prevention outcome, you'll start that snowball effect, and that's typically what we see is you add one, you add two, you add three, and then those line-of-business leaders start talking about what they could get out of it at the same time.

Yeah, we're definitely seeing the same sort of trend in manufacturing and albeit maybe different things. Obviously, as Robyn indicated, there's the traditional basics around safety and security. These continue to be paramount, and as Robyn said, you can't put a price on those things. But when you start getting into the operational side, say, factories, for example, there's a whole slew of opportunities for manufacturers and interests there.

So, whether we're talking about quality control measures, one of the areas that's coming up quite a bit lately in my experience is around industrial studies. There's oftentimes activities that we joke about this. There's activities where there aren't enough people on the plant floor to do these activities.

If you want to do an industrial count or an industrial time study or a queuing study on a plant floor, you might need 10, 20 people over two to three man years. And so the likelihood of you ever doing that, it's never going to happen. And so it doesn't get done, or periodically it's done, or it gets sampled.

So the idea of bringing cameras, either leveraging cameras where they exist or bringing in additional cameras into some new domains, over work cells, over machinery, and the ability to start identifying queues, identifying, counting. This gets back to what you were talking about, Marshall, this idea of really extracting material meaningful data Out of the environment and bringing that up the stack, whether it's into a Vidya data platform and how we can visualize and look at data temporally over time, or to bring that up the stack to do something even more material with it. These are what we're seeing from a lot of our manufacturers.

It's really trying to deal with Staff augmentation, and how do we do these tasks that are time-consuming, and we typically can't accomplish with the workforce that we have. Right. I think you guys are nailing it and circling this idea that, again, there's a lot you can unlock with the camera, and we're sitting on an operational asset, we're sitting on an asset, which is your camera stream that you turn into data. And what we find is the elevation of those who are involved with the cameras to greater stakeholder conversations than they've had in the past.

I'm working with somebody today who we're brought in from a security standpoint, but we're working with eight different operational departments because the cameras that they have cover all of those operational departments, and it's what else can we see with a camera that can help those stakeholders?

What you find is that a different level of strategic conversation happening. But it's really important in this context as we paint the exciting picture about vision AI, is that we have a dose of reality that you have to be able to see it. The camera has to actually be able to see it, and you got to be able to see it in a way that you can actually spot and detect.

And what I find is, and what I try to teach my organization and even customers is beware of the vendor who says yes to everything. Too often we hear of it felt like a science project. It came in, and it was going to take months to get anything going. And we're proud to have what we feel is the broadest, most applicable platform in the industry, and we want to get in, and we want to nail use cases that are right out of the box and get you value going quickly.

And then, like I mentioned, that manufacturer that we've done 18 new use cases. When I first met with that CIO, we had done 12 use cases, and they said to me, "Hey, we're at the point we're bringing new use cases." And so we've been partnering with them monthly, addressing new use case to meet their specific needs in their facilities.

And I think that's the overriding sense that you want here with your vision AI vendor and with your partners, is you want a sense of partnership. You want an open, collaborative relationship. What's real, what can be done today, what needs to be developed, what is in the future roadmap? Because it's a journey.

It's very clear to me that the promise of vision AI is greater than what we still realize today, but we realize more than enough today that is of significantly high value for people. And so you want to get on that journey with a vendor that you trust and then sort of accelerate and develop more and more over time.

And that's what a company like Vidyo does in connection with partners like Connection and HPE. Yeah. It's a great transition point because I think what you're getting at is the technology doesn't do everything today that we could envision. It might get there soon, but I've seen even through the years of our partnership, from basic detection to natural language where we're able to, with your platform, meaning not having to bring in another solution, we're able to move from just basic model detection to natural language where we can detect a range of things through prompting. And so this technology is going to only advance. And so this idea of advancing with

partners who are on the cusp of this technology and as the underlying capability allows for it, you're going to expose new capabilities, new operational insights. And of course, what I want to transition to is how do we extract more data from our physical world, which is what I think you're really getting to, Marshall, which is at the end of the day, there are basics that we'll do.

We'll detect a particular incident, we'll alert someone. That's very important. We might detect that bread's short in the aisle at a particular grocer. In the case of Brian's world, we want to notify someone so that maybe we can bring more bread out and make sure that we deliver a great experience for our customers.

I want to turn it over to Rob, and let's talk about where this is going or even some of the more advanced things we can do with the data that we're extracting from the physical layer and from products like Vidyo. Why don't you share a little bit about where other technologies or other AI solutions that we can stack on top of Vidyo and what's available today, and how are we bringing additional value through things like agents and language models?

I think that's really important when you start to consider how much vision is still evolving but is able to bring us and to bring that physical world kind of into the AI world. But being able to then connect it. As an example, something we did with the town of Vail is being able to integrate Vidyo with one of our other Unleash AI partners, Kam iw, they're a fantastic agentic orchestration platform, and being able to have that vision AI come into that agentic understanding where additional context can be brought to the information that the vision AI is transferring, that Vidyo is able to share, and being able to provide additional context, being able to build it into an entire

workflow that then can continue beyond that point-in-time insight that Vidyo has been able to bring that kind of has kicked off a particular activity, whether that's fire detection, whether that's safety and security, or whether that's the trash can's too full. Regardless of what the actual notification is, it's so powerful to now be able to bring it into that multi-agent environment where it can be processed and then move into that real-world workflow as well.

It's really about the so what? I have the data- Right ... so what do I do with it? Or how do I interpret it? And so what we can do is partner with others who can bring that agentic layer to bear to bring context and action to the data that's created from the camera. Yeah, I think that's really important is being able to, to your point, Marshall, is that so what? It's like we now have that insight, now what can I do with it? How do I take action and bring that in?

Because when you think about how people work, we don't just go from insight to insight, but we actually then have to do something with it to make it meaningful. And that's where I think it starts to unlock that true value that we can find from AI, is that we're actually able to now implement it into action.

Brian, if we take what Marshall and Robyn are talking about in your world, this idea of extracting data from the physical world using language models, agents, data pipelines, integrations, how do you see the future of this integrating with the applications and the business systems that are driving retailers and how this can benefit them?

Yeah, and these guys have touched on it, but specific to retail, when we think about uniqueness of location and shopper, retailers have put a lot of time and energy into things like retail media networks. Well, what am I advertising to who? How can I operationalize those in a specific setting knowing that it's a mom shopping with three kids tagging along? That's one marketing opportunity. Versus it's three guys in their 20s staring at the beer cooler.

What do you want to market and what value do you want to get out of that? The ability to build here and understand what those unique cases are, that personalization is what every retailer dreams of. And so one of the things that I love from what Robyn and Marshall, you guys were just talking about, and I have this conversation all the time, is taking the learnings and letting the data give you the results. And so often as a retailer, we say, "Hey, when we get this data, here's what we want you to do with it." But allow the outcomes to be seen. That's what the camera gets to do, and allow the compute to take that data and say, "Huh, here's the outcome."

Rather than when we see X, a person says, do Y. That, to me, is one of the coolest things that this partnership here is able to do that so many of the camera solutions that are out there, even when they give heat mapping. I can tell you where the heat goes. I was a retailer. It's always hottest in the front of the store.

Okay, but what do I do in the secondary level of the store, the back of the store? We've got cameras. Let the data drive that instead of having humans have to make all those decisions. And then to be able to personalize that, that's the home run for retailers. And Brian, just building on that, you mentioned, okay, when I see X, I do Y. Great. The question becomes, how often am I seeing X?

At what time of day? And would that change what the Y is that I would do, or would it change how I manage so I have fewer X's? Yep. Or I have more X's. And so it's seeing the business in real time, and it's seeing the business over time, and collecting that data and insight so you can act appropriately and manage your business to be as effective as you want it to be.

Yeah. You mentioned line queuing earlier. When to open up an additional register. Well, that's the short term. The long term is, let's look at when customers were walking into my store. Understand that the average journey through my store with a small cart versus a basket versus a large cart is X, and proactively have those lines open before there's ever a need for them.

Understand what that looks like, and that's where the data-driving outcome, as opposed to just providing data, changes that business outcome. That's the thing that when you were running a single store, you felt like you had your pulse on it. But when you're a person managing across a large retail footprint, it's hard to have your pulse on that and to feel like your newer managers are able to make the same types of good decisions that your experienced managers are going to make.

And so how do you bring data to bear to improve the operations of all stores by learning the patterns and benefits of the stores that are performing well? What do you see that's working well? The stores that maybe aren't. Maybe it isn't a demographic issue. Maybe it's a staffing issue. Maybe it's a store layout issue.

And so you can find out a lot of the things that you used to be able to do with your own eyes sitting in a store. You can now get that by getting the data out of the cameras that are already monitoring and are already watching the store. And interestingly, most of those things are the things the employees hate having to do.

That manager in a grocery store hates having to go wrangle somebody else to jump on a register, or now it's time to go to the lot and bring in the carts, or walk the aisles and create a pick list I've never met an employee who liked to do those things Because that's a really great traction in what they want to be doing. Yeah.

Exactly. Well, this was an enlightening conversation, and I think we took it from table stakes to operational insights and, of course, some direction in terms of how we can even go beyond the basics of vision AI into agentic platforms. And of course, ultimately, what is it that we're trying to get out of all of this, and how does it make our businesses better? As we come to time and wrap up, I just want to go round Robin here, no pun intended, but we'll start with you, Robin. Just share with us, if you could give any one thought to those listening, what call to action would you request of our listeners today?

I think the one call to action I would have is to not be afraid of AI, is to get started and to start to really explore the data that they have. They already have cameras. They already have this data. How do you turn it into information and help it help you? Jensen, the CEO of NVIDIA, started his keynote at GTC in March with a pretty simple concept. He said, "Structured data is the foundation of trustworthy AI, and unstructured data is the context." And I would argue that in any large manufacturing facility, in any large retail store, in any large business, in any large geography, the camera's the largest untapped source of structured and unstructured

data. And so what you want to do is partner with someone who knows how to unlock the cameras, turn it into data at scale. And scale is really important because there are those who can do one or two things. I sat with the CIO of a retailer recently who said, "I don't want eight different vision AI solutions. I want one.

I just got done rationalizing my technology portfolio over the last four or five years after a decade of best-of-breed software purchasing. I want a broad platform, and I want to partner with someone." And so beware of a company that says, "I can do everything." Get started quickly and recognize the value that you have an untapped data source that can meaningfully attribute to how you operate your business.

Yeah. To me, the biggest challenge I'd put out there, I mentioned earlier getting all the operational leaders to sort of cross channels when it comes to vision, because if anybody in the business can see it, we can manage that. But on a more simplistic scale, when they're sitting in those board meetings, executive team meetings, start assuming that whatever you wish could be happening or be changed in your business, that it is actually possible. And for so long, it wasn't. "I wish we could do X." If you say, "I wish," you should be calling us up.

We all put our heads together, and it's pretty amazing to me to see just what can truly be done today that five years ago couldn't have been dreamed about. But today, it can be done pretty simplistically. Marshall's probably got real-life examples that he's deployed in his back pocket. Somebody's just got to ask.

Thank you very much to our panelists, Robin Braun, Marshall Tyler, and Brian Gallagher. Appreciate you joining me today for this session on vision AI. If you're interested in learning more, understand that with the power of HPE, with Vidyo, with our industry solutions group, including Brian and myself, and of course, our Helix Center for AI, we have the ability to deploy vision AI together, along with some of the other more advanced solutions that Robin talked about.

You can always reach out to your account executive, or you can reach out to our website at CNXN-Helix, H-E-L-I-X.com to learn more about this technology, our services, and all of our AI offerings that we can bring to bear. Thank you so much for joining today.