In this interview from the 2026 Tech Innovation CUBEd Awards, Jim Leach, director of product management for Cisco Compute at Cisco, joins theCUBE's Dave Vellante to discuss Cisco Unified Edge, winner of the award for most innovative IoT or edge platform. Leach explains why AI inferencing is pulling workloads out of the data center and toward the data itself, breaking through constraints around bandwidth, latency and limited IT expertise at remote sites. He details how Cisco engineered a short-depth chassis capable of running enterprise-class processors and GPUs outside traditional data center environments, and why a modular design — scaling from a single server up to five GPUs and full enterprise storage — lets customers grow into their AI journey without overbuying infrastructure.
The conversation also explores how Cisco Intersight extends a "desired state" management model from the data center out to the edge, giving subject matter experts centralized control over policy, configuration and security without requiring on-site IT staff. Leach highlights the Cisco Compatible AI Solutions program, which onboards independent software vendors across retail, manufacturing and healthcare to validate performance atop Unified Edge. He points to tangible business impact, from computer vision-driven retail analytics to sharper inventory management, that gives customers an easy-to-track return on investment. Looking ahead, Leach envisions agentic AI systems increasingly managing themselves, compressing the runway between innovation and deployment, and outlines why Cisco's core-to-edge architecture positions enterprises to capture the machine-speed operating model defining the AI era.
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James Leach, Cisco Compute
In this interview from the 2026 Tech Innovation CUBEd Awards, Jim Leach, director of product management for Cisco Compute at Cisco, joins theCUBE's Dave Vellante to discuss Cisco Unified Edge, winner of the award for most innovative IoT or edge platform. Leach explains why AI inferencing is pulling workloads out of the data center and toward the data itself, breaking through constraints around bandwidth, latency and limited IT expertise at remote sites. He details how Cisco engineered a short-depth chassis capable of running enterprise-class processors and GPUs outside traditional data center environments, and why a modular design — scaling from a single server up to five GPUs and full enterprise storage — lets customers grow into their AI journey without overbuying infrastructure.
The conversation also explores how Cisco Intersight extends a "desired state" management model from the data center out to the edge, giving subject matter experts centralized control over policy, configuration and security without requiring on-site IT staff. Leach highlights the Cisco Compatible AI Solutions program, which onboards independent software vendors across retail, manufacturing and healthcare to validate performance atop Unified Edge. He points to tangible business impact, from computer vision-driven retail analytics to sharper inventory management, that gives customers an easy-to-track return on investment. Looking ahead, Leach envisions agentic AI systems increasingly managing themselves, compressing the runway between innovation and deployment, and outlines why Cisco's core-to-edge architecture positions enterprises to capture the machine-speed operating model defining the AI era.
In this interview from the 2026 Tech Innovation CUBEd Awards, Jim Leach, director of product management for Cisco Compute at Cisco, joins theCUBE's Dave Vellante to discuss Cisco Unified Edge, winner of the award for most innovative IoT or edge platform. Leach explains why AI inferencing is pulling workloads out of the data center and toward the data itself, breaking through constraints around bandwidth, latency and limited IT expertise at remote sites. He details how Cisco engineered a short-depth chassis capable of running enterprise-class processors and GP...Read more
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Why is the edge suddenly becoming such a strategic priority for enterprise infrastructure, especially as AI moves from centralized environments out to the edge?add
Why is edge computing suddenly becoming a strategic priority and a hot topic?add
How did you adapt enterprise-class hardware to run AI workloads outside the data center (in edge or non-data-center environments)?add
How is the platform designed to support enterprise-class AI workloads outside traditional data centers, accommodate different customer AI maturity levels, and allow modular scaling from a single server up to multiple GPUs, servers, and enterprise storage?add
How does the platform’s modular design support scalable, secure enterprise AI deployments at the edge for customers who are uncertain about their AI needs?add
How can customers be provided with a complete, operationalized, and secure AI solution across distributed environments (from data center to edge) rather than just being sold hardware or infrastructure?add
How is Cisco building its partner ecosystem to bring AI solutions to market, and what is the Cisco Compatible AI Solutions program?add
>> Welcome back to the special coverage of the CUBEd Award winners where we recognize the technologies and platforms pushing the industry forward. Our segment today highlights one of the most discussed shifts happening in enterprise infrastructure right now and that's the move of AI from centralized environments out to the edge. Cisco has been named the winner of the 2026 Tech Innovation CUBEd Award for most innovative IoT or edge platform, and they won this for the Cisco Unified Edge. And joining us is Jim Leach, who's the director of product management for Cisco Compute. First of all, Jim, congratulations and welcome. Thanks for making some time with us.>> Hey, thanks for having us. This is great, and thanks for the award. This has been fantastic.
Dave Vellante
>> Well, you bet. I know how much effort goes into the submissions. We have dozens and dozens of judges around the world. They take this very seriously. I've been a judge on previous ones. I wasn't a judge on this one, but I meet the judges all the time, and it's really a pleasure to be working with some of the leading innovators in the industry. I want to start with the big picture here at the highest level. I mean, for years, the industry has talked about edge computing. We think of it as an extension of the data center. I've always said the cloud is not just a bunch of remote services up in space somewhere. It's extending out. And AI seems to fundamentally be changing that equation. Why, Jim, is the edge suddenly becoming such a strategic priority and a hot topic?>> Well, that's a great question, and I will tell you this. There has been a trend going on for quite some time actually where we're seeing some workloads from the data centers migrate towards the edge, and really, it's not necessarily migrating to the edge for the sake of going there. It's more migrating towards the data. And a lot of that is based on the fact that where the data lives, we can process it much more efficiently than if we were to try to move it back to some centralized cloud or data center. So if we go to the edge with the workload itself, we can process all the data we want and we're really free of a couple of constraints that have been bogging down this operation in the past where we tend to have limited availability of bandwidth, limited tolerance of latency when we process that data. And generally, we have a lack of IT experience at some of these sites as well. So we have some reasons why and constraints around how we bring the workload to the edge itself. But recently, I think that's been really disrupted, that whole model with ... As we all know, the most disruptive workload we're seeing right now is AI. And AI inferencing coming to the edge is both a tremendous benefit to our customers. They're able to take full advantage of all this data that they've been gathering. Right? If you think of data as the new oil as they say, we have to go and we have to process it and we have to refine it as close to the mining of it as possible. If you look at that evolution as it's been going, AI has really turned it on its ear, right? It's made everything so much more ... Every constraint we have has been really stressed. All the equipment, all the infrastructure that has existed in the past is really not suited for this kind of high-performance AI workloads, and we're seeing that the AI workloads themselves even just exploding in this space. So as we moved from enterprise workloads to generative AI workloads, the infrastructure was stressed to begin with. And now, we're seeing the gen AI really moving towards agentic AI. And agentic AI, I think of it as almost like a multiplier effect on my workforce. So how would I deal with 10X as many employees in the field and being able to provide them the infrastructure they need? That's the kind of problems that our customers are facing now with AI creeping towards the data, towards the edge in the way that it is really unprecedented.
Dave Vellante
>> Yeah. You mentioned gen AI and now agentic. I remember last year at Cisco Live, G2 Patel showed a chart that I actually stole snapshot and published, and it was a snapshot of what sort of gen AI 1.0 looked like from a network chatter standpoint, and it was intermittent. And then he showed what agentic looks like and you could paint the chart with just an area. And so the demands on the systems are so much higher. And you pointed out that the data, we certainly hear from enterprises all the time that edge environments are fragmented, you get, to your point, different hardware, different protocols, different security. So it's a very, very difficult problem. Now, you bring in AI. The infrastructure that's at the edge today is not built for AI, to your point. So why don't we get into what Cisco has built? I mean, you talk about the Unified Edge as the first edge computing platform that brings together compute, networking and storage and security, very importantly, into a modular system so you can bring that whole system to the edge. Help audiences understand what you've done architecturally and why it's well suited for today's AI era.>> That's another great story here, right? I'd break it down into a couple of pieces to start with. First is the actual hardware itself is very different. We took a look at being able to host an AI workload, an enterprise workload, and what the infrastructure might look like for that. And we're very well versed at Cisco in data center-type computing, and that was pretty easy for us. But then we said, "Okay. Now, let's take that and let's shift it outside the data center." So we're losing some of the things we take for granted in a data center, for example, like physical security and nicely filtered and conditioned air and high tolerance for noise. And we took all of those new constraints and we said, "Okay. Now, how do we adapt the hardware first to be able to live in this type of environment?"
So we made a short-depth chassis that is capable of running real enterprise-class processors and GPUs within the infrastructure. So we're not trying to adapt a workstation or a PC, which is a lot of what has been out there in the past. We're trying to take real enterprise-level computing for enterprise-level and AI workloads and bring it out there. So we had to do it in a way that we don't know where this was going to be mounted, we don't know what the environmentals are going to be like. We know that people are going to have to work around this unlike a data center. We know that they may not have the ability to secure this safely from where people and potentially even the public have some limited access, and we had to be able to adapt the hardware to that. That was one really important piece. The other was something that came from talking to a lot of customers. And as we talked to many, many customers, we realized that this AI journey that our customers are on, it has many stops, and a lot of our customers were in very different places on this journey. So if you think about the customer who is maybe more mature, you tend to build one type of a system, and if you think about the customer who's really just still dealing with enterprise-level applications with the idea that AI is coming, that creates a different kind of right-sized type of architecture. Well, we wanted to have one platform that could satisfy both. And the reason that they really needed to be satisfying both is that the lifecycle of these types of deployments is fairly long, right? We're talking three or four years. But if you think about AI right now, you know that nothing about an AI workload lasts for three or four years. They're changing constantly. So even customers that are at the beginning of their AI journey need the headroom to be able to get to something that is a much more full-featured performance-level with GPUs and accelerators and all of these types of performance-level, enterprise-level infrastructure, but maybe they don't need it today. They just need the ability to easily grow and get there. Right? They don't want to overbuy something that is not going to be really useful or even state of the art by the time they need it. So we really leaned in on the modularity and created a platform that we could build everything from a single server that is simply just bringing enterprise-class reliability and performance to these enterprise workloads all the way up to being able to put in up to five servers, five GPUs, enterprise-class storage, bringing in a full big security model. All of this is built in and it's built in a modular way that makes it just so much more appealing to customers who are still trying to figure out what their AI journey even looks like, to be honest. This is truly like a get-out-of-jail-free card for these IT folks who have to make these choices really based on information that the line of business may not have fully communicated to them about what AI applications they need at the edge. So this gives them a way to build the infrastructure today and grow it as the business needs it so that they can really truly show that return on investment that they need to justify this type of an investment in their own AI journey.
Dave Vellante
>> Well, the modularity gives customers optionality, number one. Number two is Cisco does the engineering work to keep all that stuff in balance. As we know, compute storage networking, a lot of times, things can get out of balance. It's your job to do that R&D so that the customer doesn't have to do that work. I want to come back. They say follow the money. I like to take that to follow the data, and we know that 75% or more of enterprise data is going to be created and processed at the edge. That's really where it's at. So we often talk about bringing the AI to the data. We don't like to move data if we don't have to, but this is an architectural shift. So I wonder how you could explain how Unified Edge can help enterprises operationalize AI actually at the point where data is created and processed. You've got the modularity, you've got the system in balance, you've done that engineering work. How does a customer practically operationalize AI at the edge?>> Yes. That's a key problem that our customers were facing, too, is we could give them the infrastructure that they need, but that really didn't do ... It didn't create the solution that they were looking for. Right? They weren't looking for what's the next server I should buy. They were looking for the entire solution. So it wasn't just about how we brought that compute to bear. It was about how do we operationalize it for them? How do we bring in the other pieces? Right? So it's compute, GPU, networking, storage, but also think about security, think about building security in all of these new layers that we're building for the customer within the solution. We're living in a almost post-mythos-type world right now where security has to be thought of differently. It's not just about secure the hardware and secure the data within storage; it's about securing the model. It's about securing the model from giving a guardrail so that customers can interact with these agentic AI models safely. It's about securing the model from customers and from people that are maybe doing malicious prompt infusion and things like that. So I'm securing every single element, and that's the kind of security that can't be bolted on after the fact. It had to be baked in at every single level. And then if you pull all these requirements together for this full stack answer to the problem, that's where you run into the, "Okay, great. Now, that's very complicated. How do I simplify that in a world where ... I'm talking about distributed AI? This isn't AI that I have control of in my data center. So how do I do that? How do I secure everything? How do I build all this solution? But then how do I operationalize it day 0, day 1, day N?" And that, for us, was wrapping a complete SaaS-based operational model around this. So we have tools like Cisco Intersight, which allow us to give the subject matter experts complete control over the policy and complete control over the configuration of these remote systems. We built capabilities into the system so that they can be onboarded with really no IT experience in the field. So somebody, it could be the electrician that pulled the low-voltage cabling through, can have the ability to stand this system up, to power it on. Once it's powered on and connected, everything is controlled centrally. Right? So I'm taking all that complexity out of the hands certainly of the folks that are in that distributed environment, putting it back in the data center, and I'm also giving them the tools to be able to manage at scale in a distributed environment across all these different architectures. Right? GPU, networking, storage, think about some of the SASE capabilities and services that we offer, some observability, all the model security like AI defense, all of that has the ... Really, that's the sort of thing that has the ability to make it more complicated, but with some of the fleet management capabilities and some of the other capabilities that we have within Intersight, we're able to take that complexity away and actually make it simpler, make it much easier for the customer to achieve that kind of zero to actually getting value out of their AI infrastructure at scale. So to me, it's really a comprehensive solution, and that SaaS management suite is something that Cisco has been leveraging inside the data center for a while with Cisco Intersight, and now, we're taking it out of the data center. We're giving a lot more capability to the customer that's managing this distributing environment, but at the same time, we didn't change the model. This is not an edge-type management solution, and we have the data center management solution separate side by side. This is one solution that can manage from within the data center all the way to the edge. So this whole notion of core to edge capabilities are something that we think of ourselves as really the one place, the one company that I think has these kind of capabilities all together. And Cisco, the last few years, has really focused on building itself internally and externally so that we can deliver all of these technologies to customers without having to say that, "Oh, that's another business unit," or "This is this business unit," and just giving them a bunch of Lego blocks and telling them to build a solution. Here, we're able to bring the best of Cisco technologies together regardless of what business unit it's in and something like Unified Edge and giving it to customers and showing them, "Hey, here's a solution," instead of, "Here's a set of tools to fix your problem."
Dave Vellante
>> Well, you made the point upfront that you're not always necessarily going to have IT people out at the edge. So you've got the best of both worlds. You've got the distributed system, but you've also got the centralized management. So what I find interesting is this isn't just a hardware conversation. A lot of edge projects fail because of operational complexity, and you talked about extending Intersight, that gives operational consistency from data center to the edge. So that's super important. You mentioned SASE. I want to bring this conversation back to security. Security at the edge has always been a challenge because you're now distributing infrastructure everywhere. How did you approach security differently with Unified Edge?>> Well, I think one of the fundamental pieces that we have with Cisco Intersight really helps us to think about how security should be done at the edge. And what I mean by that is a lot of tools that are out there in the market today with other type systems, they have really models that say, "Hey, let's build a configuration and then let's push that configuration out to the box." It's kind of a fire-and-forget-type method. We took a very different approach, and we always have really. Cisco Intersight is what we call a desired state model. So we build this state of the system, we abstract the entire statefulness of a system into a profile. We've always done this. This is what makes UCS really unique. Now, if you take that to the edge, now we're saying, "Hey, let's build this entire system, not just around compute, but around observability, around security, around all of the pieces we want. We define the exact state we want it to be in. We push that state out, but we monitor that as a desired state." That's the state we wanted to not only achieve but maintain. And if you can maintain that secure state, regardless of any changes that have been made, whether it's malicious actors or accidental changes or through something that may not be running at full performance level, all of those things will change the desired state in a way that we can then monitor that and know that, okay, something's changed, I need to go and act upon that and get it back to the desired state. So that's the kind of thing that you can do programmatically, doesn't require ... I'm not sending just a page or a text to somebody and saying, "Hey, go ahead and ... Something's wrong here." This is more us saying that the machine, the model can maintain its integrity, whether it's deployed or whether it's just something that I'm building through profiles. So that's really critically important. The other piece is around some of the situational awareness tools that we give to customers, and those can even be across technologies. This is where it really gets exciting. Right? This is probably the most amazing time to work at a company like Cisco because if you think about, I'm deploying compute at the edge and that's my focus in life, but I talked to my counterparts who are working on projects within Cisco Unified Edge, like the secure router node that we have with our Catalyst team that's internal to Cisco, they have a lot of experience at the edge, significant amount of decades really. And so their team's building into our platform and taking their tools and our tools and being able to give you, as a compute person, I have the situational awareness to see what's going on in a tool like the Catalyst management tools and vice versa. They can see what's going on in the compute side. So I'm really not just trying to bolt things together here. We're embedding these capabilities and these management capabilities, and really, security is all about management. If I can't see it, if I can't measure, it's the least secure. If I can see everything, if I have complete situational awareness, if I know that that desired state is achieved, then I'm going to be much, much more secure.
Dave Vellante
>> Got it. I mean, I'm going to switch gears a little bit on you, Jim, and talk about the ecosystem. Cisco is known as an exceedingly partner-friendly company. I've been to partner events that Cisco has held. It really is a key part of the Cisco value proposition, is its ecosystem. So those partnerships ... Most recently, you guys are partnering with Nvidia, you've got historical investments with the x86 ecosystem, other ISVs, other hardware partners as well. And the edge opportunity, it feels like it's too broad. "You need a village," as the saying goes, too broad to solve alone. So I wonder if you could discuss the ecosystem, its importance, and how you think about it specifically as it relates to the edge.>> Great question. That was definitely something that was a little bit different about this exercise of building this product and trying to bring it to market. Right? We realized that we have great partnerships that exist already. We're working very closely with Intel, for example, as we always do around CPU technology and some of the networking technology that they have. We're able to take those types of partnerships, expand them a little further. We work with our typical, the Nutanix of the world, the Red Hat, those types of platform vendors for our platform-level software OSes. But we realized that to bring this full solution to customers, we really had to go deeper than that. And this is a place where the market's a little bit more developing in terms of the ISVs that exist to do some of this AI. So if you think about how fast AI is really exploding, at the same time, there are all these really, really inventive and imaginative companies that are coming through and they're able to very quickly get to market with solutions that help our customers build really vertically specific solutions, for example, into retail, manufacturing, healthcare. And so we wanted to be able to build an ecosystem and really a mechanism to onboard those types of partners, knowing that there's a lot of models. It's still pretty fragmented environment that we get today. Right? And that's kind of the innovation that's coming from all directions and generated this new world that we live in from ISVs. So what we did is we created a program through working with our partner teams. So I'm sure they're happy that you mentioned how you've noted their proliferation in the past, but we have this new initiative called Cisco Compatible AI Solutions. And what that does is it gives us a mechanism to (a) go to some of these vendors. Like if we talk to a customer and they say, "Hey, I'd really love to work with ISV X, Y, or Z," we're able to go to them and say, "Hey, we have a mutual customer. We'd love to work together with you." And we can allow them to do testing and validation and sizing and all of that on top of our Cisco Unified Edge and actually can even work the other way where they can see that some of their customers leverage our ecosystem internally and they can come to us for that same level of capability. Right? So we can give them access to gear, we can onboard them, we can make sure that things are validated in terms of the sizing and some of the performance capabilities and characteristics that they can take advantage of and make sure that all of the pieces we have and all the CPUs and GPUs are all something that they've had experience working with within their ISV. So that program has been going for the better part ... Really since a little bit before we launched the platform, and it's really starting to take off. And the team within Cisco that is working on that has just done an amazing job of creating this really ... I think it's a really wildly cool ecosystem because if you think about the way we demonstrate this, it's really fun to demonstrate things like the end product here, right? So showing maybe a hologram that's sitting in a retail shop and it's giving customers directions to purchase the store, to purchase gear, or some of those really engaging type things. It's much more fun to demonstrate that and show the return on investment for the full solution than, say, showing them how redundant and resilient our hardware is by itself. So we really need that partner to take that full-stack solution over the finish line. Right? That's that last mile. So we're really excited to be building this Cisco Compatible AI Solutions.
Dave Vellante
>> Well, that's great. I mean, with such diversity, especially at the edge, you need a leader like Cisco, but as I say, you can't do it all yourself. So those validated designs across that vast set of hardware and software suppliers lends itself to building trust with customers. I want to ask you about some of the business impact. So when customers deploy this solution, what are they really after? What's their expectation? Is it cutting cost? Is it operational resiliency? Are they looking for new revenue opportunities? All of the above? Maybe you could describe what's driving the urgency.>> I think it's a lot of all of the above, honestly. So when I talk to customers, it's very vertically specific. For example, retail is a great place that I think they have long looked at things like customer experience, as well as back office efficiencies, as something that they monitor and they have a lot of metrics around how they flip their inventory. And if you think of some of the more advanced-type retail outlets out there, they look at product placement and they look at the path a customer takes through the store. And to date, some of that has just been, well, we have a little bit of anecdotal evidence that these products sell better if they're in these placements or that these types of products have a dependency on the weather or the season on how they sell, but now, they can really take that and really wrap data around all of that and really fine-tune some of these things that they , maybe these initiatives that they had, and they can very easily now attach metrics around what's the ROI? And that's where it gets really exciting, is you're giving customers the ability to make a change in their business model and then quickly address and adapt to whatever the outcome is. In the past, the cycle of trying to trial and error took a long time and it was very inexact. I think now we have the tools with computer vision and some of the other types of ... That data has really been there for a while, but we've never really been able to take advantage of it. Right? We've known where the people were coming in our store, we could tell you how many came in, but we couldn't always tell you exactly where they went. Now, we can tell you where they went, and if we can attach that person to, did they make a purchase or something like that, I think that's where the customer gets the ability to take advantage of some of this and actually turn it into revenue increases, to turn into new sources of revenue, to turn it into really just being more efficient on the front end. And then you turn that into the backend, they get the same type of capability. Right? So if I can manage my inventory to even much more critical level, maybe I don't have to store as much, maybe I don't have to take E&O losses on produce, or some of that inefficiency that exists constantly in those types of environments can go away. Right? And if I can clean that up on the back end, now, I take these new sources of revenue and I take the more efficient path to get there, and that just translates to really easy-to-track ROI. And that's exactly what the name of the game is for a lot of our customers, is can I make the investment and can I show that it's going to rapidly pay off?
Dave Vellante
>> Excellent. Yeah. Show me the value and I'll buy more instead of the . I want to end, Jim, with the future of edge. We spent years talking about the data center and then obviously, over the last 10 years, the cloud as the center of gravity for enterprise computing. AI is not going to wait for the data center or the cloud. As you look ahead in three to five years, how do you see the edge evolving? What's your vision?>> I mean, first, it's incredible to think that three to five years is the future. I mean, three to five months could be pretty incredible, honestly, but if you take the trajectory that we're on, I think that we're going to see some really amazing, amazing technologies continue to evolve, because the way I look at it right now, we're bringing a lot of technology together in the Unified Edge and this type of a model to give to customers, and they're out looking at these different ISVs and they're starting to bring maybe different types of AI technologies into their ecosystem. And as those start working together, the agentic AI push here, this trend, this is where we're going to take all of these different agents, and pretty soon, these agents are going to work together and create things that we're probably not even considering possible today, but that's the kind of efficiency that we're going to have in the very near future. So pretty soon, we're building the runway here and this plane that's taking off is going to be the machines controlling the machines even more. Right? And when that happens, we make the path or the time to these new technologies maturing much, much smaller. And that's where if I had a really good crystal ball, I'd probably have a different job right now, but I can say that from where I'm sitting right now, it's going to be an incredible ride.
Dave Vellante
>> Well, and I think you're talking, you're hinting at something we've been talking a lot about on theCUBE Research is a new operating model, and that new operating model that's running at machine speeds requires new thinking on the infrastructure to support the agentic era. So James, first of all, congratulations on Cisco winning the 2026 Tech Innovation CUBEd Awards for most innovative IoT or edge platform for the Cisco Unified Edge. Really appreciate your time and the insights and congratulations again.>> Thank you very much. The team did an amazing job.
Dave Vellante
>> Yeah, well done. And thank you for watching theCUBE. Stay with us for more coverage and more CUBEd Award winners. This is Dave Vellante, and we'll see you next time.
Dave Vellante