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In this Tech Barometer podcast interview, Insight CTO Juan Orlandini describes IT modernization challenges hitting simultaneously with the rise of AI. This interview was recorded before CRN reported that Broadcom had deauthorized Insight to sell VMware licenses and Broadcom-funded VCF Entitlement Services in North America.

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Jason Lopez: This is the Tech Barometer podcast, I’m Jason Lopez. When thousands of VMware resellers were deauthorized  last year, many customers needed a new provider of VMWare licenses. One of those resellers was Insight Enterprises. It was reported by CRN in September of 2026 that Insight would no longer be authorized to resell VMware licenses in North America. But in an earlier interview prior to that CRN report we talked with Insight's CTO for North America, Juan Orlandini, who says this is the kind disruption they are built to handle.

Juan Orlandini: These are all knobs, and every knob is connected by a piece of string. And if you turn one knob, the other knob goes in the direction that you probably didn't want it to go. The job becomes, how do I adjust all these knobs that are linked together to find the optimal solution for each client? And that's not always obvious or easy or even clear until you're well into the process.

Jason Lopez: He mapped out the some of the big factors that make this so complex: dozen of decisions, tangled together, and none of them are happening on their own.

Juan Orlandini: We're going through a number of inflection points simultaneously. It wouldn't be a conversation without AI, obviously, so that's a big one. The other one is this post-Broadcom world that we're living in.

Jason Lopez: In the post-Broadcom world, how companies think about how they're running most of their data centers is being rewritten. When Broadcom bought VMware in 2023, it didn't just change who owned the company, it changed how the product got sold.

Juan Orlandini: The dynamics that Broadcom has introduced is that they want to bundle their products so that customers buy into more of the ecosystem that is included in the bundled package. Now, the challenge is that many customers either can't or don't want to do all of that deployment. They don't want to deploy all of the capabilities that are included in VCF. It is a tall order. It's a big shift. If they don't, well, then the value proposition that Broadcom is bringing to the table goes out the window. It's too much, and you have consternation. There's also arguments to be made that this was played a little bit heavy-handed by Broadcom, so that's caused some ill will. For some customers, it's not a matter of technology. It's just a matter of, like, hey, I'm going to find something else, flat out.

Jason Lopez: VCF is VMware Cloud Foundation, the bundle that Broadcom wants everyone to buy. But Orlandini's read on this moment goes past pricing and packaging. He sees it as the end of an era when VMware simply didn't have to compete for anyone's business.

Juan Orlandini: Right or wrong, while we were living in the pre-Broadcom days, VMware dominated the market like almost nothing else has ever before. In a large sense, a lot of innovation had stagnated because there was no competition. We were literally at the behest of what VMware thought was going to be next. Well, now that there is a healthier market, innovation has really started to creep up, and we're seeing some amazing technology and stuff that I don't think we would have ever seen before coming out from people like Nutanix and some competitors that are really pushing operations, optimization, performance, and all these other dynamics in ways that are just extraordinary. In the long term, I think as an industry, we're going to be in a much healthier spot. But until we get there, this is really, really painful.

Jason Lopez: Painful, yet Juan says it's also forcing a conversation a lot of IT departments had been putting off about what a modern data center is actually supposed to look like.

Juan Orlandini: For many organizations that wanted to transition from running that highly automated IT infrastructure to a private cloud, there is a big operational change that needed to be done. And without a forcing function, there was no impetus to change that because of that inertia that was there. And now this broadcom change has actually created a forcing function that is actually allowing IT organizations to relook at how they've built their processes and their tools and the services that they provide, and then start reconsidering this to really build a true private cloud that happens to include virtual machines and containers as well.

Jason Lopez: That was two, maybe three years ago. He says the mood has shifted since.

Juan Orlandini: Right now, the angry exits, the knee-jerk reactions have largely started to disappear. We're two, three years into this transition, and most people have already been through one refresh of their licensing model, at least, so they know what's coming. So it's not an angry decision anymore.

Jason Lopez: The exodus has not stopped, but it has moderated.

Juan Orlandini: You're probably going to see a smaller amount of exits, but the ones that do are going to be very public. And you can do your own research. There's tons of companies that are very large that have very publicly said, I'm out. But there's also very large ones that have not said. That doesn't make the news, but it's there. It's happened, If you go below the global 1,000, the number is probably going to be higher for exits because broadcom has very clearly said, hey, I'm really particularly interested in those really larger organizations. And for the smaller ones, it's actually much harder for them to adopt all of the capabilities that are built into the broadcom platform. So for them, it makes more sense to exit into something alternative that is a little bit more bespoke or tailor suited for their environment. So you're going to see a higher number for those.

Jason Lopez: Every one of those exits is a client sitting across the table from someone like Juan asking what comes next.

Juan Orlandini: For the customers that are choosing to exit from broadcom, the VMware technology stack, then for us, it's really an opportunity. And what we work with our clients is to understand what are their workloads really doing? What are the kinds of capabilities that are needed in order to support those workloads?

Jason Lopez: And where is the best place to put those? And what those clients want from him is not a list.

Juan Orlandini: What customers are not looking for is a catalog of things to buy. They're looking for an opinion. They're looking for somebody to come in and understand their business, become a true partner to them and come with an informed opinion that says, hey, I have access to all of those technologies. But here's my opinion as to which one actually fits you best. And it's not an agnostic approach. It's an informed opinionated approach that clients are looking for.

Jason Lopez: Juan Orlandini is the CTO for North America at Insight Enterprises, an Arizona-based IT firm providing services like cloud migration, cybersecurity and digital transformation. This is the Tech Barometer podcast, I'm Jason Lopez. Tech Barometer is a production of The Forecast. For more podcasts and stories on technology and also the people in tech, you can find them at theforecastbynutanix.com. That's all one word, theforecastbynutanix.com.

 

Nearly half of women in the workforce are navigating perimenopause or menopause, yet misinformation, social media influence, and a rapidly expanding menopause marketplace are increasing demand for care and making it harder than ever to separate evidence-based guidance from hype.

In this episode, Dr. Paula Briggs, a menopause specialist, researcher and former chair of the British Menopause Society, shares the latest developments in menopause and perimenopause care. Drawing on decades of clinical experience, she shares what women need to know when it comes to managing symptoms, when hormone therapy can be beneficial, and how to parse through the advice offered on social media.

The growing demand for menopause care impacts benefits strategies and workforce well-being. This episode looks closely at emerging treatment options, realities and misconceptions about hormones, and how women can best navigate this complex and rapidly evolving care landscape.

Speakers

Paula Briggs | Consultant in Sexual and Reproductive Health at National Health Service and former Chair of the British Menopause Society

LuAnn Heinen | Business Group on Health

Healthcare IT veteran Benjamin Urquhart says AI can transform patient care, but only if it fits clinician workflows and tames shadow AI before it scales.

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Benjamin Urquhart: AI, if done and implemented correctly, and I believe the healthcare environment, it has to be done thoughtfully. It can be amazing. It can be. I don't want to get on the hype train and go, "It's going to solve everything," because we've had those solutions in the past. It's going to solve everything and it never does. It might come close, but it's going to miss some pieces that we just don't realize. And AI has rapidly evolved and it's been adopted at such a rate that in the 18 years I've been in the industry, I don't think I've seen anything like it. In a sense, it's scary. Because of that in healthcare, all the regulational pieces we have and understanding how our client data is kept, where it's kept, what can access it, east-west traffic right inside with an AI agent can be extremely scary, very vulnerable in that aspect.

And so when we're looking at it from AI and healthcare, in my mind, the biggest thing becomes doing it thoughtfully and then doing it in a way that works within the end user's workflow already. We give them all these things all the time. We tool them to death, right? I'm sure you've heard that phrase. And tools and solutions are not always the way to do it. You can look at their best thing about AI and healthcare, I think we can get rid of a lot of the administrative burden. We can utilize it within their workflows to remove some charting capabilities, do the reporting, take care of these things, but then at the same time, also use it to help analyze that data that's coming into these EMRs and everything like that to where, because it is complex data. Health data is much more complex than I think everyone doesn't always understand.

But we can use AI to better analyze that, giving us better outcomes at the end of the day for our patient care. Because if we're using it in a thoughtful manner to go, we're going to remove some administrative burden and we're also going to utilize AI to help assist you diagnose a client or work through in determination, well, that provider themselves now has a better understanding of what's going on. They're able to spend more time with the client, which gives us the outcome we want and that is helping people. That's the way I view AI in our world right now in healthcare. It's got to be done in their workflow. It has to be done very thoughfully.

Ken Kaplan: With the foundation that you've built and are growing, you could take on new capabilities.

Benjamin Urquhart: Yeah. Anytime companies grow or you have to evolve, it's always kind of a mental check aspect and you kind of go, "Are we really going all the way in? Are we only going halfway in?" But being afraid of the technology as it's evolved, if you are, we might want to look elsewhere. And I don't mean to be mean or so blunt about that, but because tech, my dad, I've grown up around tech. My dad was a mainframe programmer in the banking industry when I was a kid. Going to the Saster test at midnight when I was like seven or eight, no idea what's going on.

And now it's great. The tech has evolved my entire life and everything. One of the things though too, people have evolved and our end users today, they grew up with smartphones. We maybe didn't, but they did. And so they, for an end user and a lot of this younger generation I feel that are coming in, we need to change maybe how we communicate with them a little bit. It's not just a technical solution to them. We need to prove that in value to their life because that's the tech that they've had their whole life. It added a value. Our legacy work environment does not really add a value. We add burden, just the way that systems are designed. So adjusting that and making them work within that is an evolutional piece I think from just being in tech in general. We just have to kind of adapt how we're explaining, how we're talking and communicating more so than just about anything else.

Ken Kaplan: How comfortable are you and your team to say, "This is the year that we're going to do these three things with AI." How do you feel you're able to move into the future?

Benjamin Urquhart: I feel cautious about it. It's one of the things that I know we're going to have to do. Every company is doing it. We have shadow AI now. They were talking about that, one of the keynotes today. And in the past we had shadow IT where people were just. Now we have agents where they're going out and they're, "Oh, hey, because we didn't have something maybe locked down." Well, now there's this agent that no one knows about. So I think when we look at having to do AI, we're going to have to do it. I think it's the way of the world. I think if, like we said earlier, thoughtfully done, it can make massive improvements. But at the same time, we need to ensure that's structured correctly for being in your industry, whether it's healthcare, manufacturing, whatever, you structure it right before you just say, "Hey, we're doing all of this." Standardization.

You might have someone who goes, "I really like Claude. I've got someone over here, ChatGPT, someone who says they don't like Copilot, but maybe Copilot's part of your 365 ecosystem. Why would you come over here unless there's an obvious reason to do so?" So right now it's really more around trying to figure out in a strategic aspect, look at where we are, look at what fits our ecosphere within our organization and making those decisions on how to move forward and where to implement first.

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In a video interview, Five Horizons CTO Benjamin Urquhart explains how hyperconverged infrastructure let a nonprofit healthcare provider scale from zero servers to over 50 virtual machines.

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Benjamin Urquhart: Nutanix for us was honestly a little bit of an easy call. Organizationally, we had no infrastructure in place as far as server backends, everything like that. Being in the nonprofit sector where we are, we're a specialty healthcare provider, so we have a lot of grant limitations on how we can spend our resources. Because of that, we can't really go, let's go throw them in the cloud, let's do this, let's do that. We had to really look at it from a strategical aspect and go, well, what's my ROI coming back in on this? What type of solutions are we trying to do that's still going to allow us to be scalable and grow? We've opened up four new facilities in the last 18 months between Mississippi and Alabama right now. So being able to have something in house with Nutanix for us was great. It was HCI platform, something I'd never done, but let's go.

It gives us our hardware, it gives us a hypervisor. It's different, but let's give it a shot. Licensing became much more easier or simplified. There we go. Nutanix allowed us to go from really not having an internal infrastructure to running over 53 VMs right now internally from different internal resources, whether they're part of our research environment or our true day-to-day activity. So it's been a great marriage from that aspect, I would say, like partnership. It's allowed us to grow and be scalable, but also secure and understanding where our data is and how it lives within our ecosystem.

Ken Kaplan: From that time that you've got things going to the 57 VMs out there, what was that time span and how did things change?

Benjamin Urquhart: So it was a very unique instance actually. I was working for another organization very similar to five Horizons Self-Services and that particular organization was failing. Five Horizons and their board and our CEO, they came in. We had over 3000 clients in our coverage area for that particular organization. They bought all of our assets, everything like that. We retained certain staff members and we didn't close the doors once in the fall of 2022. So from that angle, it was great because we have this infrastructure here that we had already started building with this company. Now we're going to Five Horizons because they've acquired our assets and being able to keep the doors open for patient care.

It was almost like having where the starting line was on your infrastructure instead of completely starting over again like I had the three years before that. Oh hey guys, we're halfway there. We have the cores. We're changing domains. We were changing simplified things. So for us it's just allowed that ability. The biggest push because we already had it was now we're at the point where we're looking at additional blocks and clusters because we continue to expand. Not necessarily a bad thing, but it allows us that ability to keep everything in a manner that we know we can do. We have a very lean team that really have to support not just our Nutanix environment, but our backup environment and everything else on top of it. And Nutanix has really simplified that. I mean they can spin up a new VM now in about 15 minutes from them, which is fantastic. There's a lot less time and delay because of how HP operates.

Ken Kaplan: Now you're like a veteran almost with HCI and what have you learned and what do you want to try next?

Benjamin Urquhart: So some of those things are coming from here right now this week and some of that is kind of going more into the VDI world maybe on top of the Nutanix platform running into HV, doing some more virtualizational pieces around our security and compliance that would help dealing with the industry we're in. Looking at things like that, the containerization aspect, allowing us to spin up more things from an application aspect and having a better use case of our hardware equipment or allocation of whether we're talking storage or our RAM compute, allowing us to trim line those into things like that is really, I guess evolutionary. It's like we build an infrastructure to get our foundation and make sure it's scalable. We build for today and tomorrow where we're going, make sure it's a foundational piece. Well now you also have to look at it and go, I have this foundation.

I need to kind of break it up though and let's evolve it. Let's allow that foundation to evolve and now we're just building buildings out of our infrastructure stacks.

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The promise of cell and gene therapies is big. They have the ability to address the underlying genetic causes of disease, offering new possibilities for people with serious and previously difficult-to-treat conditions. But as cell and gene therapies move beyond just treating rare diseases to also address conditions such as high cholesterol, the market is being shaped by new questions about access and value. The evidence base for existing therapies also continues to evolve, particularly as researchers assess long-term outcomes for treatments that are often approved based on small patient datasets.

Sarah Emond, President and CEO of the Institute for Clinical and Economic Review (ICER), returns to the Business Group on Health podcast to discuss what has changed in the cell and gene therapy market. The conversation explores evolving evidence, how therapies are priced and paid for, provider capacity, regulatory developments, and the long-term durability of treatment outcomes.

As the number of people who become eligible for cell and gene therapies grows, employers face the challenge of connecting employees and their families to potentially life-changing therapies, while also assessing value and managing financial risk.

Tune in for practical insights into new developments, emerging payment options and how stakeholders across the healthcare ecosystem can work together to balance innovation, access and affordability.

Speakers

Sarah Emond | President and CEO, Institute for Clinical and Economic Review (ICER)

Ellen Kelsay | President and CEO, Business Group on Health

In this video interview, Liqid Founder and CTO Sumit Puri argues the path to affordable inference runs through composable AI infrastructure that dynamically taps into pools of GPUs, CPUs and DRAM while optimizing usage to manage costs. He says Liqid allows a single server to access a pool of 30 AMD GPUs to meet performance-hungy AI needs.

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Sumit Puri, Founder & CTO, Liqid: Since we spoke to you guys last, one of the major trends that we're seeing is the enterprises are finally past the initial exploration phase. Now they're in the adoption and deployment phase of their journey of AI. And I think a lot of them are trying to figure out how exactly they are going to do this journey. And the first crossroads that they're up against is, am I going to take all of my AI capability and move it into the cloud? Or am I going to own the infrastructure on-prem or in a colo that's required to do AI? And what it all comes down to is tokenomics. At the end of the day, these companies have a limited amount of dollars and a limited amount of power in many cases that they can deploy. And what they're trying to figure out is how do I get the most amount of tokens out of that very, very scarce resource?

And so those are the discussions that customers are having now. And the cost of these tokens is front in mind for them. If we notice what's happening in the industry, large organizations are now saying, "Hey, we're going to limit the amount of tokens that you as an engineer inside of our organization can consume because the cost of that is becoming very high." And so the one great way to address that increasing cost is to own your own token machine. And so that's one of the conversations that we are having with our customers is one way to reduce the cost of those tokens is bring the infrastructure, bring the GPUs on-prem or in a colo so it's a one-time expense and you can consume all the tokens that you want out of that investment that you make. It is about the model that you are looking to deploy and you're going to, in any environment, you're going to have a variety of models.

[Related: Rise of AI Agents Forges IT Industry Partnerships]

You're going to have big models, you're going to have small models, you're going to have medium sized models. The model actually, the size of the model will dictate the type of infrastructure that you need. If I'm running a very, very large model, I'm going to need a large quantity of GPUs in order to run that model. And so you have to figure out if I'm going to build a very big system, how do I get those large quantity of GPUs into play? One way to do it is independent scaling of resources. We come in and let customers say, "I don't want to scale my compute as I'm scaling my GPUs. Allow me to just scale GPUs." As an example, today we're at the AMD show. The announcement that we're making today is our ability to take a single server and scale up to 30 AMD GPUs to that single machine.

And the benefit of that is we can now start to run these very large frontier models on a single server infrastructure. These models are so big. Previously you had to run them in a cluster. Now we come in and say we can take those models that were previously clustered and consolidate them down to a single server, a single instance, back to that tokenomic story. That's how we reduce the cost of deploying these models. Models will change over time. And so some cases you might want a very small model that will require, let's say, a single GPU. Sometimes you will want a medium sized model that will require eight. And sometimes you will require a massively large frontier model, which will require dozens. You as a customer, it's impossible for you to know ahead of time what size model I will need when. And so our vision is let's take the guesswork out of that.

Let's have a pool of servers, a pool of GPUs, and dynamically spin up GPU quantity to server based upon models. And the reality is the way that'll be done is something called Kubernetes orchestration will be the way that they do it. Nutanix has a phenomenal way of going off and doing that through their NKP platform. We're a big believer in NKP. And so we integrate closely with NKP. And so we say, let's allow people to deploy these models, the variety of models that are supported with the perfect set of infrastructure so we can have this matching of infrastructure to model to not have any wasted resources, improve the tokenomics. In a resource constrained environment, utilization is critical. You cannot afford to have your GPUs at 30, 40% utilization because you can't get enough. And so if we can implement technologies that drive utilization to 100%, we can do more with the same amount of infrastructure.

[Related: Demand Shifts to CPUs to Power Agentic AI]

So that becomes critically important and we do a lot of that today. We look at an environment and we say, "Hey, listen, this workload is only using those GPUs 30% of the time. Let's set up policies that allow you to move those GPUs to different parts of the infrastructure to raise the utilization." The second thing is Brownfield. We support brownfield environments. So imagine an environment where I can't buy servers because memory's limited, CPUs are limited, servers are very expensive. We come in with our solution, which is disaggregated pools of GPUs, and we attach them to existing infrastructure. And so now we're giving new life, new capability to the infrastructure that customers already own. As inference becomes a thing in places like the enterprise, it will only be done in containerized environments. NVIDIA has created something called NIMS, NVIDIA Inference Microservices. AMD has created something called AIMS, AMD Inference Microservices.

And the reason is both of these companies realize the complexity related to kernel revisions, operating system revisions, library revisions is too complex for average enterprise customers to debug and solve, and it slows the rate of AI deployment. So if we can containerize all of these models, put them into a store, allow the enterprise customer to bring whatever model he wants, a vision model, a speech model, a text model, bring it down in whatever size of model that they need, 7B, 70B, 405B, and make all of these available containers that the customer can deploy and implement immediately, that's how we're going to accelerate the pace of deploying AI. The way that we approach that is we have a plugin for Kubernetes that when you say, "Hey, give me Llama 7B as an example," we'll take the container, we'll bring it down, we'll crack open the container, we'll determine the exact amount of physical resources that that container needs.

Well, this needs two GPUs and we want to put it on server number four. We'll pause that container, we'll go into the background infrastructure, we'll put two GPUs on server number four, and then we'll take that container and we'll launch it onto that server. We automate the entire process of deploying models down to a two minute instance. So you say, "Give me Llama 7B." Two minutes later, you're speaking to a chatbot with the exact amount of hardware resources required for that instance. And the moment that that server doesn't need that GPU anymore, we can delete that instance, remove those GPUs, put them back into a free pool so your next model that comes along always has a pool of GPUs to draw from. Liquid started, the way we started was around GPU pooling. That was the first thing that we went off and we accelerated and we composed and we pooled and we shared.

Now the next thing that we have just announced is memory pooling. So we are the first company to provide an end-to-end solution around DRAM pooling built around CXL. So now I can have my pools of GPUs and now I can have my pool of DRAM. The two most expensive things in the data center can now be dynamically allocated by workload. The days of over-provisioning your server with excess memory, those days are done now. And so we must find methods to precisely provision the exact amount of memory the application needs so we can reduce our memory costs inside the data center. And by doing that, it's back to that tokenomics. Yeah. Well, the company is surely in its growth phase right now. I'm a technologist at heart. I'm super lucky to have built out an amazing team. We're very lucky to have a new CEO on the team.

A gentleman named Rick Hagberg, who's just an industry veteran who's been phenomenal. We're raising additional capital. We're growing the team. We're bringing new executives on board. We are having our moment right now and we're super excited and blessed to be part of this industry. The way I feel about it is when we go around and we tell customers about our vision, which is stop putting static resources inside of a server. Let's take all of the GPUs, pull them inside of the rack, add a push of a button, dynamically allocate any GPU to any server that you want. You won't lose any performance. You'll scale up as big as you want. We're a driverless solution. We can drive to 100% utilization. When we tell this story, no one ever says, "Well, that's a terrible idea." That's not the reaction that we get, right? The reaction we get is, "Wow, this is incredible. This is obvious. Obvious is the one that we get more than anything else. This is obviously the right answer of how this should be done. Why isn't everybody doing it this way?" And me, that's my mission as the founder here is to make sure eventually everybody is doing it this way.

Nearly half of all adults turn to social media, at least in part, for health information. This represents a growing concern when considering how the spread of health misinformation has ballooned in recent years. The consequences are significant for individuals who may make uninformed decisions about their health, but they also extend to employers when it comes to utilization and increased costs.

In this episode, Tim Caulfield, bestselling author and professor in the Faculty of Law and School of Public Health at the University of Alberta, explores how social media, influencers, and algorithms have transformed how people discover health information. He examines the rise of "scienceploitation", a phenomenon where scientific language and research are manipulated to promote questionable health claims, and the subsequent challenge to distinguish evidence-based information from hype.

Caulfield also explains why misleading content spreads so widely and rapidly, how it influences behavior, and what we can do to combat it by strengthening trust in credible sources.

From healthcare utilization and benefit costs to employee health and well-being, the adverse effects of health misinformation can have far-reaching consequences. This timely conversation highlights the forces shaping health beliefs today and the implications for workforce health.

Speakers

Timothy Caulfield | Professor in the Faculty of Law and the School of Public Health at the University of Alberta, author of The Certainty Illusion: What You Don’t Know and Why it Matters

LuAnn Heinen | Business Group on Health

Intel IT’s IaaS agentic AI platform delivers comprehensive, cross-domain infrastructure management and monitoring across the enterprise IT landscape. A coordinated mesh of specialized AI agents fuses live enterprise telemetry with configuration, incident, and change data, then reasons across live infrastructure systems to transform fragmented operational data into actionable intelligence. The platform’s hybrid large language model (LLM) architecture materially changes the economics of enterprise AI. We run high-volume, CPU-optimized inference using on-premises open-source models on Intel Xeon processors, which helps reduce token cost while preserving control over sensitive operational data. We use frontier cloud-based LLMs only for complex, low-volume reasoning.

Intel—now Intel Foundry—has been a leader in industrial automation for decades, long before commercial analytics platforms could meet our highly complex needs. Therefore, we developed many of our own analytics solutions. Since then, the software industry has matured significantly and presents an opportunity to shift from a build-to-suit model toward a buy-and-standardize approach. We are now adopting a third-party analytics platform that offers several advantages, including a common user interface and the ability to deliver new analytics services 30–40% faster. It also lays the foundation for the next generation of manufacturing analytics, including unified data and AI.

Financial services firms are pouring money into AI but face soaring token costs, shadow AI risks and data sovereignty pressures. The 2026 Nutanix ECI reveals the gaps.

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Jason Lopez: Banks, hospitals, government agencies, organizations in most sectors are all building AI into how they operate and they're hitting a wall. The compute infrastructure wasn't built to run old systems as well as the new ones at scale, and that means running systems securely without breaking. Nowhere does that strain show up more than in banking and financial services.

Sean O'Dowd: I guess my head immediately goes to, this is going to get uglier and scarier before it gets better.

Jason Lopez: Sean O'Dowd is the head of financial services solutions at Nutanix. This is the Tech Barometer Podcast. I'm Jason Lopez. On this podcast, we talk to O'Dowd about the 2026 Enterprise Cloud Index or ECI. It's Nutanix's annual global research survey on IT trends. When we asked him how financial services are doing overall, he compares what's happening to things like the shift from human labor to mechanized factory production in the late 1800s or the way nuclear technology reshaped geopolitics or even how space exploration, which had a big front end investment, ultimately proved transformative.

Sean O'Dowd: So I say it's going to get uglier before it gets better because as we all appreciate when you look at these awe technologies, the disruption, the market structure changes, especially in financial, this has the potential to really upside a lot. I think you're going to see those frictional and structural pains play out over the next 10, 15 years like you did in these other major industrialization cycles. So it's going to get a lot uglier before it gets better. But I still have a lot of conviction that there will be a lot of positive outcomes here. And we have to be thinking about not just the technology, but how do we govern this? How do we police it? What do we want for ourselves? These are big questions that will get addressed whether we want to or not. They will percolate up.

Jason Lopez: O'Dowd has been an observer of the banking industry for nearly 30 years. He's seen the ups and downs and lately identifies macro factors which map to his earlier comment. Things will get uglier and scarier before they get better.

Sean O'Dowd: Volatility is good for a lot of financials, so are where rates are. So from a business standpoint, these guys, they're profitable. They're posting strong earnings. Regulations are in their favor. So business is good. Being a bank I think is good business right now. However, it's increasingly costly. So the big things that I continually look at are what are the executives thinking about and obviously the tech behind it.

Jason Lopez: The cost pressure is real money. According to Forrester, financial services as a whole is projected to spend nearly half a trillion dollars on technology in 2026, about 17% of total US tech spending. Findings from the Nutanix ECI report provide more insight into how this is playing out. Shadow IT is one of O'Dowd's ugly scenarios. Suppose a loan officer asks ChatGPT to summarize data in a complex customer file. Suddenly data like a social security number sits outside of the bank's firewall. It's just one of a myriad of weak links. According to Nutanix's ECI report, 86% of financial sector executives believe shadow AI tools introduce severe business risk.

Sean O'Dowd: Yeah, for me, it's expected, but also surprising given the FinServ industry has some of the most mature risk management discipline across sectors. They manage credit risk, market risk, op risk with a lot of rigor. The fact that two-thirds of them are discovering AI tools are deployed with zero oversight. I think that gap between how seriously they take every other category of risk and how exposed they are on that government as this percolates up top and as oversight sees and hears, they are just turning the screw on that operational oversight.

Jason Lopez: AI is pulling most of the tech world toward the public cloud, but financial services isn't fully going along. 79% of FinServ's IT leaders call data sovereignty a top priority and enough caution that it's capping public cloud use at just 62%. Odowd says the reason isn't really about any one company. It's about what happens if too much of the financial system ends up depending on the same few points of failure.

Sean O'Dowd: We're really worried about concentration risk with a few IT players. We're also concerned about sovereignty. So how do we protect the financial infrastructure so that there's not disruptions? Yeah, it's not really a play against AWS and so forth, but it's just ensuring that things like payment transaction doesn't fall away or trading markets aren't disrupted from this thing. So that's the big one.

Jason Lopez: Banks are pouring money into AI, but it isn't clear if it's paying off just yet. O'Dowd talks to CIOs across the industry regularly and notices some common blind spots, including managing the cost of using AI.

Sean O'Dowd: There's fatigue in the way that it can present actual risk to adoption long term. And I think that's moving away from what we saw last year was build it and they will come. That fatigue is just forcing it on employees. Cost and management obviously is huge. When you have a tool that's generating a hundred thousand in savings, but you spend a million on tokens to get there, what's the true value of that?

Jason Lopez: One of those cost structures is tokens. The per use currency that AI models run on and one that's quietly exploding.

Sean O'Dowd: On token usage, it's up 500% in one year. JP Morgan even talked to the fact that they have employees that are spending more on tokens than their own salary. The other thing that's come to bear is not just the cost, but how to track it. So there's just a massive lack of transparency into the bill that they are paying on a monthly basis. A lot of these guys just don't have that granularity and able to say, "Hey, it's costly, but it's worthwhile."

Jason Lopez: The lack of transparency isn't just a financial headache. It's a symptom of exactly which banks have their AI infrastructure under control and which don't. The ECI report cites the experience of Fairway Home Mortgage, which has streamlined mortgage processes and preserved high governance standards while rolling out agentic AI. O'Dowd points to the democratization of data and information.

Sean O'Dowd: If you go back 10 years ago during the Hadoop and big data wars, there was a lot of promise there. But as we all know, AI took what was almost like a data consolidation exercise within these organizations across all types of data to actually being able to really unlock value at speed with it. Yes, intelligent applications, yes, omnichannel. Yes, better intelligent information driving it and automating workflows. But cloud didn't prove to be the easy button, although it's accelerated. Fast-forward, you find yourself, I think a bank CIO having to manage the dual architectures that you've built or even more between on-prem, cloud, co-location. So it's just gotten entirely way more complex. We all know this. The data problem, they've done a good job, I think over the past five, 10 years trying to build out those databases, consolidate and catalog that information. It increasingly becomes harder with AI. And this is where the chief data officer really I think are key. And that is because they've spent a lot of time trying to catalog it in big data for big data elements and machine learning. The data tagging, what I'm seeing is because foundational models become so expensive, they want to use open source models. But in order to use that, they've got to do what they've done previously, which is build up that data hierarchy, data catalog, data tagging in order to make those cheaper, sometimes lower quality, intelligent foundational models that are open source.

Jason Lopez: Knowing why AI is expensive is one thing, but building infrastructure that can actually handle its demands is another. ODOWD explains why banks are getting better at managing existing and new applications, which are typically built with cloud native containers.

Sean O'Dowd: AI workloads, they're dependency heavy, they're expensive to run, they're bursty, and they're increasingly modular. And containers helps address those pressure points all at once. So AI adoption I think is pulling that container adoption along with it. I think what's different, at least the way I'm seeing it from my seat, is what's the mix of new applications being built and what's the mix of legacy applications that are changing within the bank? There's a real come to Jesus conversation right now in terms of which ones to tackle. We increasingly see consolidation in the market as a means of survival. There once was 40,000 banks in the US, now there's 10,000. To compete, you've got to outspend and out maneuver. You're just going to see fewer banks doing it well. Those that can spend, those whose management can steer through. Those that get larger and consolidate are kind of going to win here.

Jason Lopez: Sean O'Dowd is Nutanix's head of financial services solutions. You can read the 2026 Enterprise Cloud Index at nutanix.com/enterprise-cloud-index. There you'll find industry-specific reports for financial services, healthcare, and the public sector. This is the Tech Barometer Podcast. I'm Jason Lopez. Tech Barometer is produced by The Forecast. And you can find more podcasts like this and articles about the tech industry and the people in tech at theforecastbynutanix.com. That's all one word, theforecastbynutanix.com.