#269 Jason Hardy: How Hitachi Vantara Is Powering the Future of Enterprise AI

AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. In this episode of Eye on AI, Craig Smith sits down with Jason Hardy, Chief Technology Officer for AI at Hitachi Vantara, to explore what it really takes to deploy AI at scale in the enterprise, beyond the hype. Jason shares how Hitachi is building a pragmatic, outcomes-driven AI platform through Hitachi iQ. From working with NVIDIA to integrating agentic AI into operations, this conversation unpacks the infrastructure, mindset, and strategies needed to move AI projects from experimentation to production. Whether you're navigating AI adoption, battling with data readiness, or looking to build your own LLM-powered applications, this episode offers invaluable insights from a company that's actually doing it globally, sustainably, and at scale. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI (00:00) Preview (02:10) The Role of CTO for AI at Hitachi Vantara (05:38) Applying AI Across Manufacturing, Energy & Transport (09:54) What Is Pragmatic AI? (13:21) Infrastructure Demands of Generative AI (14:47) Why Most AI Projects Fail (20:25) Inside the Hitachi iQ Platform & NVIDIA Partnership (25:42) Building a Model-Agnostic, Hybrid AI Stack (32:08) Beyond Selling GPUs: Delivering Real AI Outcomes (38:09) Supporting Hybrid Deployments Across Cloud and On-Prem (42:02) Rethinking ROI: Failure as a Strategic Advantage (47:44) Agentic AI and the Future of Autonomous IT Workflows (49:37) Five Core Domains of Agentic AI at Hitachi (53:02) Making AI Infrastructure Sustainable (56:48) Hitachi's Vision for the Future of Enterprise AI

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Pragmatic AI is, is how we're, how we have a pragmatic approach to providing AI capabilities. I E. I'm not promising the world to our customers. I am painting a vision that we can get there through this very methodical approach where we think through it. We are very agnostic. What we wanted was to create something that's easy. So that it can be as much about the solutions that we bring to the customers as much as a factory for customers to build their own AI outcomes within. Our strategy obviously is to sell product, is to solve these problems with, for our customers. But it's not to just sell them a bunch of GPUs and walk away. It is to make sure that they're getting the desired results to actually see that outcome.

• 0:45Speaker 00

  That 90% failure rate is well worth that 10% success rate because it more than pays for itself. So that I think we'll continue to see now, obviously, again, it's just like going to the gym. The more you exercise, the more you become, you, the more better you get at these things, the more success you see. So from our standpoint, as we see more engagement, as customers start to work on this more, they'll start to see fewer failures and more successes. Uh, my name's Jason Hardy. Uh, I'm CTO for artificial intelligence at Hitachi Vantara. Um, I've been with the company for, oh man, almost 13 years now. Um, I've, my kids have seen nothing but Hitachi, so it's, it's been fantastic.

• 1:29Speaker 00

  Um, I was actually a customer beforehand and I came over, uh, through a transition. And, uh, yeah, I've been really enjoying it ever since. My background has been in data and in, um, data analytics. Not from a data science perspective, but how we actually apply data into, into solving for some critical problems. And that brought me into, um, our data portfolio, supporting our object storage technology and things like that. Just through the natural progression of technology and how the market evolved. Um, I spearheaded the effort to create an AI brand, um, behind, uh, Hitachi Vantara or within Hitachi Vantara, um, and that is Hitachi IQ. So. That is

• 2:13Craig S. Smith?

  Yeah.

• 2:13Speaker 00

  This laborious process that I went through and I love every minute of it. And now I'm, I'm leading the charge as far as what AI means to us as an organization and, and how we can create a different paradigm within the market and really help our customers create something that's relevant and start to see a lot of value from this technology.

• 2:33Craig S. Smith?

  Yeah. And can you sort of put Vantara and Hitachi IQ in the context of Hitachi, which most people know from at least my age.

• 2:47Speaker 00

  Yeah.

• 2:48Craig S. Smith?

  Yeah. Electronics, consumer electronics company, but obviously it's much more than that.

• 2:54Speaker 00

  Yeah, it's a massive, yeah, so we are a division. We are several layers deep inside Hitachi Limited, the, the larger multi-conglomerate Japanese organization.

• 3:09Speaker 00

  I think there's now about 900 business units inside of Hitachi Limited and we're one of them. So we range from everything from trains and nuclear power and transformers and consumer electronics, like you said, like rice cookers and things like in microwaves and things like that, all the way into the IT capabilities from what we do inside of even consulting services into product engineering for customers as well as obviously engineering our own products. Hitachi Vantara formerly known many years ago as Hitachi Data Systems is kind of like the IT arm effectively of Hitachi Limited as in we sell data storage technology to

• 3:52Speaker 00

  many, almost all of the Fortune 100 and beyond organizations, everything from commercial to large enterprise to government and, and beyond. So from our standpoint, it's a bit different for a company who makes trains and transformers and things like that to also have this very predominant and prestigious data platform business. And really the, the core behind that is the fact that we understand across all of the. Big things that we do, that data is still the most valuable thing that our customers have and that we that even ourselves create. So having a

• 4:35Speaker 00

  a capability to deliver on data outcomes, data technologies, whether it's storing it or activating it or consuming from it or whatever it is, it's very critical and it's critical to how our customers succeed. So this data business, living inside of this massive Hitachi organization, is actually a natural fit because of that that need for manufacturing and energy and transportation and logistics, all these different types of businesses rely on it so heavily. So for for us, it's it's really capitalizing on what we're good at. And IQ, Hitachi IQ, which is the AI portion of Hitachi Vantara, takes that one step further. As we know and have been said in the market frequently, that

• 5:20Speaker 00

  data is the fuel for AI, and I wholeheartedly believe that. So again, it's this natural fit for who we are as a company, being very data centric, and how we can use that and capitalize on that capability we have to then drive AI outcomes into the market. Now, taking that one step further, when we look at Hitachi IQ, yes, it is the infrastructure, yes, it is the compute and the GPUs and the data and how we harness it and the IT pieces of it. But it's also having an awareness and bringing in other parts of the Hitachi Limited machine to help customers realize the relevancy and to align with them on their thinking in the industries that they work inside of.

• 6:00Speaker 00

  So we actually make trains, so we can actually do AI for trains. We make, obviously, all the power grid stuff. We can do AI inside those systems and into those business units and and customer types and financial services and et cetera, et cetera. So really, our strategy and everything that I created around Hitachi IQ and how we're executing is as much on the infrastructure side and where we've been really good at for 50 years now, from a Vantara perspective, to as much as the outcomes and solutions we create with that point of view and that expertise in the industries that our customers execute within. So it's blending now across those and just like we're having a data business inside of a very large manufacturing company like Hitachi.

• 6:46Speaker 00

  Now having an AI piece of it inside of a very large manufacturing country, a company like Hitachi is kind of that natural evolution to how we can create a lot of really pragmatic and capabilities out there.

• 6:59Craig S. Smith?

  Yeah, so Hitachi IQ isn't a product or or one system, it's a collection of.

• 7:08Speaker 00

  It's a brand, yes.

• 7:09Craig S. Smith?

  A brand.

• 7:10Speaker 00

  It is, it is infrastructure. We do have SKUs and GPUs and we've partnered with Nvidia on collaboration and bringing product in. But you could consider it a kind of an umbrella around what we do for infrastructure. So we have GP Nvidia GPU systems. We have storage technology for parallel file system functionality. But it's also the solutions that we bring in into it as well. And and we call those the Hitachi IQ for solutions for an outcome.

• 7:41Craig S. Smith?

  For of, I'm sorry, for the number four or?

• 7:46Speaker 00

  No, for like F O R. So Hitachi IQ for manufacturing, Hitachi IQ for energy. So.

• 7:53Craig S. Smith?

  Right. And and are those

• 7:57Craig S. Smith?

  SaaS products or is that a consulting business?

• 8:01Speaker 00

  So it's, it is. It's not SaaS. It is consulting, obvi- absolutely, because it is so heavily bespoke. But it is also turnkey. And I know that's like, wait a minute here. So what we've been able to do, and this is through decades of having a capability in the AI space. We've been delivering outcomes to customers to do tremendous things with AI for decades. What we've been able to do is through that learning, we've built product. Now we built product in a bespoke way for a customer or for an outcome. What we've been able to do is take that product that we've built.

• 8:39Speaker 00

  And poured it into Hitachi IQ's portfolio so that we actually have skewed outcomes effectively that say, hey, this, let's, we can make up like, we have, we have a fault detection system or a guided repair platform that uses generative AI where you feed it a service manual or many service manuals in some cases. And as a service engineer, you can describe the problem and it will give you a diagnostic tree and it has an LLM that helps feed that and power that process. That is an outcome that lives inside of Hitachi IQ that we sell as a capability. Now, it's not saying, hey, you buy Hitachi IQ in this frame and then you run just this thing on it.

• 9:24Speaker 00

  That's just one of the many things that can be used inside this shared resource from an infrastructure perspective. So that's why I say it's as much infrastructure and what we deliver into data centers as it is the outcomes that we put on top of that to then help our customers realize the potential that for AI to improve on, on their business and, and, and, and give them a, a bigger capability.

• 9:47Craig S. Smith?

  Yeah, I see. Can, can you tell me what pragmatic AI is?

• 9:54Speaker 00

  Yeah, of course. Um, yeah, so.

• 9:58Speaker 00

  AI has been kind of framed as this, and I agree from a, from the big vision of it, it is going to transform how we do business. It, a genetic AI is going to drive our productivity increases exponentially. Obviously, there's a lot of technology hurdles and things like that that we have to get to, but we're starting to see some of the fruits from that labor. Now, what pragmatic AI is, is how we're, how we have a pragmatic approach to providing AI capabilities, i.e. I'm not promising the world to our customers. I am painting a vision that we can get there through this very methodical approach where

• 10:39Speaker 00

  we think through it, but day one, don't expect it to completely revolutionize how you do anything. Day one, you start to cut your teeth on it. AI itself, generative AI and agentic AI especially, they're still going through teething pains. I I call it kind of it's going through puberty right now. It has to grow up very quickly and especially at the rate at which it's being adopted and the rate it's being evolving. It is growing up and maturing very quickly. It's being forced into it. But we still have to be practical about how we think about this and we have to be grounded in reality that, yes, there is some fantastic capabilities that can come from this, but let's be pragmatic here.

• 11:22Speaker 00

  Let's go after things that maybe that low hanging fruit that where we can see some success or we can learn from it. But let's also not try to transform the core of your business step one, where you end up on front page news when something goes wrong. So I kind of I call it going after the peripheral of the business, kind of that edge. And then get maturity in that space and those use cases and then start to work your way into the core as you gain and exercise that muscle.

• 11:48Craig S. Smith?

  Yeah. How is gen generative AI changing the game when it comes to AI infrastructure demands?

• 11:58Speaker 00

  Um, it's increasing it exponentially. When we talk about traditional AI, that machine learning capability, even things like robotic processing is a form of AI. It it could be done in CPU. It could be done. I have Raspberry Pi at home and I like to tinker with and stuff. You can run AI on that. Now, at the same time, though, that that is not generative AI. Generative AI has created this mathematical paradox of how the transformer operates. And so in that, in in the core of how generative AI works, it has caused this exponential increase in the need for processing power. Now, there are certain ways to cheat the system and you can do things you can do with lower end systems.

• 12:44Speaker 00

  I run L L M's on my Mac, uh, on my MacBook, uh, which is great. It's, it's, it's great to be able to experiment with on that. But when you look at it from an enterprise perspective, especially adopting generative A I. It has such a tremendous amount of one data necessary to build relevance within the model through things like retrieval augmentation. Or even how you fine tune your model, but it's also how your end users interact with it. So it's very Wild West almost to where it's not a predictable workload. So now the more you use it, the more you're like, wow, this is really powerful and I can see how it can improve on my business. But at the back end, to get to that point takes a lot of effort, but also a lot of infrastructure to be able to do that.

• 13:28Speaker 00

  And as your users get more accustomed to it. It's only increases more in consumption because they know they, again, it's exercising that muscle. What we also see too is that. Users, if you don't give them access to it, they will go figure out a way to use it anyway. And in most cases, it's using things like chat G P T or Gemini or Grok or any of these. Um, oh, these open and available free systems. Nothing's ever free because that data is being utilized for training. So then we call that shadow A I. So there is this, again, this like, it's a huge amount of inner infrastructure. But at the same time, your users are going to figure out a way to do it if you don't make that investment into the technology. So.

• 14:11Craig S. Smith?

  Yeah, uh, the, what are some of the mistakes you see? Because everyone's experimenting with the generative A I and our agents.

• 14:22Speaker 00

  Yep.

• 14:23Craig S. Smith?

  There isn't that much in production yet, but what are the common mistakes you see that lead to failures?

• 14:32Speaker 00

  Yeah, a lot of it would be the data and it's kind of multiple folds here. One of them is data where your data estate, not a hundred percent of it, but take that use case that you're going after. Let's say it's again the guided repair thing or the maintenance thing. If your data is not in a state that you can accurately consume from it, it's a garbage in garbage out problem. So obviously the output coming from the system is going to be flawed because the input going into the system to give it context is flawed. So this unprepared or un-unclean data, for lack of a better term, causes a lot of this consternation around, well, I'm not getting what I expected out of it, so therefore it's a failure.

• 15:17Speaker 00

  Well, it's maybe it's because we didn't do all the pre-work ahead of it. Also, another area that I see is where customers have an expectation that AI is a product. It's not a product, it's an outcome, it's a solution, it's a, it's a, it's a tool. It's not like SQL or it's not like a database even to at the most natural form. It's, it is an outcome, it is a solution set. So in that is having that expectation that I'm buying a product, so I just should work flip of a switch. And that's definitely not the case. So it's having an unhealthy expectation going into it, going back to being pragmatic about it. The other side of it though, too, is where I see that there's this rush into AI, executive level saying, we need to do AI.

• 16:02Speaker 00

  So they're trying to figure out AI and the right parties aren't involved. Having a successful AI outcome, generative or otherwise, really is focus on generative and agentic. It, it's a multi-discipline thing as in you need IT as much as you need legal and HR and the solutions that you're trying to solve for to be involved in it. So don't just expect IT to give you AI and what they go turn on Copilot or something like that and call it done. That's, that's not a success. So having all the right parties involved, having them evaluate, okay, major, what are my KPIs, me from the point of view that I have in the role that I'm in? And how does that play into the AI outcome?

• 16:44Speaker 00

  So not having all the right people involved really causes problems there. So that's why we, what we built is this service to even help bootstrap some of this that we call discovery. And its intention is to pull all the right parties together. Its intention is to pull all the, to analyze and understand the data, to understand the business problems that we're solving for. And make sure that everything aligns so that when we do go build a proof of concept or go into a pilot, that everything is set up beforehand to be successful. And occasionally it still fails. Maybe it's just not the right time or the, or you don't have enough of the information. Your information's clean, but it's just, there's not enough detail or the expectations weren't right, whatever it is.

• 17:30Speaker 00

  Or it's successful. But the point is, is that you're getting all your ducks in a row beforehand, before you start to click go on that. And then obviously that leads into a lot more successes. Um, and this ideation process also helps flesh out what are the use cases we should even tackle? Yes, we have many problems inside of a business. Again, let's start at the edge. What are those low hanging fruit? Things that we could use to, uh, to, um, to, to show value from an AI perspective?

• 17:59Craig S. Smith?

  Yeah.

• 18:02Craig S. Smith?

  What is over provisioning?

• 18:07Speaker 00

  Over provisioning could be a lot of things. Yeah.

• 18:09Craig S. Smith?

  Why is it a problem?

• 18:12Speaker 00

  Yep. Over provisioning from a compute perspective basically turns into where you're throwing far too much workload at an environment. Where I'm over provisioning it for, for, um, it, it can be kind of twofold here. I'm over provisioning from a, uh, from, I'm throwing too much at it and it's not, um, it's being oversubscribed. It's being taxed. So you start to see these, these choke points along the way. The other, so now as I'm interacting with it from an end user, if I have to wait a minute or 30 seconds. Imagine if you went to chat GPT or whatever generative AI platform you use. And you type your question in or whatever it is you're doing, and then you hit wait and you hit enter. And you don't get a response back for 30 seconds, 45 seconds, a minute.

• 18:58Speaker 00

  You're going to think something's broken here and I'm going to refresh the page and I'm going to try again. Well, that's because behind the scenes, there's not enough resources to be able to support that ask that you're asking. And that's an over provision problem, an over subscribe problem. The other side of it, though, is where you over provision your resources and you don't have enough workload to keep them all busy. So now you've spent too much money to actually um to support the need that you have. Now, there is obviously where you want to have some margin in there. But uh but yeah, from an over provisioning perspective, it can be either not enough or too much technology um to to support what you're doing. And then obviously, there's cost implications from that.

• 19:39Craig S. Smith?

  Yeah, uh and so uh Hitachi IQ, do you guys have your own suite of models? Or are you uh the the the solution provider and a model agnostic?

• 19:53Speaker 00

  We are very agnostic. Uh what we wanted was to create something that's easy. Now, what we have done, though, is in our solutions, we've embedded models. So at GTC 2025, um it was Jensen announced the AI data platform program. We are a part of that. We are a part of the NVIDIA AI data platform program. And that really is the initiative to bring data and inferencing closer together. Um and and it's really important for storage companies to to provide that because obviously we're the keepers of the data. So in the solutions that we build for this, we utilize things like Lama Nematron, um which is an NVIDIA model that was created.

• 20:35Speaker 00

  But at the same time, too, we use other models, maybe Lama four or Lama three even, or different whatever you can get from Hugging Face that we picked at the time. We also are building our own models from a Hitachi perspective for the industries that we work inside of. Those aren't released to market yet. Those are still under experimentation um and under under design and being built. But we we really designed it to be agnostic so that it can be as much about the solutions that we bring to the customers as much as a Factory for customers to build their own AI outcomes within. And that's why Nvidia's AI factory concept makes so much sense is because it is this

• 21:16Speaker 00

  agnostic engine that they can use our tools, they can use their own tools and and then our tools include things like Nvidia AI Enterprise. Or it could just be a bunch of compute resources that they use for whatever they need to, and they never call us again, except for when they need more resources. So it's it's really goes both ways and it it's up to the customer on on how they want to consume it. Now, we prefer, obviously, that they stay in in kind of the the the ecosystem that we've created for them and we build these solutions with them and all that great stuff. But it is designed to be flexible so that the customer can do what they want.

• 21:52Craig S. Smith?

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• 22:36Craig S. Smith?

  The Agency is dropping code, specs and services, no strings attached. Build with other engineers who care about high-quality, multi-agent software. Visit agntcy.org and add your support. That's A G N T C Y dot O R G. Yeah, and are most of your customers is the attraction that Hitachi has these other business units, so you have expertise in, as you say, railways. So if if

• 23:17Craig S. Smith?

  an analogous company, whether it's railways or something else in transportation, when they're looking for a partner, they they say, well, Hitachi does this already, so they would have a better handle on what we need.

• 23:34Speaker 00

  That's a part of it. I think it kind of goes, it's a couple of things. One, obviously, it is, yes, we have, we have an expertise that's unique. That where we, we build the thing that we can also support from an AI perspective. So we know both ends of it. We're also a manufacturing company, a heavy manufacturing company. So we understand manufacturing and how AI can improve on supply chain and the process and all that stuff. So it's as much that, though, as it's also that we're very trusted. We've spent fifty years building a brand, Hitachi Antara, Hitachi Data Systems, on a one hundred and fifteen plus year old brand being Hitachi. In that, Hitachi is synonymous with quality.

• 24:20Speaker 00

  So from our perspective, it's as much that our customers trust us because we've been working with them for, could be thirty years in some cases. So that they know that when we say we're doing something or we stand behind something, that we mean it, we stand behind it. So it's gone through its paces. At the same time, too, it's also that, yes, we are innovative, that we're creating new things and that we're creating these things under the, the point of view of how a customer would use it practically. Does it actually solve a real problem? How would we address that problem? We do it through this way because we see the same thing. And then obviously, having that, that relevance to the customer from their point of view.

• 25:03Craig S. Smith?

  Yeah, everyone's talking about agents and agentic workflows and agent building platforms. What's Hitachi IQ doing in that regard?

• 25:18Speaker 00

  Yeah, so we're, obviously, it's very new and very fresh. We are currently exploring what is the best agentic framework for us to really integrate in and bring our, our capabilities out. Obviously, we are doing a lot with Nvidia. So we are using Nvidia's IQ blueprints in some cases for, for a lot of the agentic capability. And so that brings in reasoning models and Things like that, like I said, the Lama Nemo Tron is an example where where reasoning is super important for agents so that they have agency and can kind of work through this. But as far as how agents, agentic, the agents work together

• 26:02Speaker 00

  and how they communicate and all the things that have to happen from a workflow perspective through the entire agentic pipeline. We are still working to see what makes most sense. We'll have some decisions made here soon. Obviously, it's we're spending a lot of time on this. So to say that we haven't figured out yet would be an overstatement, but to say that anyone has figured it out yet would also be an overstatement. So it is. There's a lot of experimentation going on right now to to figure out what makes sense. But at the same time, too, it's MCP A to A for the agentic stuff. It's there's a lot of new things that have come out that it's like, okay, what makes sense, what doesn't, how, where do we invest? And also

• 26:44Speaker 00

  of this, it's not just IQ from from the what we're doing on the AI front directly. But of these new capabilities, what also should be integrated into our other storage products? What should be integrated in to, again, like being a part of that AI data platform program? Where do we draw threads in or have threads that go into other parts of our portfolio? So that we can pull these things closer together and improve on the overall experience?

• 27:12Craig S. Smith?

  Yeah.

• 27:14Craig S. Smith?

  It are you also or one of your clients, other Hitachi business units? I would guess that you're

• 27:21Speaker 00

  Absolutely.

• 27:23Craig S. Smith?

  And yeah, do you end up building solutions that then you productize and make available to others?

• 27:31Speaker 00

  Yeah, we call it customer one. So we are as much customer one for what we build internally and take to market. And we do have other customers who are like, hey, we want to do this thing. And okay, great. We'll build this together. So very much we we use, we eat our own dog food, drink our own champagne, whatever it is that you want to use. But we we definitely work.

• 27:54Speaker 00

  Together internally to solve our own problems and then take those to market as much as we come together with a customer. We have current active engagements doing this, solving some absolutely complicated global problems where it's us. It's our business unit partner. It's Nvidia and the customer working together to solve some really fundamental issues to where it benefits us as much as the customer and every other customer after that, because we are going after these really big. I don't want to call it a moonshot because we are seeing improvements, but these are very complicated things that we're solving. Um, and like I said, it's as much for us as it is for our customers in some case.

• 28:38Craig S. Smith?

  Yeah. Uh, and how, how do uh outside customers work with you? I mean, do they

• 28:47Craig S. Smith?

  come to you?

• 28:50Craig S. Smith?

  You know, we, we want to integrate AI into our operations. We don't know how, or do they come to you saying, uh, you know, we run a, a fleet of uh

• 29:04Craig S. Smith?

  ships around the world and we want to optimize our scheduling with AI. I mean, do they come to you with a specific problem? Or do they come to you sort of greenfield saying, uh, you know, come help us uh become an AI company?

• 29:23Speaker 00

  I, I'd say it's that and it's also where we kind of we spark curiosity through conversations that we have where it's not even about AI. Obviously we have a very deep rolodex of customers. Um, so it's, I have a dedicated set of solution overlays sellers that are, all they talk about is AI capabilities. And they go ideate with customers and inspire them like, hey, this is the potential of it. So it's mu- it's as much as them coming to us saying, hey, we want to. Be AI-enabled, or hey, we have this problem we're trying to do. Um, we've done this in manufacturing. It's like, hey, we're trying to solve for this problem.

• 30:07Speaker 00

  Hey, we actually have an idea for this and we work through it and like we solve the problem and they're very happy and then we go on to the next and the next with them. So it's, it's as much us inspiring them as the customer coming to us saying, hey, we have a problem or hey, we're thinking about this. And then having that conversation and, and fleshing that out to where, to the point where it's like, yep, we can start to solve that. And this is the resources and what it will take to actually get there.

• 30:30Craig S. Smith?

  Yeah, uh, you know, AI is becoming so important in all industries. When, when did Hitachi IQ start as, as a brand? And how important it would, I would guess that it's going to become a, a really core offering within Hitachi.

• 30:55Speaker 00

  Yep, absolutely. So we launched IQ officially at GTC twenty-four, so a year and some change ago. Uh, GTC is obviously Nvidia's big event that they have in San Jose. So we just celebrated our one-year anniversary with it back in March, um, at the same time of my birthday. So it was a great birthday present.

• 31:15Speaker 00

  Excuse me, but, um, but before that, obviously lots of work went into, okay, what does this mean? How are we going to take this to market? And then a lot of work has happened to put us on the map and to define us as we are more than just a storage company. We are a data company. Obviously we've been, that's been our, our, our branding capability for a long time. But now evolving that and we are an AI company. So that is much of the time that I spend is advocating with our partners, with our customers, with analysts and press and, and, and Nvidia that directly and others saying, yep, we're an AI company. Where are we going to solve problems and, and what are we going to do here? Um, and then talking with customers.

• 31:58Speaker 00

  But then it's also what we've done previously to reinforce that and showing those examples and showing those use cases. And that's where we've been very specific. Our strategy obviously is to sell product, is to solve these problems with, with, for our customers. But it's not to just sell them a bunch of GPUs and walk away. It is to make sure that they're getting the desired result to actually see that outcome. And then, okay, what's it going to take to hit to that outcome? And then work backwards from there and pull through what's necessary. So while we are an infrastructure company, there is no denying that we are more than that.

• 32:38Speaker 00

  And it's, it's, it's having that vision and painting that picture to our customer, being that very outcome oriented. And then again, bringing in the necessary to support that.

• 32:49Craig S. Smith?

  Yeah. Can you give us some examples of, of some of the larger projects that Hitachi IQ has tackled?

• 33:01Speaker 00

  Yeah, um, big global manufacturing projects, um, companies that manufacture a tremendous amount of, um, of displays where, hey, they want to improve on, uh, identifying defects and damage along the way. And IQ is the, the big repository for that. It's where all that training takes place so that they have that visual inspection capability. Um, IQ has trained L L M's customers, um, who have devices with L L M's embedded in them. Some of those L L M's have been trained on IQ. Um, there are, um, numerous use cases of just private AI systems to help better process data,

• 33:44Speaker 00

  to better in, in how different companies engage with their customer base. IQ is the AI engine that helps coach them through, um, filling out paperwork or answering the question better, things like that. Um, and then improving on things like just enterprise search. It's, I am more inclined to get the information I need if I can just ask a chat interface. And how, and it's plumbed into the data and it summarizes and gives me exactly what I need instead of scrolling through pages and pages of irrelevant search results. So we've built tooling like this and capabilities like this that help our customers solve these problems. And some of them are very, very big and some of them are nascent, small starter areas

• 34:29Speaker 00

  that will evolve and become bigger and bigger because again, they see that, hey, this is powerful. Hey, you know that first batch that we did? Hey, it's time to do the next thing and we have some ideas for that. And that inspiration sparks and now it's, it's turns into this wildfire of ideas.

• 34:45Craig S. Smith?

  Yeah, how how are you? I mean, having started this a year ago, I mean, it's well, and yeah, how do you? How do you? Where do you start? And and how do you? I mean, obviously, you start with your advantages. But but how how do you? Is there a roadmap for for keeping up?

• 35:10Speaker 00

  Oh, absolutely. Yes. It's it does help that I have amazing leadership inside of Hitachi Vantara that believes in this wholeheartedly, even above Vantara into the global Hitachi Limited ecosystem. They believe in this wholeheartedly. So having that support has been amazing. I couldn't say enough about Sheila, our CEO, and Octavian, our Chief Product Officer, who believe in this and have dedicated resources to get this going. My PM organization, our general manager, all of this, like, people are like, they are committed to this thing. It is the number two priority from a company perspective. Obviously, there's our core product, what we do, it's like, it's our

• 35:53Speaker 00

  bread and butter. But AI is number two. And it's it's been amazing, an amazing ride, and we're moving very quickly. Now, what we do have, though, is hindsight as a benefit. So we've learned from the mistakes that others have made, and we have been able to skip them. And it's saved a bunch of money and heartache and time and all that stuff. But that doesn't mean that we don't make our own mistakes along the way, just like everyone else is. But it's being able to, I call it knobs and dials, rapidly address them, change what's necessary, and then move forward. Fail fast is another method or term that's used. But the point is, though, is that it's a commitment from a large organization, it's a commitment from leadership, and it's a commitment from the people

• 36:37Speaker 00

  who are directly involved in it to catch up and to start to look at going, okay, we didn't have to make these mistakes. What can we do to leapfrog? And that's where I'm really excited about what our roadmap will will show. And the the market will see a lot of very cool things that are coming out that show this leapfrog mentality that we're really going after and how we're addressing this space. And I wake up in the morning excited about what's the next thing that we get to do, what's that next thing that we get a release into, and and how can we innovate more into this? And it's been it's been a great ride. It's a ton of fun.

• 37:13Craig S. Smith?

  Yeah, yeah, I can imagine. And and so you're you are building some models, but but you integrate with

• 37:26Craig S. Smith?

  range of open source models. Do you

• 37:28Speaker 00

  Absolutely.

• 37:29Craig S. Smith?

  leverage any of the proprietary models?

• 37:33Speaker 00

  We give our customers the ability to offload and send into things like OpenAI and and Grok and others through it's it's an API call. So we don't preclude them from doing that at all. We can do it on prem. They can do it in the cloud. Though the biggest thing that we realize, and this goes across our the entirety of our strategy from a Hitachi Vantara standpoint, is that we live in a hybrid ecosystem, a hybrid world. Our customers are in the cloud and on prem in various levels, customer by customer. We that's just the way it's going to be forever and now. And that's totally fine. So our

• 38:11Speaker 00

  strategy isn't to fight that. Our strategy is to show benefits from either side and how it would align to a customer's requirement. And then support them in their journey. And if it means, hey, some of the stuff goes to the cloud, some of it stays on prem, whatever that is, that's totally fine. The other side of it too, though, is how can we use concepts from the cloud like as a service or OpEx type payment capabilities and bursting and all that? How can we use that model to improve on how we can create a better offering to our customers? And and so it's it's as it's as important as understanding where their data lives, if it's on the cloud and on prem, how they like to consume things as it is to just say, listen,

• 38:55Speaker 00

  we're flexible, we'll give you the ability to consume how you want to to create the outcome that you need and and go from there.

• 39:02Craig S. Smith?

  Yeah, the is is does Hitachi IQ, is it built around a sort of a flagship product? Or or a solution? Or is it much more horizontal and?

• 39:22Craig S. Smith?

  A map.

• 39:23Speaker 00

  It is horizontal. I would say the thing that we did, we made a conscientious decision to build her on the Nvidia ecosystem. I mean, they're the gorilla in the market. There's no denying how successful Jensen and that company have been in this and and they are an amazing partner for us. We we love working with them. So where we were strategic is in aligning to their ecosystem. So things like, obviously,

• 39:48Speaker 00

  H one hundred, H two hundred, Blackwell, the even Bluefield and Spectrum X and all these things that they're doing from a portfolio perspective, absolutely very important. Nvidia AI Enterprise from a software suite and that capability that they've done from blueprints and things like that, very important. Really, though, what I would say what we revolve around is it more so than anything else is obviously who we are as a company and our ability to understand data and to bring what's necessary into those frameworks and be able to support that outcome in there. So so I would say that our, if if anything, our flagship is the data reputation that we have.

• 40:31Craig S. Smith?

  Well, how do you advise clients on on the need for experimentation with the pressure of showing ROI? That's a classic problem that the businesses face.

• 40:44Speaker 00

  Yeah, I mean, I think what this has really highlighted is that the definition of ROI is changing. Now, what it's changing into, I think we're still trying to figure that out, honestly. But the traditional ROI, like if you look at it from, if we just take AI at its face, generative AI and agentic AI at its face, what it's supposed to do is improve on efficiency. Improving on efficiency and how are people do things, how they do their jobs, usually means that we don't need as many people or we don't have to hire as many. And maybe it's cutting jobs, things like that. And that's not what I want to happen. And and I think that that gets overplayed a bit. But That's why ROI saying what's the ROI on AI. It's well, it's your people spending

• 41:29Speaker 00

  time on things that are more important. So you could say, well, it is that you don't have to hire as often. The other side of it, though, could be is like, well, you're learning more about your systems and you're starting to build a competitive advantage. So your investment in AI means that you're one step further ahead than your competition. That's a ROI. But that's a lot harder to to kind of quantify. So from my perspective, it's, this is why I say it's also being a bit redefined, is we have a high failure rate traditionally of AI projects. If he, I think McKinsey's last number was something

• 42:12Speaker 00

  near ninety percent of the AI product, projects that were that were started fail. And again, see the previous conversation of why we see a lot of that. But in that, though, is how do we wrap failure in as a part of the ROI? So, and that could be learning. I I personally learn more from when I fail at something than when I succeed at something. So that has a tangible value. So how can we wrap that into an ROI model? So this is why I say it's being redefined, what ROI actually means, but it's complicated. And saying that we know what it is now, we're not there yet.

• 42:52Speaker 00

  But again, this is where we have to take into consideration that there's a lot more to it than just how many jobs did I cut or how much money did we save or anything like that? Because it's it's a lot more complicated than that.

• 43:05Craig S. Smith?

  Yeah. And you guys are, Vantara is at its core, an infrastructure company, right? How how do you, how can businesses evaluate whether or not their infrastructure is ready to support AI at scale?

• 43:26Speaker 00

  Yeah, I mean, usually it's they're not. Nine times out of 10, they're not. I mean, AI brings a whole different paradigm to what infrastructure needs to be able to support. From data perspective, we're talking tens to hundreds of millions of IOPS throughput, networks that aren't designed for the level of data that flows through it. Even source systems that that have from great examples of manufacturing company that we were working with. Their source data wasn't ready for AI to consume the data of where it lived. So we had to go in and transform that source of data. Where the where we had to pull things from in a more real time fashion.

• 44:10Speaker 00

  To be able to support the additional workload that AI was that also supports their enterprise requirements. So in a lot of cases, customers run right close to the edge. And by putting AI workloads on top of these systems could cause them to fall over. And that's the last thing that we want to do. So it's the it's forcing this transformation and this modernization of the network, of the compute resources, of the source applications, of the data itself. So that now that they're ready. Now, the good thing is, is it doesn't require like a mass overhaul of everything. But it is very it's in it's very silent, it's very targeted that needs to see this improvement. And the way that we built IQ is that

• 44:55Speaker 00

  it is self contained as much as it needs to be. So it has storage that's designed to meet the scale and demand that the GPUs will create. The hardest hitting workloads. It's got the network so that the GPUs can talk to each other for memory sharing and all the other stuff. As well as what the GPU needs to be able to talk to the storage platform at as fast as possible so that they're getting fed and and and and being fully utilized. So we've built that to be turnkey from an infrastructure standpoint. The what you connect into and start to pull data from and things like that. That's where that transformation needs to start to take place in. And that's where, again, understanding your use case helps us then understand where we need to reinforce capabilities.

• 45:41Craig S. Smith?

  Yeah, naturally, you would be talking to your existing customers, Vantara customers.

• 45:50Speaker 00

  Yep.

• 45:51Craig S. Smith?

  But but you also talk to companies that are using other.

• 45:58Speaker 00

  Oh, absolutely.

• 45:59Craig S. Smith?

  Whether it's Oracle or IBM or somebody like that.

• 46:04Speaker 00

  Yeah, yeah, I mean, everyone's on the table as far as we don't shy away from anyone. And again, it's because it's where we really target the like, how can we improve on things? We're even at the point too, where a customer may have made an investment already and they're trying to figure out how to use the GPUs they already bought. We'll go help them do that also because we realize once you start to see success, then the use cases continue to scale and continue to grow. And who are they most likely to call back when they need to start doing expansions on the infrastructure side or start looking at the other use cases? So we very much, again, we're agnostic. Everyone is a potential opportunity for us. We want to help everyone realize the value behind this and and really spend time in in making sure that they

• 46:48Speaker 00

  get the best bang for buck on their side so that they can see that value.

• 46:52Craig S. Smith?

  Yeah, where do you see those going both for Hitachi IQ? I mean, presumably it's it's going to grow.

• 47:03Craig S. Smith?

  But where do you see the the whole AI in the enterprise going? As you were saying, there's a very high failure rate on the pilot programs.

• 47:15Craig S. Smith?

  People are gradually putting things into production. But looking ahead 10 years, what's your hope for Hitachi IQ? And how do you think how?

• 47:31Craig S. Smith?

  How immersed will the economy and enterprises be in in AI? Or how broadly do you think AI will be disseminated?

• 47:44Speaker 00

  Yeah, so um, a couple of things. That's a great question. First and foremost, what I think is from a this failure rate thing. The there's two sides of that coin. The side one is yes, a high failure rate. 10% at best succeed. The other side of that, though, is that there's this term, the juice is worth the squeeze. I don't even remember what movie that came from.

• 48:07Craig S. Smith?

  Yeah.

• 48:08Speaker 00

  The the 10% that's left has shown such a dramatic improvement in ROI, bottom line improvement, competitive advantage, whatever it is. That that 90% failure rate is well worth that 10% success rate because it more than pays for itself. So that I think we'll continue to see now, obviously, again, it's just like going to the gym. The more you exercise, the more you become you, the more better you get at these things, the more success you see. So from our standpoint, as we see more engagement, as customers start to to work on this more, they'll start to see fewer failures and more successes.

• 48:48Speaker 00

  The other side of it is I see that this is will start to be ingrained, not just in like, hey, I have a chatbot to solve a problem for me. But Agentic AI will start to help things like AI Ops, better data management tools, things like that. This is a lot of thinking that we have also on how we can improve our products with Agentic AI. IQ talking into our other storage product products and helping IT operations be more streamlined. So things like and I posted a blog about this not too long ago, it kind of gave you a glimpse into our thinking. It's having an Agentic AI kind of broken down into five categories. A data for classification and management agent workflow. A proactive storage optimization workflow.

• 49:31Speaker 00

  How can we get the the storage to run as efficiently as possible and have less hands on to do it? Um, automated data governance and compliance. That's super important, especially in today's world where we have um, boundaries and we're seeing more clear regulation on how data can be handled and things like that. It's going to take systems to be able to do that more efficiently. Things like predictive maintenance where systems can stay online more um and and be able to phone home better and do things around that and route around problems. And then most importantly, though, especially in today's day and age, is better data security and protection. How can we ha- ensure that, hey, I know, and how do these all work together?

• 50:13Speaker 00

  So I have a data governance agentic workflow that understands data as it's coming in and understands the the kind of the the point of view of what that data is and then ensures the right compliances there. And then my security and my data security agents can then ensure, hey, by the way, this data was missed and we need to go in and we'll update it from the governance side and then protect it better and do all that stuff. So we see a lot of these themes that will start to be injected into how AI Ops operates. And that will again give a lot of value and and simplify a lot from an from an operation standpoint, but also help in a lot of these compliance requirements and things like that.

• 50:53Speaker 00

  So overall, I mean, there's a lot, it's again, it's very young, it's growing up very quickly. Um, and I see that the potential is vast for this. So now it's a matter of capitalizing and capturing that and then creating some very valuable outcomes to our customers. And then uh capitalizing and going forward and and continuing to learn and evolve from there.

• 51:16Craig S. Smith?

  Yeah, um, I've got to ask because I imagine you're you're hiring. If you're in the uh expansion phase, uh, is it hard to to to find people in in the current marketplace?

• 51:32Speaker 00

  It's, it depends, the the answer is yes, but also no. I mean, everyone's on fire about AI, um, everyone is learning about it. Um, there are, there are a lot of nuances to it. Um, selling it is one thing, having being a technical expert around is another thing, developing for it. It it gets very expensive. These resources are in very high demand. The good news is, is from our perspective, we invested in these resources for a long time. We have, if we look at inside the the group that we live inside of within Hitachi, we have over ninety five hundred machine learning engineers

• 52:09Speaker 00

  who do this on a daily basis. So from an engineering perspective, we've got a lot of resources. Now, a lot of those are customer facing for facing into projects. But yeah, we are hiring and we are working on, okay, getting the right resources in. How can we best? Obviously, I still have a budget I have to adhere to. So who I can bring in and where we can be effective with that. And then obviously, how we can have just a killer team out in the market that is going in identifying and helping our customers. And and obviously, that takes a certain personality. So it's it's also bringing in those those resources too. Now, I think from my perspective, Craig, you like nailed it. This was a great conversation. I think a lot of cool topics and

• 52:49Speaker 00

  it's, as you can see, I'm very passionate about this and I love this stuff. So it's it's been fun to to have this conversation.

• 52:55Craig S. Smith?

  Talk a little bit about the sustainability issue, because that is coming to the fore.

• 53:02Speaker 00

  Yeah, absolutely. This stuff requires a lot of power, not just power directly on the GPU, but obviously, a lot of data is required, a lot of processing. It's it's not just the data it's sourcing, but it's generating a tremendous amount of data. And how and doing that efficiently is really difficult. Now, we at our core, from Hitachi Limited down to Hitachi Vantara and everyone else in between and to our left and right, sustainability is our number one priority. Obviously, the only planet that we get will ever get. So how can we do this right? Everything that we do from a product perspective has sustainability in mind. Now, we don't build GPUs.

• 53:44Speaker 00

  It's just we don't. So obviously, we can't impact on that.

• 53:47Craig S. Smith?

  Yep.

• 53:48Speaker 00

  But what we do build is what I call the peripheral of the GPU, the storage platforms, how we engineer, the code we write, all of these aspects of it, of how these resources are consumed. So what we have is, in our mind, a point of, an objective of how we can improve on the sustainability side of this technology through what we can directly impact. So it's in the outcomes that we create. It's in our storage technology, which, by the way, is Energy Star top three rated as the most sustainable storage platforms in the market. So there's a lot that we've done in our portfolio to be able to offset the, the impact of some of this technology.

• 54:30Speaker 00

  The, the, the, the point is, though, is that, listen, this stuff will continue to burn hot. This stuff will continue to grow in scale and, and consume more and more. Nvidia will do what they can do to improve on it. They, they also have this in mind, as well as AMD and Intel and all the other players in this space. But it's what we can do directly on, based on what we control, that is important. And that is what we can do in our products, how we can ensure that throughout us as an organization, whether it's, again, the code rewrite, the products we build, even our supply chain and how we source and the things that we do, that we do this in a sustainable and conscientious way, so that we can say, listen, we're doing the best that we can to improve on that.

• 55:15Speaker 00

  Now, what it turns into in some cases is, for lack of a better term, it's like carbon credits. We're optimizing where we can to offset where others can't. And we're proud of that because we are contributing in a very positive way to, uh, to improve on the sustainability side of, of what AI actually is.