NetApp INSIGHT 2026 | Ashish Dhawan, NetApp & Brian Sathianathan & Jon Nordmark, Integrate.ai

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More from NetApp INSIGHT 2026

Ashish Dhawan

SVP and CRO - Cloud and AI Business Unit

NetApp

Brian Sathianathan

Chief Technology Officer and Co-Founder.

Iterate.ai

Jon Nordmark

CEO and Co-Founder

Iterate.ai

play_circle_outline Partnership between NetApp and Iterate.ai for private AI

play_circle_outline Governance, security, and compliance (HIPAA, PII) for private AI

play_circle_outline AIPod Mini: Turnkey Private AI Appliance with ONTAP Storage, Embedded LLMs and SuperAgent Workflows

play_circle_outline NetApp Insight 2026 Las Vegas: Partners Enable Project Novus Zettabyte-Scale Storage Deployments

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Ashish Dhawan, NetApp & Brian Sathianathan & Jon Nordmark, Integrate.ai

Ashish Dhawan

SVP and CRO - Cloud and AI Business Unit NetApp

Brian Sathianathan

Chief Technology Officer and Co-Founder. Iterate.ai

Jon Nordmark

CEO and Co-Founder Iterate.ai

In this interview from NetApp INSIGHT 2026, Ashish Dhawan, senior vice president, general manager and chief revenue officer of the Cloud Business Unit at NetApp, joins Brian Sathianathan, co-founder and chief technology officer at Iterate.ai, and Jon Nordmark, co-founder and chief executive officer at Iterate.ai, to talk with theCUBE Research's Christophe Bertrand about how the companies are making private AI simple and secure for enterprises. Dhawan explains how NetApp's data strategy has moved customers from siloed, single-use AI toward an intelligence laye... Read more

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How does NetApp enable enterprises to run private AI, and what role does the AIPod Mini play in that approach? add

How does NetApp's intelligent data infrastructure — including its "intelligence layer" and the AIPod Mini (with Iterate.ai) — help customers manage data complexity, governance, and deploy private AI? add

What is the AIPod Mini and what features and benefits does it offer for enterprise AI deployments? add

What were your impressions and main takeaways from Insight 2026? add

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Ashish Dhawan, NetApp & Brian Sathianathan & Jon Nordmark, Integrate.ai

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(INTRO)

Christophe Bertrand

>> We're back at NetApp Insight 2026 in Las Vegas. I'm Christophe Bertrand, analyst in residence. We're here with three great speakers, a great partnership. Let me start with introductions. let's start with you, Ashish. Tell us about yourself.

Ashish Dhawan

>> hi, I'm Ashish Dhawan. I lead the cloud and AI sales for NetApp. Been with the company for three years. This has been a very exciting Insight for me. Good job.

Brian Sathianathan

>> Brian, what about you? I'm Brian Sathianathan, I'm president and co -founder, chief AI officer of Iterate.ai. We are a private AI company. That means we provide a full-stack AI, all the way from inference to agent building and governance and security, because as organizations are building all these things, they want a secure platform, they want a platform that runs on premise, that runs in the cloud, and we have a platform that's turnkey, that can do it quickly and faster and cheaper for them.

Christophe Bertrand

>> Perfect. And we'll talk about the partnership in a second, but Jon first, let's go with you.

Jon Nordmark

>> And I'm a co -founder with Brian, CEO of the company, and just love this partnership we're building with NetApp right now.

Christophe Bertrand

>> Well, let's double click on the partnership. how did you become partners? Obviously, lots of announcements at Insight 2026. There were announcements on Friday about an acquisition. Of course, the announcement of Novus with the new architecture. That has to be something that got your attention. So tell us about the partnership and what you think about those?

Jon Nordmark

>> Yeah, well what we're excited about is that NetApp is all about making private AI easy and accessible to any enterprise. And we, like Brian was just saying, we focus on private AI, which means you own your own hardware, you own your own language model, you obviously own your own data. and so instead of taking your data out to a third party that you don't really know, NetApp's all about keeping it private inside the enterprise, and keeping it real secure. So that's what this partnership is all about. It's starting with the AIPod Mini, it's a turnkey solution. The easier, the better, as far as we're concerned. It doesn't require a technologist to be in the middle of this. Absolutely. Yep, the business person can just light it up.

Christophe Bertrand

>> So, Ashish, actually I have a quick question for you? We're past the project phase now in AI. We're seeing a full operationalization of AI. I guess working with partners is going to be critical. Tell me what you're seeing from your standpoint.

Ashish Dhawan

>> Yeah, when this whole wave started about generative AI, you found that AI is not new. Our customers have been using AI for the longest time. but what happened was that a bank used to use AI for one particular fraud detection use case. A telco used to use AI for one particular churn analysis use case. But what's new now is what we're doing is we're creating an intelligence layer across the enterprise. Now for that to happen, you needed to have a good data strategy across the enterprise. So when this whole generative AI wave started, and when people started to look at their data estate, the data was in a sprawl. So the last couple of years, I think that's the major thing that we've been working with our customers, to make sure that their data is ready for AI. And that's why you now find customers are going from the pilot phase to production, getting actual benefit productivity use cases across many industries. You can see it in the increase in productivity. for many, many decades, the annual increase in productivity in the US was .9 % per annum. Now, just the last year, it has risen to 2 .6%, almost a three -fold increase in productivity. So you can clearly see that from the pilot phase to the production phase, that has moved across industries.Right, so.

Jon Nordmark

>> And just really quick, what's exciting to me about working with NetApp is every company that uses NetApp today has a ton of data sitting in an area that in the past was inaccessible, right?

Ashish Dhawan

>> Right.

Jon Nordmark

>> And all of a sudden with this AIPod Mini and providing access to that data through ONTAP, but layering the generative AI agentic layer on top of that, all of a sudden the world wide web of a company, the storage of a company, becomes married to the live databases too on the front end. And that's what we're doing with that. That's what we call the intelligence layer, right?

Ashish Dhawan

>> That's where the intelligence layer. That's why a company is all about intelligent data infrastructure. So absolutely.

Brian Sathianathan

>> The other problem is it's a little bit of an easy button, right? Because everything that we are talking about here has a lot of complexities abstracted underneath it. With NetApp, what we are doing is we are together doing all the plumbing underneath. So as a customer, you don't have to worry about all the complexities of governance, the complexities of pulling data out of this, right? Think about NetApp as a very deep well with amazing gold treasure of data.

Ashish Dhawan

>> And the other thing that has happened is that there's also been more maturity about the usage of models in private AI. You find that whether it's closed models or open weight models, they're getting close to the frontier. So therefore private AI usage is also increasing because people can get the same benefits that they actually get from closed models on a particular cloud, are available with open-weight models as well. That's where our AIPod Mini with Iterate.ai can really work well.

Christophe Bertrand

>> So let's talk about that because we've covered a number of topics and I think we should break them down a little further. First of all, you mentioned AIPod Mini, which I think is, AIPod Mini, not AirPods, sorry. AIPod Mini, I'd like to understand a little bit more about what that is. Okay, Brian, easy button. I don't always trust that statement from any company. But indeed you are figuring out how to put together, for lack of a better term, an easy button for private AI for customers who have NetApp environment and other data sources, by the way, because that's the other benefit of it. Tell us more about how you built it and maybe give us a customer example. Or two if you can.

Brian Sathianathan

>> Absolutely, so think about AIPod Mini is a perfect combination of hardware and software together. So AIPod Mini has attached connections to the NetApp storage and it talks to NetApp through using the ONTAP protocol that's time tested. And then on top of it, on top of the AIPod Mini, we are running our Generate software. And that software has an LLM embedded in it. So you can run all your AI queries locally and fully privately within your four walls. And then it also has vector embedding capability that embeds and vectorizes all your data. What it means in simple terms is that it prepares your data, getting it ready for the AI. Then you can do this at terabytes and terabytes of scale. That's what the AIPod Mini is. And then on top of that you have workflows, security, governance, all the tools that's required to make it ready to run for the enterprise. That's why we call it sort of the easy button. because at the end of the day as a customer, you don't have to start building the stack yourself. And we've done all that hard work for you.

Jon Nordmark

>> And out of the box too, it's got, there are over 200 templates for agents. There are over 200 skills built into it. There are over 800 tools, I believe, that are accessible through it. So really it's super versatile. None of that needs to be built by the enterprise itself. It's just ready to go.

Ashish Dhawan

>> It's ready to go.

Jon Nordmark

>> And you can build an agent. We have a concept called SuperAgent, and so with this layer on top of the AI pod, meaning you can talk to generative AI software and tell it what you want an agent to do and in a couple of sentences, it will build a pretty complex agent on the fly.

Christophe Bertrand

>> So let's pause on that. This is very interesting because to me, this is where you get measurable results and you can start looking at, okay, now I'm getting productivity out of my AI investment. now this is not just fun and games, or a pilot phase, now we're into implementation. So, start with you Ashish, I have this question for the three of you. Where do you see the most measurable results right now in AI implementations?You know.

Ashish Dhawan

>> The various use cases, obvious the most easy use cases are where you can really replicate or automate human effort, right? And that's why in most of the tech industry you find the use cases around making sure that there are autonomous coding, low code, no code environments created. That is a huge use case for us. But then you go across the industry, you will find use cases across financial services where there is an increased usage in not just things like fraud detection, but also in enhancing customer services and personalization. you go to the healthcare industry, customer experience is such a huge impact there. But also, when you go to life sciences, you find genomics and others, you can clearly see the impact with more and more cancer drugs coming, new peptides coming, all because of the impact of AI. And then you go to government services and public sector where AI is so critical for security, et cetera, and that's where we're seeing a huge impact in making sure that citizens' lives are more secure, they get more services out of the public sector, and the agencies that work across any country can work together really well. So there are many impacts across various industries that we're seeing.

Jon Nordmark

>> And Ashish is bringing up, you got to be HIPAA compliant, PII compliant and all.

Ashish Dhawan

>> Absolutely.

Jon Nordmark

>> That's where we specialize, and that's why this is a great marriage.

Ashish Dhawan

>> Perfect partnership.

Jon Nordmark

>> Perfect partnership. And we have what we believe is an outcome based AI. And what we mean by that is it's got to produce the proper KPIs and business outcomes, now the technical things are kind of table stakes, but everything else is what really matters to the enterprise. So just a couple examples. This morning I got a call from Brazil. ZILT. It's being used by a law firm where they're actually just testing it out, but they wanted to see if it would read a 750 -page case that included other documents and pictures, and whether or not it could analyze it. And guess what? It took 20 minutes to crunch that data, but it wrote a 25 -page report for them on what it would recommend the lawyers do with this case. And they were thrilled. There's an insurance case also where it had a long document with a lot of pictures and workflows like it was an accident report. What would normally take an analyst about eight hours to evaluate got done in eight minutes. And so when you think about that, a lawyer who's making $300 to $2 ,000 an hour to be able to compress that 700, what would take to do a 750 page report to 20 minutes, and then allow them to use their time strategically, it's a big deal.

Brian Sathianathan

>> And also to Ashish's point, it becomes very powerful when you begin to apply it to the different verticals. So one of the verticals we've applied this particular solution to is the healthcare vertical. So when hospitals bill insurance companies, they always get paid 80, 85%, and the remaining they get denied. So we actually have a revenue cycle management, a forensic analysis agent that runs underneath. So recently we ran it for a hospital that is 150 million billing annually. We were able to identify 17.4 million and then they recovered a couple of million out of it within 60 days.

Jon Nordmark

>> 17.4 million of denied claims.

Brian Sathianathan

>> Denied claims.

Jon Nordmark

>> That the humans couldn't figure out how to recapture. That's fantastic.

Christophe Bertrand

>> Right, so that's measurable. Those are very clear outcomes. You started touching upon agentic AI, which is obviously a big theme in Insight 2026. So, what meaningful deployments, to double click further on this sort of how do you use it, what meaningful deployments have you seen with agentic AI? Let's start with you, Ashish.

Ashish Dhawan

>> Yeah, agentic deployments are again happening across the industry. Our customers are using a myriad of agents. For example, a large retail customer of ours has about 100 API endpoints across three or four different models in which they're trying to make sure that they have agents that help customers upload the products they want to sell. So just by uploading a picture, right, a perfect listing gets created, right? And that's done by an agent. There is a car retailer who just by uploading a picture can make sure that the insurance claim is absolutely perfect. This is for a damaged car. There's another retailer that is helping customers make sure that they get the right price for the product they want to sell. So in various applications, agents are being used not just to make sure that customers get the right experience while using sort of an AI platform, but also they get the right value from it. Right? And this happens because there's tons of data behind it and people can actually use really well.

Brian Sathianathan

>> And also one of the things to Ashish's point what we are also seeing is a lot of the agents you apply typically at the very beginning were productivity agents. So people were using it on documents. How do I get productivity? How do I create, right? Yeah, the Copilot.

Christophe Bertrand

>> The Copilot stuff.

Brian Sathianathan

>> But now what happens is it's moving into back -end autonomous agents, right? So there are agents and agent teams beginning to do real enterprise work, right? For example, we've seen in retail, there is a lot of back -end enterprise work in inventory, in various types of retail practices. We've seen in the healthcare, like I told you, forensic kind of capability. We are seeing in insurance similar type of capability. But the beautiful thing is these are autonomous agents. So if these autonomous agents are beginning to run, what it also means is you want to have governance. You want to have permission. That's where the NetApp platform is really powerful.

Jon Nordmark

>> With ONTAP.

Brian Sathianathan

>> With ONTAP, yeah.

Jon Nordmark

>> And by the way, on the agent work, so the healthcare example Brian just gave you, it's fascinating because it's got three different agents involved. The first agent reads the contracts between the hospital and the insurer. And an average hospital has something like 70 contracts. So imagine being a human trying to keep track of all . And they're complex, and they're changing all the time. And then so the second agent watches for denials that are coming in. And when they come in, it tries to examine what happened in that denial. Was it a mistake? Did something not get uploaded into the first submission? And it identifies the problem. Then it goes back and matches against the contract that that would apply to and determines whether or not they could resubmit the claim. and then if it can get resubmitted, the third agent writes the resubmission on behalf of the CFO or whoever's managing that process.

Brian Sathianathan

>> Because that used to be an activity that takes hours and hours of the physician's time. And that's very expensive time.

Jon Nordmark

>> Just writing the letter can take 30 minutes for a physician, so you can imagine. And if you have, average hospital has 110 ,000 denials a year and just imagine the workload in that. But these agents just do it.

Christophe Bertrand

>> So, these are great examples. Luke, as we wrap up, I'd love to get your impressions from Insight 2026, real quick around the table. Jon, what did you do?

Jon Nordmark

>> Oh, it's been amazing for us. We've met so many of NetApp's sellers and all the people that helped make this company the great company that it is, that take it out to the market, and we've been ushered from one to another with our NetApp friends, and I don't know, I don't think we could ask for better meetings.

Brian Sathianathan

>> I 100 % agree with Jon. The first thing I think is that what we really love about NetApp is the partner ecosystem, and their seller ecosystem around the partner is fantastic. And they have lots of trusted customers who vouch for NetApp, and we were able to see that today. That's amazing. The other thing, more from a technology perspective, I love the Novus launch. It's amazing! It's phenomenal, phenomenal, phenomenal, remarkable achievement!

Ashish Dhawan

>> Yeah, and for us at NetApp, it's very important that partners like Iterate leverage this conference well. So I'm really glad that you found it valuable. Truly appreciate it. Our team has been hard at work, as you can see, to launch some very interesting capabilities. Project Novus obviously has caught a lot of eyes. The first Zettabyte scale storage, especially for high performance computing, Neo Cloud, Sovereign Clouds, it's such a huge opportunity, right, not just for AI, but just really to change the way humanity will exist in the future, right, AI for good, right. Then of course, huge advancements in solving some of the biggest issues that are there in the infrastructure space. How do you solve for that whole expansive metadata scale that we have? How do you make sure that we work with the large hyperscalers, right, and make sure that our services really are best in class across them? And then making sure that all this is available, not just to the customers, but the partners in right in time, right? So everything that we launched is either in preview or generally available, so.

Jon Nordmark

>> And you know what I love too, just the talk about the memory. The context equals knowledge. Memory plus context equals knowledge. We talk a lot about that. Memory is what makes AI work. Absolutely. And you know what? So much of a company's memory is in storage today, and that's what NetApp's here to unleash. Intelligent data infrastructure right there.

Christophe Bertrand

>> That's right. Gentlemen, thank you so much for your time. Thank you for joining us today. To our viewers, stay tuned. We'll be back at NetApp Insight 2026 in Las Vegas. Thank you.