In this interview from theCUBE + NYSE Wired: Practitioner Series, Anand Pradhan, head of the AI Center of Excellence and Mortgage Data Group at ICE, joins theCUBE’s John Furrier to discuss the critical shift from AI strategy to enterprise execution. Pradhan shares how the Intercontinental Exchange is moving generative and agentic AI into production at scale within a highly regulated environment. He unpacks the realities of building with AI agents, emphasizing that they are simply another layer in the software stack that still requires rigorous evaluation and careful token economics. Pradhan also details ICE's unwavering commitment to security, highlighting how their infrastructure and InfoSec teams deploy strict controls, data loss prevention and continuous monitoring to safely harness both proprietary models and external LLMs.
The conversation also explores the pragmatic application of AI in enterprise workflows and its realistic impact on established SaaS platforms. Pradhan dispels the hype that AI will immediately replace complex transactional systems or systems of record, noting instead its power to enhance front-end interactivity, automate deterministic flows and dramatically improve data quality. By championing a culture of education and providing secure AI tools to employees, Pradhan illustrates how organizations can push the boundaries of innovation while strictly adhering to regulatory guardrails and maintaining essential human-in-the-loop oversight.
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Anand Pradhan, ICE
In this interview from theCUBE + NYSE Wired: Practitioner Series, Anand Pradhan, head of the AI Center of Excellence and Mortgage Data Group at ICE, joins theCUBE’s John Furrier to discuss the critical shift from AI strategy to enterprise execution. Pradhan shares how the Intercontinental Exchange is moving generative and agentic AI into production at scale within a highly regulated environment. He unpacks the realities of building with AI agents, emphasizing that they are simply another layer in the software stack that still requires rigorous evaluation and careful token economics. Pradhan also details ICE's unwavering commitment to security, highlighting how their infrastructure and InfoSec teams deploy strict controls, data loss prevention and continuous monitoring to safely harness both proprietary models and external LLMs.
The conversation also explores the pragmatic application of AI in enterprise workflows and its realistic impact on established SaaS platforms. Pradhan dispels the hype that AI will immediately replace complex transactional systems or systems of record, noting instead its power to enhance front-end interactivity, automate deterministic flows and dramatically improve data quality. By championing a culture of education and providing secure AI tools to employees, Pradhan illustrates how organizations can push the boundaries of innovation while strictly adhering to regulatory guardrails and maintaining essential human-in-the-loop oversight.
VP, Head of AI Center of Excellence and Mortgage DataICE
In this interview from theCUBE + NYSE Wired: Practitioner Series, Anand Pradhan, head of the AI Center of Excellence and Mortgage Data Group at ICE, joins theCUBE’s John Furrier to discuss the critical shift from AI strategy to enterprise execution. Pradhan shares how the Intercontinental Exchange is moving generative and agentic AI into production at scale within a highly regulated environment. He unpacks the realities of building with AI agents, emphasizing that they are simply another layer in the software stack that still requires rigorous evaluation and ...Read more
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What is this NYSE Wired (theCUBE) NYSE Studio segment introducing, what is the focus of the new practitioner series, and who is the guest?add
Does agentic AI make adopting AI easier and eliminate the need for a defined strategy, evaluation, and cost optimization (such as managing token usage)?add
How do you see software agents fitting into the evolving cloud-native hybrid and distributed (on‑prem/edge) computing stack, and what are the practical implications for practitioners implementing them?add
How do you balance maintaining ICE’s strict, regulated security and privacy controls for proprietary data (including mortgage data and related investments) with enabling AI innovation—specifically addressing risks from AI factories, tokens, LLMs and their APIs, model hosting, and data flow/leakage?add
What is the prevailing sentiment among AI practitioners and leaders about how AI (including agentic AI and LLMs) will affect established technology stacks — which parts will be disrupted, which will remain intact, and where is AI most likely to add practical value?add
How should one design/architect workflows that combine deterministic flows and AI (including agentic) model-based flows, incorporate human-in-the-loop validation, front-end and data-pipeline components, and account for ongoing maintenance and support?add
>> Welcome everyone to theCUBE's NYSE Studio. Of course, we're at our Palo Alto Studio connecting Silicon Valley and Wall Street. This is the NYSE Wired program, a Cube original. Of course, the NYSE Wired community, Brian Bauman and the team, growing community of experts. This is a new series we're starting. The practitioner series kind of grew out of our AI factory. We're starting to see this year, 2026 is the year of execution, execution risk and reward. You're starting to see practices who have lined up, have done the work to do the due diligence, get the strategy, understand how things work, but put it into practice. And at the end of the day, is it in production? Is it in production at scale? Is the data secure? Got a great guest, theCUBE alumni back, Anand Pradhan here, head of the AI Center of Excellence and the Mortgage Data Group at the Intercontinental Exchange, ICE, the parent company of the NYSE. Great to see you again. Thanks for coming in.
Anand Pradhan
>> Great to having me here, John, and congratulations how you have grown the Wired with Brian. We started at a small CUBE, so get the -
John Furrier
>> You started from the beginning. You were the first person I met here at the NYSE, of course, from the parent company ICE, which owns the NYSE. And that's really news. This is not your grandfather's NYSE, but ICE is a very innovative company. They have their fingers in so many pies. They got a diverse data business, which you're involved in a technical level if you're seeing all the innovation. Obviously AI, you've been on the AI factories before it was called AI factories when we first met. But I think you are the perfect practitioner to kick off this series because you meet with so many people. You do the work to dig in, figure out how things work, but also the idea on commercialization and you have a high bar. So I got to ask you, do you agree with my statement that this is not the year of strategy risk, but more execution risk, or are we still in strategy formulation, execution, toes in the water? What do you see in this generative AI and now shift to agentic infrastructure and AI native apps?
Anand Pradhan
>> I think it's a combination of both. There are some places where the strategy is not there and it's all over the place, but there are places like ours, we had a set strategy, how we will approach AI. Agentic AI definitely is an announcement to the previous layer. It makes some of the usage easier. But you still have to understand what goes in that agentic layer. You still have to make sure that you have all the evaluations that you needed to make the agent right for the job. Beyond the high, like I keep saying, you still have to understand how many tokens are going into the LLM, how many tokens are coming out of it, how do you arrange the tokens, how do you make it economical for the use case. Because it's not only about revenue, you still have to make it economical.
John Furrier
>> And bulletproof.
Anand Pradhan
>> Yeah.
John Furrier
>> Today actually, ironically today inflection went public here at the NYSE. They're doing quantum work, they're shipping sensors into space. They got the sensor business, which does all that data gathering, also the community business. So quantum could be a very big impactful changing the context windows on the edge. You're seeing a lot more discussion around, okay, cloud native has moved into cloud computing on-prem and edge. We all couldn't talk about that for many, many years. But we are now seeing that hybrid distributed computing paradigm really blossoming. Nvidia GTC's coming up, Mobile World Congress, MWC is happening in a couple weeks. We're going to start to see the discussion move from hybrid pure cloud on-prem to distributed nodes in the network. That foundation is cloud native hybrid computing, but it's the foundation, so agents are going to sit on top of it. So it's not like magic wand is going to rip out that out and run on some cloud, agents will have to play in the stack. What's your practitioner view on this?
Anand Pradhan
>> The way I see it, agents are nothing but another layer in the software stack. If you look at MCP server, which made agents mainstream, basically it's a standardized protocol to interact with the agents or build the agents. But the data layer is the same. Behind the scene you have APIs, you have databases, you have file systems and things like that. And then you put MCP server, you build an agent. The good thing is that it's software stack. Then each of these agents are interacting with LLMs and they're getting some output. That is either making a decision or routing the traffic to another agent. And now we have agent teams, sub-agents, and all the orchestration that he needed. So fun times in my opinion, people who know how to implement it will excel at the end of the day. We will see many implementation of the agents as well as some hype, but we will implement agents where it's needed, where it's add value or it reduces cost, time to market, data quality improvement, things like that.
John Furrier
>> I know you guys, in talking to you, people might not know it, but ICE International Continental Exchange, again, big organization over a hundred billion dollars value, you guys have a high security, you're regulated. This is a lot of stock data, a lot of proprietary data. You are very focused on security. How do you see AI factories, all these tokens impacting the data, because you're running the mortgage data business tech side? You're in the middle of the data and you've got polymarket investment with NYSE, which I thought was phenomenal. As you look at the security posture, how do you think about that piece, although you have a mandate, while not foreclosing the innovation opportunity?
Anand Pradhan
>> ICE is supported by a great infrastructure team. If you talk to any of our infrastructure engineers, whether it is data center, platform engineering, network engineering, we live and breathe security. Basically everyone talks about that because we have been groomed like that in the regulated industry and paying attention to security, as well as our InfoSec organizations led by a great CISO. We have architects in the cyber sector, we have red team. We have everything covered to make sure that we evaluate all the traffic that is going in and out, as well as where do we store the data? Do we have enough controls to make sure that data leakage doesn't happen? We have privacy controls, data loss prevention, so on and so forth. And we pay real attention to details on each of the thing when we deploy anything. So yeah, we have all the controls, all the detection mechanism for all the traffic flows, whether it is agentic layer that we are building or our proprietary models, model hosting infrastructure, or if we are using LLM APIs. Either way, we track all the traffic back and forth which is flowing. We have all the controls that we needed, like I said, detection and prevention and so on and so forth.
John Furrier
>> As a practitioner on your peers, we interview them a lot on theCUBE. We expect to have more coming in on this series. We're on an end user focus from a vendor perspective, the JP Morgan, Chase, the banks, all the insurance, all these verticals. It's a vertical dream scenario, these apps. Because whether it's a plug-in or a model, proprietary data is going to get unlocked as it continues to play. What are some of your peers thinking, if you had to high level globalize it without naming names, what is the current sentiment and mindset of practitioners today that are similar AI leaders like you?
Anand Pradhan
>> Right. One of the things which is little bit of a hype, and I have been noticing the market, that all the established technology stack will be impacted. In my opinion, that's just an overstatement. It takes years to build the platform that runs NYSC or our so -
John Furrier
>> You're not going to vibe, code it over the weekend and roll out some agents to do the stock market,
Anand Pradhan
>> Correct.
John Furrier
>> Obviously.
Anand Pradhan
>> Or our data, like you said, backtesting years and years of algorithms which have been hardened and people are using to make investment decisions kind of thing. That's not going to happen, but where can we add value? So workflows, if there are established workflows and automation can be established. Some of the front ends makes more interactive. One of the thing, if you think about it, all the BI like tools or front-end dashboard, everything is becoming interactive rather than building static dashboard. And this is an evolution I have seen in the last few years, two, three years, not a long time back. The stack has evolved from general AI implementation of natural language query to agentic AI implementation to improve the accuracy of it. So you don't have to code for implementing these things. You can ask natural language question to get the data. These are some simple example of how AI is announcing software stacks. Transactional systems, systems of record, those will not be impacted. Just to give an example, I was talking to one of my friend who joined from AWS to another company which runs a task management platform. I'm not going to name the company. But when we were talking about it, he said that people underestimate how much work it is to have a company which is managing CRM platform, task management and all those things, what it takes to build all these workflows. Those are complex, so obviously AI can enhance that thing, but it'll be hard to replace all-
John Furrier
>> So what is your opinion of the narrative in Wall Street? Obviously SAS cut bloodbath last couple of weeks. And the narrative, obviously the young starts... I call them the bomb throwers. They're going to throw Molotov cocktail at everybody. We're going to just rewrite Salesforce with agents. Like, "Okay, it's not a 3D printer." I mean Salesforce and these kind of servers, now they have established motes. How are those motes changing in your opinion? And do you buy that every single SAS company's at risk? I interviewed OpenTable right here at the NYSC, and I'm like, they've been around 20 plus years. They got every venue on the planet covered. They got demand curve with the users. To replicate that would be incredible. Now there's a data mode. So how do you think the SAS market, again, it's mobile cloud, now we're in the agent era, what's the mote? What's the architecture? What do you think people should be thinking about?
Anand Pradhan
>> I can give you an example. Open cloud became a phenomenon recently-
John Furrier
>> Overnight.
Anand Pradhan
>> Yeah, overnight. And if you talk... I was watching the interview over the weekend, the three-hour-long interview that dropped on YouTube, and in one of the sections he mentioned that whoever is claiming that coding can be fully automated, nobody has done that. Basically it runs one time kind of thing. You still have to pay attention to all the details, all the testing, all the evaluation, all the hardening stuff. So even he who built this thing, he claims that when he writes code, he builds all these things himself. So this is-
John Furrier
>> He had no constraints really in the demo.
Anand Pradhan
>> Right.
John Furrier
>> He didn't really... You can make it... Is showed... I call it the DeepSeek moment for agents, because what DeepSeek did said, "Hey, you don't need to have the big honking Nvidia systems when you can just use architectural and software." So in a way that was a similar agent where like, "Okay, it's going crazy, Moltbook. Oh yeah, agents friending each other." It just goes to show you the power. Yeah, it was off the rails and it was like a meme more than anything.
Anand Pradhan
>> But to give him credit, he was able to build something, which-
John Furrier
>> It was a good demo. It shows the power.
Anand Pradhan
>> It has a lot of power too. But as being a sophisticated engineer, he also thinks that encoding itself, where significant progress has been made in the last one year, that also cannot be fully automated with AI. So we are still far away from the perfection using AI, but we are using AI to our advantage. Same goes for other companies.
John Furrier
>> So you're pragmatic, you're taking a practical approach.
Anand Pradhan
>> Practical approach. Basically, for example, data quality improvement. So things which should take for us to long time to analyze what kind of patterns are there, makes it easier. Now you can use that knowledge to augment your data pipelines or data quality implement. For example, let's say address match, names matching and things like that, these were difficult. Or you have to code around -
John Furrier
>> So is Claude Anthropic, like the Mac, PC debate. Remember those commercials? The Mac was cooler and the nerd was the Apple Bill Gates. Open AI on one side and you got Anthropic. Anthropic is all about what you just said, use the domain knowledge and make the workflows go better. Open AI's like, "Yeah, do it. Go crazy, be cool."
Anand Pradhan
>> Just to give them credit. I know Anthropic from the inception days, we met some of the founding team members in the various very initial days. They had their first principle set, we are not going to do certain things. We are going to do certain things kind of thing. Which I like. They focused on model. They focus on Claude Code and Claude Desktop, now Claude Cowork. But if you think about it, if you have an agent running on your MacBook right now, how do you take that and deploy at scale without duplicating the same work.
John Furrier
>> Deploy at scale.
Anand Pradhan
>> Right. Because otherwise everyone would be building their own version of the same thing and it just mushrooms.
John Furrier
>> It's like they send everyone back to kindergarten, "Go meet the other agents, hang out and playing with blocks." There's not a lot of learnings there. There's a lot of... I use kindergarten as an example because that's the first day of school for a human.
Anand Pradhan
>> Then I'll tell you another thing which resonated with me. One of our best engineers, he told, "Hey, I can write a prompt, write this script, and then I can run the script five times, that way I'm not burning the token. Or I can keep writing the prompt, which would create the script and I would burn the token in five minutes for my quota." But to understand that, you need decades of experience that I can write a Python script and I can run the Python script or Bash or ORC or any other thing. It takes time. It's like a sharp knife, right now you need to understand-
John Furrier
>> You could use it well, but be careful.
Anand Pradhan
>> Be careful. Be careful in the sense that how do you use it in a regulated environment? How do you make shareable component? Are you careful -
John Furrier
>> Do you think architecturally, and this is more of a personal opinion, you can share the ICE perspective if you want. I'm just riffing on this with the Dave Vellante and on the last Cube pod. Architecturally, it seems it might be easy just to decouple the interface piece, lock that down, create a hard top or hard side, whatever you want to call it, stack, and saying, "Okay, my mode and my competitive advantage is system of record up and through workflows." So if I have experience, I have years and years of a domain, I have a trajectory, I have economies of scale. To try to replicate that somewhere else would take on massive diseconomies of scale on the back end. So I would much rather say, "Hey, I'll revert to some sort of nice clean DMZ front end to keep everyone happy."
Anand Pradhan
>> Correct. That's the approach we also took. And if you think about it, there are deterministic flows and you know that we shouldn't have any kind of variation in this flow. There will be some places where you can introduce human in the loop and some sort of variation can be there using AI. Those are the flows which we are improving. And yeah, front end obviously is one of the-
John Furrier
>> He's had a good strategy.
Anand Pradhan
>> Yeah. Then you can build data pipeline to enhance where you have basically determinism as well as AI model based, agentic AI based flows. And then wherever there is need, you need to have human in the loop to validate that thing. So optimally selecting those kinds of architecture and reviewing the flow. The other thing that we need to think about is how much maintenance you needed for all these things. Because once you build it, it's not the end of the story.
John Furrier
>> You got to run it.
Anand Pradhan
>> Yeah, you got to run it. You need to make changes. You need to enhance it. So you still need support for that. Having said that, it has given us a lot of power.
John Furrier
>> Yeah, it's been great. I love this market, don't get me wrong, but it's still embryonic. I was talking again on the pod and with other guests here at the NYSE and theCUBE studios here about the parallels between cloud native. You go back to the early days of the Clouderati, we were in 2008, '07 timeframe, small group in San Francisco, group here in New York, mostly San Francisco and Seattle. Amazon was viewed as a junkyard. That you could cobble together some pieces of this and make a car. It wasn't like the EMC drives. But cloud native got the developers in because the alternative was great. So you had that cloud native first generation wore the startups. The young guns, Airbnb, sleeping on the floor at their friend's house, start a company, Dropbox, all these things are happening.
Anand Pradhan
>> Yeah.
John Furrier
>> Okay. What happened next was the adoption came from the enterprise, and the security, I remember this in 2014, was the number one reason no one went to the cloud in 2013. Security. What did Amazon do? They fixed security. They win the CIA against IBM. They start knocking down the banks. So security was the key linchpin. Today's market, AI native, you had all the young guns and the coding away, throwing stuff against the wall, "Look what I built." There's emerging native companies coming out that have never touched cloud native. They don't even know what DevOps is. So the DevOps movement is kind of similar. Do you agree? Are you seeing parallels between the two?
Anand Pradhan
>> Yeah, very similar. Like you said, even now companies got started on top of OpenAI, API, that's how many of the companies started. And now Anthropic Stack or Google Gemini Stack or any of those things like Azure. But security always will be a major challenge, as well as people need to pay attention to it. When you are ramping up, when you are starting a company, obviously you have to build features and you have to roll out, so attention to details are not there. Once they crack the first enterprise deal, that's where the -
John Furrier
>> They've got to get the learnings
Anand Pradhan
>> Correct.
John Furrier
>> I think to me, it's clear that once the security is enterprise ready, they're getting all the startups now, they'll get the momentum. It's all basically startups, vibe coding, individuals, now democratize. Let me ask you a question about existing organizations. Cloud native certainly transform IT, information technology divisions, rack and stack, data centers. Is there personnel transition mindset from the new IT or cloud IT, cloud operations? You're running a NOC, you've got red team, blue team, all kinds of action going on. You're basically running IT operations in the modern era at ICE. What's that look like from a people change management standpoint? The center of excellence is we are under your purview. What's your view on this?
Anand Pradhan
>> What we are doing is, education is one of the main thing there. If you're ahead in understanding some stack, how do you make sure that other people understand? That's one of the thing I focus on. And make sure that people understand that, look, this is how it works. Let's poke holes in this and let's fix it. What do we need? Do we need tools? Do we need controls? Do we deny? Those kinds of discussions we have. The mindset is changing because everyone is trying to understand or implementing their own version of AI, which is a great thing. One of the things, our idea was to empower all the employees with some AI tools, which we are doing, so that people can understand how it works, what are the risks associated with it? Should I upload the file, should I not do it, kind of thing. So all those things are there and every operator is understanding, they're trying to enhance their stack themselves. A lot of progress has been made in the last one year.
John Furrier
>> Jeff Sprecher is an innovator. What's his view... Let me rephrase. You don't want to speak for him. I hope to interview him. I'll ask him. But what I like about ICE and the NYSC is, it reminds me, it's like a whole new company NYSE since I've been here. It's not what I expected. It's not my grandfather's NYSC. It's kind of a new NYSC. I think that's why NASDAQ's nervous about all these IPOs are coming here. But the innovation, it's massive. Can you share the scope without giving away any secrets to some of the operational things you guys do technically? Because I know from some conversations I've pieced it together, you guys are running a pretty large operation. Could you scope that order of magnitude for us?
Anand Pradhan
>> You want to know the footprint? Look, just to-
John Furrier
>> Without giving away secrets.
Anand Pradhan
>> Look, in order to just power this New York stock exchange, where trillions of messages are flowing, think about the data center capacity we need as well as servers, the network and everything, so on and so forth. And we are constantly trying to innovate and challenge ourselves to go to the next level. We are just not sitting quiet that, what is the next challenge that's going to come-
John Furrier
>> I mean, polymarket investment. That's a signal to me. The crypto, leaning into crypto, that's decentralized.
Anand Pradhan
>> I remember when you and I were having the conversation in the initial days, I was telling that this is how we are going to approach AI. You were like... And that's what the industry said-
John Furrier
>> Did I roll my eyes?
Anand Pradhan
>> Yeah.
John Furrier
>> No, you were very you up front. You said, "We have constraints." You guys have constraints, you're on-prem. You have proprietary data.
Anand Pradhan
>> What we do is that innovate with the guardrails we have. Innovate. Have the ability to innovate, understand the guardrails that we need to implement. But that's what I like, that we are able to innovate in spite of all the guardrails we have and regulations we have because we understand where we can stretch, where we need to limit ourselves kind of thing. And the scope of innovation, if you think about it everywhere, all the stacks, we are constantly evaluating whether we need to improve. And the culture that Jeff has built, that's what I like, it's a relentless pursuit to go to the next level.
John Furrier
>> I remember when I first met you, it was a breakfast, like, "Yeah, we'll do this thing Wired, create a digital twin of the interviews." And one of the things I mentioned to you, and we kind of not laugh, but kind of joked, said was, you were doing so many meetings with startups with Brian. I'm like, "Well, you can't have lunches with 500 companies in the next year." I think we're now here at theCube, Wired, and with the Wired community, over 650 companies, interviews. So we're starting to hit the scale. I hope you're watching all the videos. We got an AI tool you could just ingest in.
Anand Pradhan
>> We need an AI clone-
John Furrier
>> Agent.
Anand Pradhan
>> We need a clone of yourself.
John Furrier
>> Go to thecubeai.com, you'll see the agent of theCUBE. Every transcript's in there. But this is a great example of your workflow was, you do the work. I gave you the compliment on that the beginning of the interview. This is scale for you. If you had an agent rolling up the TLDRs, "Hey CUBE, hey agent, tell me, what did I miss? It's my filter. I'm looking for a company that's going to be doing security ops, AI agents with tight security parameters and constraints-
Anand Pradhan
>> .
John Furrier
>> "... physical AI. Here's a video here." That's where this goes.
Anand Pradhan
>> Yeah. True. That's where your interface comes into the picture. You have the data now built with all the interviews that you have done, and then you can put an interface there with AI agents and ask questions, summarize it.
John Furrier
>> Well, technology is the market. I look at the options board here. It's Nvidia, Tesla, a few randos on there based on what's going on with earnings. But for the most part, technology is the market. There's a huge thirst for technology change. End users are coming in. This is our Practitioner Series. Thank you for kicking off the program. We're going to do a deep dive. We're having an ongoing AI technology, emerging technology Practitioner Series where we feature practitioners, people out doing the work, getting it done, exploring the future, staying within the guardrails and constraints of their business or operation, pushing the envelope, managing the risk without foreclosing the future. And that's the goal of this series. I'm John Furrier, your host, the NYSC CUBE Wired community. Thanks for watching.
John Furrier