Kevin Levitt, director of global business development for financial services at Nvidia, joins Remy Blaire to discuss new survey data showing how AI adoption is driving revenue, efficiency, and operational scale across financial services.
Remy: Artificial intelligence is no longer a future bet for financial services. It is already a core driver of how money moves. It is rapidly reshaping how the industry operates and according to a new industry survey of more than 800 financial professionals, does show AI adoption at record levels. And firms are not just testing the tech, they are scaling it across core functions like fraud prevention, customer service and risk management to drive real returns.
While joining me with the insights into the sixth annual Nvidia State of AI and Financial Services report is Kevin Levitt, director of global business development for financial services at Nvidia. Good morning and thank you so much for joining me. Well, the 2026 survey was just released, and nearly 90% of respondents say AI is boosting revenue or cutting costs.
So where is the payoff biggest right now and where is the hype ahead of reality?
Kevin: I think that was one of the most exciting aspects of this report is when we asked these respondents around where they’re generating ROI from their investments in AI. It’s on both sides of the coin, so to speak. It’s both generating new revenue and reducing their annual costs. And that’s driving this near 100% response rate. we asked the same group of individuals, you know, will your financial services firm increase your spending and at least maintain budgets for AI in 2026?
And it was a resounding almost 100% that said they would. And so we’re really excited to see the continued momentum for AI and financial services. And as you said, it’s not about testing or pilots and POCs. It’s about actually deploying AI enabled applications into production and financial services.
Remy: Yeah. And Kevin, building on that, open source models are becoming core to strategy when it comes to AI. So can you walk us through how they change the competitive balance for financial institutions?
Kevin: Yeah. Happy to. You know, 84% of respondents said that open source software is moderately to extremely important to their organization in financial services and their AI strategy. And the reason for that, and this is one of the biggest swings of the pendulum, so to speak, that we’ve seen over the last few years is this migration away from off the shelf, proprietary AI solutions to leveraging open source models. Because what the banks want to do is take open source models and marry that with their proprietary data to build more accurate outcomes from generative AI and energetic AI capabilities. the other benefit that they’re seeing is that by using open source foundation models, the banks are able to keep their proprietary data in-house.
They don’t need to export it to a third party. So really, the intelligence of the enterprise and financial services stays within the bank’s data center within their four walls, and they’re able to deliver more accurate, AI enabled applications to either internal employees and or their external customers.
Remy: Yeah. And you just mentioned agents,AI, AI agents, it goes without saying or moving from theory to deployment. So for the layperson out there, can you tell us what tasks they’re handling today and how fast they’re actually scaling?
Kevin: Yeah. Happy to. I mean, they’re handling everything from knowledge management and retrieval of information. Uh, they’re helping with internal process automation. They’re engaging customers through support channels. And, you know, the real power of agentic AI now is all the sophisticated reasoning models that these agentic AI are powered by. And we used to use large language models, and they would generate sort of a one shot response just immediately give us the answer to the question. Now with the agentic AI These models are actually reasoning, and that long thinking requires 100 times the amount of compute than traditional LLMs. Now, the benefit of that long thinking in that compute is that you’re able to get more accurate answers. that’s why banks are investing at scale in Nvidia’s accelerated computing platform, because it’s powering not just the training of these models and financial services, but also their deployment inference, because you need accurate outcomes and low latency that our platform enables.
Remy: And finally, before I let you go, 2026 is well underway. And we’re hearing from companies, given the fact that it is earnings season. And in addition, there are plenty of IPOs that we’re closely watching. But with AI budgets rising across the board, what gets priority next based on your perspective, is it more use cases, better execution or the infrastructure to support it?
Kevin: Yeah, those are exactly. That’s exactly where we’re seeing companies say they’re going to invest in 2026. They want to, of course, identify additional use cases, optimize their existing workflows, and importantly, continue to build and provide access to AI infrastructure. What we’re seeing is banks investing at scale in AI factories, and this is a foundational platform that sits across the enterprise, because AI is going to touch every function, every line of business within the bank. you need an accelerated computing platform to deliver these AI enabled applications at scale reliably, to enable all of these functions that are supporting not just hundreds of use cases today, but it will be thousands into the future of financial services.
Remy: Well, Kevin, we will have to leave it there. But thank you so much for sharing the findings from the latest report. I appreciate your time and all of your insights.
Kevin: My pleasure. Thank you.