AI in Financial Services: Costs, Risks and Path Forward

August 27th, 2026 | Jason Lopez | 11:37

▶

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

Get tech leader insights to move faster and smarter.

Get more stories by subscribing to The Forecast.

Podcast transcript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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