Google Cloud Next 2026: How Agentic AI Is Transforming Financial Services

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So if you wanna learn more, please head over to nordlayer.com/browser and see how it fits into your workflow. But now, it's time to get today's guest on. Welcome back to the Tech Talks Daily podcast where I'm recording from Google Cloud Next here in Las Vegas. Where one message keeps coming through louder than ever. AI is no longer a side project for industries like financial services.

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It's actually becoming part of the operating model. And for years, banks and finance civil institutions approached innovation with understandable caution. Because regulation, compliance, customer trust and zero tolerance for mistakes all meant that transformation often moved slower than in other industries. But standing still is no longer an option. The pace of AI developments is forcing even the most traditional institutions to rethink how they work and how they serve customers and how they compete.

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So the conversation has moved well beyond chatbots and document search. We're now entering what many are calling the agentic era, where intelligent systems can reason, act, anticipate needs and even support decision making in real time. And for financial services, this creates an enormous opportunity, but also enormous responsibility. So joining me today is Sid Nadella, Director of Financial Services and Market Leader at Google Cloud. And Sid leads the financial services capital markets practice within Google Cloud's global strategic industries team, essentially helping major global financial institutions transform and scale using Google's technology.

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And with deep experience from his time at Goldman Sachs and now working closely with banks around the world, He is someone that's got a front row seat to exactly how AI is reshaping one of the most regulated industries on the planet. So today, we'll explore how AI adoption is changing across wealth management, banking and capital markets, and why financial institutions are shifting from dashboards to intelligent decision making systems, and understand how firms are balancing speed with trust in a zero error environment. And Sid will also share where he sees the biggest ROI opportunities in everything from growth and operational efficiency to fraud detection and risk management, and why strong data foundations and governance are all non negotiable in this industry. And we'll talk about the people side of the transformation as well because despite the headlines around AI replacing jobs, as you'll hear today, the real story is about augmentation, upskilling and helping people focus on higher value work. So it's a conversation that I hope will bring real clarity to one of the biggest questions facing enterprise leaders today.

[00:04:07] - [Speaker 0]
But enough from me. Let me introduce you to him right now. So a massive thank you for sitting down with me here at the event. For anyone listening, hearing about you for the first time, can you tell them a little about who you are and what you do?

[00:04:20] - [Speaker 1]
Yeah. My name is Sid Nadella, and I work at Google Cloud in the industries team. I work in the financial services team. I come from the industry. Started my career at Goldman Sachs, and I was there for a majority of my career there.

[00:04:33] - [Speaker 1]
And, yeah, I've been here two years, and it's an exciting time to be here.

[00:04:37] - [Speaker 0]
It really is an exciting time, and there's so many announcements here, and 32,000 people as well. I'm curious from the conversations you're having and all the announcements, everything that you're soaking in here, what excites you from everything you've seen so far?

[00:04:51] - [Speaker 1]
It's hard to pin one thing down. It's so many new things. I think the the pace of change in, you know, what and the capability of these models is super exciting what kind of possibilities it opens up, both from our industry and other industries. You know, one thing about our team is not only financial services, but we and my teammates come from all industries. So I I see patterns across, like, you know, all regulated industries, like health care, life sciences, manufacturing, supply chain, and and there's a lot of similarities, and and it's exciting, like, how much change, you know, AI is driving, and it's so broad across all industries.

[00:05:27] - [Speaker 1]
So it's an exciting time for sure.

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It really is. And, again, from everything that you've seen, how have you seen conversations around adoption of AI shifting in the financial services industries over the last twelve months? Because, as you said, the speed of change is just phenomenal, but what are you seeing here?

[00:05:44] - [Speaker 1]
So I think, you know, look, if you look at, like, AI and capabilities, over time, it shifted from, like it initially started with, like, searching and frencing documents, finding things, and then we went into deep reasoning. And I think the way I think of it, like, it's now moving into a a really, like, a doing era, like, which is, you know, actually autonomous action. So we're really shifting into that agentic era. And, you know, the pace of change is is a challenge for most financial institutions. They're not, like, used to dealing with that faster pace of change.

[00:06:19] - [Speaker 1]
I think that is, like you know, that causes a lot of anxiety at these firms, but it's also locking unlocking a lot of potential for them. The initial use cases that we, you know, we see scaled in production, everybody scales some use cases in production and we've seen them. They're getting value out of them, but most of them are, you know, very fragmented, very targeted to a certain segment or a certain use case. And most of them require a human in the loop, you know, as a control function. You know, that's there.

[00:06:48] - [Speaker 1]
But we are seeing some, like, some experimentation from from agentic autonomous use cases, and the potential is enormous, you know, obviously. From from an industry perspective, as an example, some of the things that we're seeing is the industry is shifting away from, like, static dashboards and static applications to sort of more intelligent, intuitive interfaces that that can, you know, give you analytics, like, you know, real time analytics that and anticipate changes, not just, like, give you static data, but anticipate what you need and present that data when it's needed. They can reason through scenarios, and and and this really helps, like, you know, set up, like, applications and interfaces that can empower, you know, customers or users to make better decisions. You know?

[00:07:38] - [Speaker 0]
Yeah. And I go to a lot of tech conferences. Obviously, AgenTiKi is a word I hear all the time. And the big message for me here is we're not talking about future use cases or future promises. It's things that are happening right now.

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I was talking with City Wealth yesterday. We're talking about the app that they announced and the partnership with Google Cloud there. It's phenomenal, isn't it? It's not something that's gonna happen in the future. It is happening right now.

[00:08:00] - [Speaker 1]
It is happening right now, and I was talking to somebody yesterday, and, you know, it's it's we were comparing it to, like, you know, self driving. Mhmm. Right? It's the way more of, like, wealth management, you know. It it the the potential is enormous, and the impact is also enormous.

[00:08:15] - [Speaker 1]
Right? Like, think about, you know, across the world, you know, financial literacy and and what kind of access people have to to, you know, financial advice. And that's pretty limited. It's hard to scale that because it's a very you know, it's it's limited by human capabilities and skills, but it opens up, you know, a huge potential for for organizations like Citi to elevate that experience, to provide a better experience for their customers. So, yeah, it's pretty exciting.

[00:08:47] - [Speaker 0]
It really is, and I would urge anyone listening to check out the video. I'll include a link to that just because I think use cases like that or real world examples really bring it to life. And in a zero error regulatory environment, though, the question I've got to ask is how do financial institutions balance that need for speed with that necessity of safety and trust? Because there's no margin for error there, is there?

[00:09:08] - [Speaker 1]
Yeah. There is no no margin for error. Look, mean, services institutions always look at, you know, innovation through the lens of risk and responsibility. For them, you know, protecting customer data is is is extremely important. It's paramount.

[00:09:21] - [Speaker 1]
It's, you know, priority one. So for them, it's always through that lens, and the pace of change with AI is particularly, you know, concerned there because they have to move it's forcing the industry and forcing firms to move away from two to three year tech road maps where, you know, they used to take a lot of time to onboard new technology. And one of the things that we hear from these firms is the pace of change is so high, I need to do real time deployments. I need to be able to test these new capabilities. And to test them, I need to be able to, like, then, you know, bring them into the organization.

[00:09:52] - [Speaker 1]
I need to certify them, and I and they need to do that really fast. So that's forcing them to do that. But, really, you know, they need to think of these platforms, the underlying, like, foundational platforms. They need to think of, like, how to implement the right compliance rules. It's not like plugging into one use case.

[00:10:11] - [Speaker 1]
It's like from a platform perspective. If you wanna really scale it and build agentic orchestration where you can have not one, two, three, but, like, you know, tens of agents, you need a solid foundation that you can then use to govern these agents to, you know, look at security, you know, data sovereignty. How do you, you know, implement observability, compliance rules. Right? Yeah.

[00:10:34] - [Speaker 1]
Because this compliance rules can be quite complex for each firms depending on the kind of firm. So those are all foundational capabilities and and some of the announcements you saw yesterday with regards to agent identity and governance. So that's a big focus for most firms, like implementing that foundational capability, that control plane across, like, all these implementations. Because it's really, you know, agentic applications are gonna be like applications, like, in the past. And and, you know, we saw like digital transformation where like a lot of firms have implemented a ton of applications and they needed in all the primitives, like compute storage and all that kind of stuff.

[00:11:12] - [Speaker 1]
And they needed like the governance layer. It's it's this very similar thing with agentic applications, but the complexity goes up quite a bit because now there's that intelligent layer, that, you know, autonomous intelligence, and and so you need to be able to govern that properly.

[00:11:27] - [Speaker 0]
You really do. And another thing that stands out to me here is we're we're not talking about a shiny new technology or buzzwords. It's about there's a strong focus on business outcomes and return on investment and and growth as well, I would say. So I'm curious, where do you see AI driving the most impact for financial institutions?

[00:11:46] - [Speaker 1]
We see it driving massive impact across three main areas. Right? The first is around how financial firms look at growth. Look. I think, you know, most financial services institutions, organic, you know, growth has been challenged, structurally challenged over the last couple of years.

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It's hard. And, you know, a lot of these firms are turning to AI to unlock that organic growth in terms of you know, as an example, it can be, market insights, you know, getting market insights as quickly as possible. Or it can be, you know, how do you reimagine the customer facing financial advice and guidance experience. So we see a lot of those, like, initiatives that are coming up. And you mentioned City Wealth.

[00:12:27] - [Speaker 1]
That's a great example. Mhmm. I think or, you know, another great example is, like, Citadel. They were also on main stage yesterday talking about using TPUs for their Quant Research platform. So that's that's that's a big area.

[00:12:40] - [Speaker 1]
Unlocking growth is extremely important. The second one is around operational efficiency. Operational efficiency is always, like, very, very important at banks because that's a big cost center for them. And using AI and AI infrastructure to, you know, modernize their core banking systems or, you know, to accelerate, like, developer efficiency and developer productivity, that's been an early use case and very successful. That's important.

[00:13:06] - [Speaker 1]
But they're also moving to, like, you know, automate some of the key business processes, like, it can be underwriting or, you know, call center management, you know, customer engagement, you know, things like that where they can actually reduce costs and get more efficient. You know, it's massively beneficial for them because they really change the cost structure Mhmm. And they can move faster. They can they can pre engineer their cost structures and move faster. So that's a huge benefit for them.

[00:13:31] - [Speaker 1]
So that's the second bucket. We see a lot a lot of firms doing that. Some great examples as well with CME and Goldman Sachs and and developer productivity and Scotiabank, you know, transforming the call center operations. And the third area that financial institutions really, you know, drive impact is is risk management. And we recently did a survey of the top 500 financial services institutions, and and the top use cases for AgenTik AI at these firms was, you know, fraud detection and management, you know, risk management, you know, compliance, and security.

[00:14:09] - [Speaker 1]
These are really important areas. Right? We've seen firms drive massive impact with AI and even cloud infrastructure. As an example, with risk management, firms that have, like, done it, like, you know, used cloud infrastructure and scaled it have been able to turn it from, like, a cost function to a strategic asset because it really enables them to make decisions, you know, smarter and and faster, and it really empowers the business. It's foundational.

[00:14:38] - [Speaker 1]
So I think, you know, that was that was an important area as well. There's some great examples for this as well, and Starling is a UK bank. It's and and they've implemented fraud detection agent. And it it scans through marketplace ads and and looks for fraud fraud patterns, And it helps, like, customers detect, you know, and make smarter decisions in what they buy. That's, you know, a great example.

[00:15:02] - [Speaker 1]
Or Commerce Bank, for example, they use Gemini to to transcribe calls that with advisers and their clients. It's a regulatory function. Advisers need to transcribe these calls and and and and do regulatory notes. And it just, like, completely changes, like, how fast they can do them and really helps them, like, focus more time on those customer interactions. So I thought there was a there were some good examples.

[00:15:26] - [Speaker 0]
I was going to say, for for many people listening, they hear these big names, and there'll be organizations that maybe feel a little overwhelmed with the pace of change and not knowing where to start and not wanting to get left behind. So what does a JentiKi look like in practice today? And any other success stories that might inspire people listening that move beyond pilots to real world deployment? Because I think there's a lot of leaders looking for a little guidance here as well, isn't there?

[00:15:51] - [Speaker 1]
Yeah. I it's it's pretty broad. And and, look, I think a lot of this is new, so there's different levels of maturity at all these firms. But Citi Wealth is a great example in how they're using, you know, the AI avatar based and Gemini Live, you know, based AI system that's always on. That's a great example.

[00:16:10] - [Speaker 1]
Another great example is Macquarie Bank. They've built a queue, which is a banking assistant that's able to, like, answer basic questions for customers, and and that's another great example. And another big bank, LATAM Bank, Banco BV, they've rolled out Gemini Enterprise to all their employees, and it's it's also improving their collaboration and how they interact with customers. It highly personalizes, like, how their customer interactions go. So those are some great examples.

[00:16:40] - [Speaker 0]
And for leaders that are looking to the future, what are would you say are the the fundamental pillars of a successful long term AI strategy in this sector? It's not just about throwing something over the line and it goes live and everyone's happy. It's continuous, this this kind of thing. So what are those successful pillars to build on and and enjoy that success and keep building?

[00:17:00] - [Speaker 1]
Yeah. I think we're we're in the edge era of, like, agentic transformation. I think this is like an evolution from digital transformation, so that agentic orchestration, where you set the foundational platforms where people can build agents to automate some of the key business processes across different, you know, different segments of the business. I think that's extremely important, and that's a you know? And and and underlying that and is gonna be the platform, the governance, and the compliance rules.

[00:17:27] - [Speaker 1]
They're foundational for most of these firms. So setting that out right foundation enables that agentic orchestration. And, look, I think, you know, there is gonna be a people element to this because AI is here to argument people. Right? It's you know, we want AI.

[00:17:42] - [Speaker 1]
AI is, like, really really good at taking away the mundane repetitive tasks and help people, like, focus on the more important items. But it does require a lot of, like, upscaling of people and thinking of, like, how people can use these tools. So there's a people element to it too. And, you know, data data is another piece. Like, you know, the data foundations and how you serve data to these models is is important.

[00:18:08] - [Speaker 1]
Some of the some of what we are doing across, like, you know, the cross cloud, you know, knowledge and knowledge catalog that we announced yesterday and then the cross cloud connectivity. Because the reality is that most financial firms, data is in silos. Yeah. And and there's good reasons for it. It can be on different cloud providers as a multi cloud strategy, or it can be on prem.

[00:18:30] - [Speaker 1]
So accessing these these agents and the intelligent agents need to access data where where it is. So being able to, like, set up that agent that data foundation and serve data to these intelligent systems is also important. And, you know, last but not least, you know, sovereignty and choice. Right? As you think of these platforms and systems, you need to think of, you know, where it's a constantly changing world, as you know.

[00:18:55] - [Speaker 1]
Mhmm. So, you know, firms need that choice to, you know, use the best in class and being able to, like, you know, have a choice of, like, which data provider or which, like, cloud provider or which model. I wanna be able to across the full stack, you want you want that choice of, you know, what you use and how you set it up for every business that's different. So I think those are some foundational elements.

[00:19:18] - [Speaker 0]
And I'm glad you mentioned people there because I think anybody that spends any time doom scrolling down a newsfeed will see the headlines of AI replacing people. But the big message here is it is about upskilling. You need to bring your people along for the journey, and there's a lot of companies that we may have seen when we're doom scrolling that investing in more AI servers means less people, for example. But that's not the message here at all, is it? And what kind of challenges are or how are you helping people upscale and bring the people along for the ride as well?

[00:19:46] - [Speaker 1]
I think it's like anything new. I think, you know, when people start using it and they realize, like, how much more how it can help them

[00:19:53] - [Speaker 0]
Yeah.

[00:19:54] - [Speaker 1]
It it really empowers. So it needs to start, like, from a grassroot level where, like, people understand what these new tools are and being able to experiment with them. Really, like, what I've seen is it unlocks a lot of creativity too because, you know, people start like, they understand how these tools work. They're like, oh, I can actually reimagine this process or I can do something new in a completely different way. It unlocks that creativity, which is, I think, you know, really good to see, like, you know, at firms that have done it.

[00:20:22] - [Speaker 0]
And looking back, there was that old mantra that there's nothing more damaging in business than we've always done it this way. That mindset change, that shift, that thirst for change and wanting to do things differently, are you seeing that in very traditional industries too?

[00:20:36] - [Speaker 1]
Yeah. We are. Because, you know, it's it's right. You see these model capabilities and what's happening in the industry. That's forcing that's forcing that function, like, that you need to innovate faster.

[00:20:48] - [Speaker 1]
It's always been, like, you know, some of these, you know again, like, most of these firms are very conservative. They look at, like, innovation through, oh, like, you know, we have regulations and compliance and and responsibility and risk. They look at that, and that, you know, that hampers them. This slows them down. But they're they're it's forcing like, some of the newer capabilities are forcing them to rethink how they think of innovation.

[00:21:14] - [Speaker 1]
You may need to move faster and adopt these tools because it's just the the potential uplift is so enormous that you

[00:21:21] - [Speaker 0]
can't ignore it. You know? And for anybody listening that wants to find out more information, dig a little bit deeper, learn how they could work with you, see examples. Any way in particular you would like me to point everyone listening?

[00:21:33] - [Speaker 1]
Yeah. I mean, Google Cloud, you know, we have our websites, and there's a lot of information about different industries and and and how AI is being used across industries and what impact it's driving. So there's a lot of information on the web. We also have, you know, training and and courses that people can take. And most of our tools are really, like, know, I tell people that most of the time, you can experiment with all these tools.

[00:21:57] - [Speaker 1]
As a as a general user, you know, AI Studio is a great example. We've seen like developers embrace AI Studio and experiment with like the models and different capabilities. So there's a lot like of of things you can do. Even our Gemini app that you can download on your phone, you know, it's it's really fun to use and learn about, you know, how what the capabilities are, so there's a lot you can learn.

[00:22:21] - [Speaker 0]
And for anyone feeling overwhelmed with everything, I heard another phrase conference recently that was, you're closer to agentic than you think, and I think that hit the nail on the head. There are so many resources from what you guys are doing here, and I'll include links to everything that you mentioned, and I would encourage anybody listening to go check those links out, have a play, roll up your sleeves, and and see how it works, what problems you can solve. But more than anything, just thank you for shining a light on this today.

[00:22:44] - [Speaker 1]
Of course. Yeah. Thank you for being for having me here.

[00:22:47] - [Speaker 0]
I love how clearly Sid framed AI as an evolution of a business strategy, not just another tech trend. And financial services has always had to balance innovation with trust, and few industries feel that tension more sharply. And when there is no margin for error and every decision carries weight, I think his point about building the right foundation first, governance, compliance, observability, identity and data access, that's why all these things matter so much. Because without that, a genetic AI becomes risk rather than opportunity. So I loved his thoughts on the, what he called, the doing era.

[00:23:29] - [Speaker 0]
Because we have moved from searching and summarizing into a world where AI can now take action, anticipate needs and support better decisions in real time. And that changes everything, whether it be customer advice and fraud detection or just operational efficiency and market intelligence. And the City Wealth example, I think, is something else that brought this to life because it showed that this is no longer about future possibilities. These systems are being built now and they're changing customer experiences today. And he gave so many examples there from Citadel and Starling Bank.

[00:24:07] - [Speaker 0]
This is real enterprise transformation happening in real time. And most of all, I think I'm glad we ended on people because all too often, the AI conversation gets trapped in fear. And his point was simple and so important. Yeah. AI works best when it removes repetitive works and gives people more space for judgment, creativity, and better customer relationships.

[00:24:29] - [Speaker 0]
This is a much more positive story than replacement. So a big thank you to Sid for joining me at Google Cloud Next and for helping listeners understand what Agentic AI looks like inside some of the world's most complex industries. And for you listening, how are you and your organization thinking about AgenTic AI now? Is it still a cautious experiment? Or is it becoming part of your long term operating model?

[00:24:55] - [Speaker 0]
Let me know your thoughts. Techtalksnetwork.com. 4,000 interviews over there. Lots of ways of contacting me. I'd love for you to get back to me on that and share your insights and your stories.

[00:25:05] - [Speaker 0]
But it's time for me to go now. I'll be back here same time tomorrow, and I hope you will join me once again. Thanks for listening. Bye for now.