U.S. Bank's Chief AI Officer on Strategy, Governance, and Scaling AI

Introduction

Michael Krigsman: U.S. Bank is the fifth-largest commercial bank in America, with 70,000 employees, nearly $700 billion in assets, and over 2,000 branches. Deploying AI at that scale isn't a technology problem; it's a business challenge.

Prashant Mehrotra is the bank's Chief AI Officer. Prashant, where do ideas for AI initiatives come from? How do they get decided? How do they get funded?

How AI Ideas Get Evaluated and Funded

Prashant Mehrotra: We work across various different lines of business, both in a bottoms-up and a top-down approach, ensuring that these ideas are aligned to our business priorities, serve our customers, and are executed in a fair and responsible manner.

We evaluate these ideas, not just for the value they bring, but also for things like technical feasibility, managing the risk, aligning with the risk posture of the bank, and ensuring that we are doing these things in a transparent and communicative manner. And we have hundreds of ideas that we review and we are intending to deploy.

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How do you go through that evaluation or filtering process? Because I'm sure, as you said, you have hundreds idea of ideas, but you're also under a very large set of constraints. How does an idea actually make it through that tunnel? And also, what's your role as chief AI officer in that?

The Role of the Chief AI Officer: The 4 E's

Prashant Mehrotra: I'll start with the last point first, because I think that is a key question. We see more and more of chief AI officer roles being executed and hired across not just banking, but across various business sectors.

I look at my role as the chief AI officer in 4 E's. It's not just a new technology, I look at it as a transformative force. So when I think about 4 E's, I think about evangelizing, educating our workforce, enabling various different builders across the US Bank in building these AI capabilities, and then there are certain strategic priorities or key initiatives that we want to execute on that require absolutely new emerging technologies and capabilities.

I'm going to walk you through a few little details of how we actually work across in the bank. First of all, we need to make sure that these ideas are aligned with our overall strategic objectives and business priorities. So for that, we work with our lines of business and our chief product officers to ensure that these ideas are something that they want to pursue and align.

Then once that has happened, we look at the feasibilities, the cost. Sometimes, because of how fast this technology is evolving, we want to make sure that these ideas can be executed using the capabilities that are already available. Sometimes a lot of times they are, but sometimes they are not. So those ideas, we will actually add it to the backlog, and we'll revisit that periodically.

Once we have done that triage, we will evaluate these ideas on the change management, the readiness, but also the value, both in terms of help to our customers, revenue growth, or savings. And then we will try these ideas. We'll first pilot them. We'll put it in hands of few of our users or our clients, and then we'll scale.

Throughout this journey, we are always looking to make sure that these ideas are feasible, they're delivering the right value, and they are aligned with our overall values and risk posture.

AI as a Transformative Force, Not Just Technology

Michael Krigsman: What you're describing sounds like a fairly typical process for technology in general. Are there unique aspects that come into play because we're talking about AI here, or is it all part of the general bucket of technology, and therefore, all handled simply the same way?

Prashant Mehrotra: One of the things that we are doing here, when it comes to AI, is making sure that that intelligence that we are introducing in the process, we are not just making some process more efficient. We are not using just a better technology. There is a cultural shift here happening on asking the question about how does this intelligence, and I would call it an intelligence layer, not just a capability or a technology, how does that help us transform that interaction or transform the process to make it entirely seamless for our customers?

One of the questions when it comes to AI that we are asking is, is this process necessary or required in its current iteration or current form? There's a lot of talk about how many of these ideas or pilots or prototypes, depending on what phrase you are using, are making to production.

And what I can tell you is you can use AI to make something slightly better, or you can use AI to completely transform how you interact with our clients. Something that we just launched, and our chief consumer digital officer just talked about, is we provide APIs and we have our developer portal that we make available to firms that we interact with. Not only we introduce generative AI-based developer assistant, it actually helps them not just find the right APIs and capabilities for them to use, but it also helps work side by side with them as a wingman to help troubleshoot and identify those opportunities.

So this is not just doing something slightly better, helping you find or creating a FAQ or providing an assistant, but working with you as a wingman on how we can do these things entirely differently.

Michael Krigsman: If you're a business leader navigating change or driving growth, explore executive education programs with Emeritus, a global leader. They offer programs developed in collaboration with top universities, designed for decision-makers like you. There's a program tailored to your goals, whether that's AI and digital transformation or strategy and leadership. Find your program at www.emeritus.org.

When it comes to AI, you are also evaluating the transformative impact of the technology as opposed to many other technologies, I don't know, a CRM system, for example, which is primarily driving greater efficiency.

Real-World AI Impact: Customer Service and Software Development

Prashant Mehrotra: That's true. We are looking certain efficiencies, but it's completely changing the game. It's not just maintaining a Rolodex of a customer in a better system kind of thing. It's how do we contact the customer? How do we actually, whether it's internal workforce or external clients, how do we interact with them better?

Another example I'll give you is how we get the information while the customer is on in moment and is on the call with us. That's another example where we have seen our customer satisfaction jump significantly because now, instead of taking minutes and a lot of awkward pause, we can provide them the answer quickly, and these are the things that we are significantly changing the game on.

Another example I'll give you, and this one is really exciting, is not only we are changing doing something more efficiently, but changing how we are creating these assets. We are introducing AI for our developer workforce, for our engineering workforce, and what we are seeing is the term I use internally is wingman. It's not just creating software or features faster. It is creating them more intelligently, being able to test them, being able to actually deploy these things faster, and we are looking at the value chain of end-to-end software development, deployment, quality engineering.

Can we do these things all at once? We will always like to, but we are going about it in a methodical fashion, and doing these things much better and in a much different way than we ever used to.

Measuring AI Success: Metrics That Matter

Michael Krigsman: We have a question from Arsalan Khan on Twitter, and he's asking about metrics. When you're generating the ideas of what could AI do for U.S. Bank, are there metrics? How do you make those evaluations? And I know you spoke about this, but if you can, again, kind of focus on any metrics that you use.

Prashant Mehrotra: I always believe that you need to measure what's important, and what you cannot measure, you can't manage. We look at metrics on multiple different dimensions and different scales. So I don't know if I can actually talk about a more holistic view, but I'll give you 2 examples.

You heard me talk about creating better software and doing software faster. One of the things we did was automated, using AI, both generative and classic AI, to be able to review, do code reviews, provide a higher coverage, and do those things faster. And when we started, we had a fair sense of, like, we think it will, we might be 30 or 40% better than before. And as we developed the capabilities, we deployed it to a small pilot group. We saw, we measured what it used to be and what it is doing now, and then we saw that the actual results were significantly overshooting our initial expectations. And when we moved out of the pilot, we were much higher than a 50% faster rate of being able to analyze and review our code.

And what that really means is it tells us what the benefits are, but also resetting those expectations and working with our business, with our development community.

The other one, I'll give you an example. So that's an internal one. The second one example I'll give you is when we rolled out being able to provide the nudge and the information to our contact center workforce on use of gen AI, we saw what used to take us minutes, multiple minutes in providing the answer and a solution to our clients, we are able to bring it down into tens of seconds, and that's a significant improvement. Our clients are busy. They want to get the information, act on it, and go about their way, and now we can actually do it order of magnitude better than they were before.

So my advice and my ask of my team and direction has always been is, let's make sure that we set an initial expectation, we review it during the process, and make sure we are only scaling up the most impactful of use cases and capabilities, both internally and externally.

Michael Krigsman: Two of the reference points that I've picked up are, number one, how does this benefit our customers? And then, number 2, Prashant, what you just said, which is, are we making the largest impact that we can make with the investment that we're proposing on this particular project or issue?

Establishing Baselines: Why 95% of AI Pilots Fail

Prashant Mehrotra: I'm positive that all the users who are, all the audience who are tuning in today, they have all heard about the MIT study that came out talking about 95% of the pilots fail. If you don't have the right metrics, and if you don't have the right focus, which for us has always been our clients. As long as we keep those two things in focus, we can make sure that not only we have a very high degree of impact, but we are also being able to scale these things.

There was one thing I, as you were asking me the question, that I remembered that I should say is: It's really important for us to measure, as we are rolling out these capabilities throughout the life cycle, from pilot to a general availability scale version, but what's really important is what do you compare it to? So you need to also have a baseline of how you are doing those things today, how long it takes, what's the quality around it. Because you can say we are reviewing code faster, but do we know that we are making a measurable impact on the quality of the code or the quality of answers we are providing to our clients?

So those are the things that are really important, all the way from baselining your existing to your new transformative interaction and capabilities.

AI and Sustainability Goals

Michael Krigsman: We do have a question from James Hook on LinkedIn, who says: "How does the AI strategy cooperate with the sustainability goals of U.S. Bank?"

Prashant Mehrotra: We publish data and report and information on it on an annual basis. We work with our partners who provide these capabilities, as we are bringing these capabilities in and rolling them out in a thoughtful fashion. They are not just aligned with our sustainability goals, but we are constantly working throughout and sharing transparently these capabilities and their usage externally. And for more information on this one, I'll refer you to our corporate governance information online.

Managing Risk and Governance at Scale

Michael Krigsman: On the topic of governance, we have a question from David Los, who on LinkedIn, who says, "What's currently the biggest constraint that you run into when trying to achieve outcomes at scale? Is it data governance?" operating model or adoption? Where are the challenges?

Prashant Mehrotra: I've been very lucky to have the risk partners and the governance partners and the compliance partners I have at the bank. We have treated this not as something that the AI Center of Excellence or the chief AI officer does, and then passes or works with our data team, or works with our technology team, and then passes it to the risk team. It's never been like that. It's been a highly collaborative effort.

We meet regularly. We talk about not just individual use cases, but how are we accelerating AI adoption and building the capabilities and introducing the capabilities to the bank. And we look at the entire life cycle of these capabilities on how we are able to do that.

Working with my business line chief risk officer, I was really encouraged on his approach, working closely. I can tell you that we have cut down. We have not just increased the time that it takes to roll out something this new and this transformative, but we have actually been able to cut down in the last 6 months the time it takes for us to go through the governance and approval process.

Now, that doesn't mean that we are adding risk, but what that means is that we have made this a repeatable process.

We are engaging my risk partners very early on in the process. We give them a sense of what is coming down, providing them the transparency, and making sure that whatever we can actually learn during one new capability or use case introduction, that we build it in the platform, and we roll it out repeatedly, and repeatably, I should say. So we are seeing in some cases the approval and the risk approval times being cut down in half, so that the next one and the next one are going faster and better.

In terms of risk, I know you asked a multifaceted question: risk, governance, data. AI is not just another new technology. It's not just a slightly better tool to do what we were doing before as is. So that kind of transformative technologies, we have to look at everything that is involved to bring these things, this capability, to life, and putting them responsibly in the hands of our users.

So I work very closely with our chief technology officer, with our chief data product officer, and we are improving each one of these, in some cases incrementally, and in some cases, taking significant step changes. And we are seeing good progress. AI by itself isn't the panacea that we want it to be. We need to make sure that we are getting all the right partners across an enterprise, and I should say internally and externally, to make that improvement.

Moving from Pilots to Production

Michael Krigsman: Let's jump to another question. I love taking questions from the audience. You guys in the audience are really smart, and your questions are wonderful.

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So here's a question from Rohit Nagpal, who says, he's asking about that MIT study where it said 95% of pilots fail. And let me just, and he's asking where to get it, and just, Rohit, just search on MIT pilot failure study. You'll find it.

But let's jump back a little bit and talk about pilots in general. Many organizations do struggle to move beyond pilots, so what has your experience been, and what are some of the lessons that you've learned in moving from pilots to production?

Prashant Mehrotra: When it comes to AI, or any other new transformative capability, there's always a lot of excitement. You want to be able to harness that excitement, those ideas, so we always end up with a lot of pilots. But if all we are doing is jumping from that excitement to running some of the pilots without a strategy and a goal of scaling, it really never works out.

When it comes to AI specifically, scaling is the key. You want to make sure that you take that initial excitement and don't turn it into a list of pilots or a whole bunch of that. We talk about the strategy of having 1,000 flowers bloom. What we need to do, what we have done is we create these pilots, and we bring those groups together in terms of pilots, and make sure that we have a very quick and transparent process to take it to scale.

Again, I'll go back to this example of product development or AI-assisted product development. We had 3 or 4 different pilots on how to do code reviews. We ran it, we brought the group together, and we took the right ideas and the right approaches from various different pilot, decided on one final product, and then we quickly scaled it. But we did it with all the groups having a seat at the table, and right now, it's in the hands of thousands of our core developers.

And we need to make sure, and I know you asked this in the very beginning of the conversation, is the funding side of it. We need to make sure these pilots, we are able to fund not just the pilot, but also the scaling of it. And scaling requires tackling enterprise size constraints, and things like disaster recovery, change management. So we have different groups engaged in the change management side of it to make sure that these pilots are taken to a successful and evaluation stage.

Aligning AI Strategy with Business Objectives

Michael Krigsman: This is from Sonia M., who asks about: How do you align the AI strategy with the bank's overall business objectives and risk management frameworks? And specifically, can you talk about the governance structures you've established to oversee AI development and deployment?

Prashant Mehrotra: When I joined and I started to set up the AI strategy, me and the team started with the business objectives and the business strategy, because AI by itself or AI strategy by itself doesn't occur in a vacuum. Now, in some cases, these transformative capabilities or AI can actually certainly influence it, but our North Star is our client, are our customers, and it's the business strategy that needs to be enabled by AI strategy. So we have never, I don't get into the situation where AI strategy is not aligned. So that's one thing I want to call out.

Secondly, in the governance structure, very early on in the process, U.S. Bank, I would say, when it comes to risk and governance, we have a very good posture and a very robust posture when it comes to risk and governance both. And what we do here is we make sure that we are creating both something that's transparent up to the leadership, but also down below.

So we have a governance, in terms of regularly reporting to our board, to our management committee, but also to my day-to-day partners, who are not just showing them the capabilities or the use cases that we are tackling, but also the platform we are building, so that ultimately, for a well-managed and well-run company using AI, this capability has to be in the hands of every single builder, creator, developer, and user, and for that, we need to make sure that they are educated, there's a good platform, and the governance is a feature of the platform rather than an appendage.

Building an AI-Ready Workforce: Skills and Education

Michael Krigsman: You're raising a very important set of issues around AI culture and what that means, and we actually have 2 questions on LinkedIn that relate to this. So let me combine these. And this is from Michelle Alman and Maya Cunningham. And Michelle asks: "What are the minimum skills every leader and risk partner should have for AI-enabled work in 2026?" And Maya asks: "What does AI education and enablement look like for employees at U.S. Bank?"

And let me roll both of these up into this broader question of the culture transformation that's required in order to achieve the kind of broad AI literacy you're describing.

Prashant Mehrotra: The advancements that are happening, the baseline keeps on improving and going up, and it's upon all of us to make sure that we are finding the right resources and working with the teams across the board to educate ourselves.

One of the things that we are doing at the bank is we are making sure that we are rolling out learning programs for our entire workforce. It's not just the leaders themselves, it's all the way from the top to the frontline associates. And we are doing it based on a persona and the roles they are playing in leveraging, building, strategizing, and influencing the direction of the company. So all of these resources have to make sure that they are thinking of, they are well-educated and aware of AI capabilities for us to be an AI-first bank.

And I have great partnership with our learning and development and human resources and risk teams in making sure that these resources are not just simply rolled out once, but they are continuously updated. And we connect these resources to the tools that we are rolling out and to the roles that they are playing in part of the company. So no matter where you are, we have something to help you adopt AI or build using AI or strategize using AI better.

Michael Krigsman: It sounds like enabling this, I call it AI culture shift, is a very important part of your role as the chief AI officer at U.S. Bank. I'm not trying to put words in your mouth.

Prashant Mehrotra: It absolutely is. The way I actually would say is it's not the job of one chief AI officer or one small group to be able to roll this out. It's all of us are in this together. It's a transformative capability, and we need to make sure that we are all in it, well-educated, well-prepared for the future to help our clients.

How AI Differs from Traditional Technology Rollouts Like ERP

Michael Krigsman: For those of us who have been around for a while and have observed things like ERP rollouts, the language in some ways is similar. If you're rolling out an ERP, really, it's a transformational change to the organization. But AI is different. So can you talk about the unique aspects or unique issues that AI drives?

Prashant Mehrotra: There are 2 things that I would say. Number one is, for the first time, we are able to use the quality, the quantity, the scale, and the speed of data and make that decision. We are able to push that decision to the front line faster than ever before.

Number two, the problems we are facing are coming, are compounding or being able to meet that at the scale, unlike anything we have done before. When we think about ERP is a great example. When we think about using ERP, you are using it in pockets. Different groups are using it slightly differently, and not everyone is using it, right?

When you think about AI, and I know we use the term AI very broadly, but no matter what your pane of glass is, no matter what data you are using, you are able to bring that intelligence or augment your human intelligence with this AI capability better and being able to do at a scale that has never been done before.

And we are, I know it seems cliché to say we are in the beginning stages of it. We talk a lot about agentic AI. We talk a lot about autonomous decision-making. We can do those things, and if we don't actually get our workforce educated and trained and ready for it now, we'll be very much behind later. And I don't mean to say we as just the U.S. Bank, but we, as everyday developers and users of these technologies and systems.

Michael Krigsman: It's a really interesting point that now is the time to prepare the ground for the technologies that we know will be here in the next year or 2. It's a really important point that you're making, it seems to me.

Prashant Mehrotra: Absolutely.

Regulatory Compliance in AI-Driven Customer Outreach

Michael Krigsman: This is a really interesting question from Connor Whalen, and he says: "As you scale AI-driven outreach, how do you make sure that automated customer contact remains aligned with evolving consumer contact regulations?"

Prashant Mehrotra: There are 2 portions to it. As we actually scale the contact, as we scale the use of AI, both internally and externally, we have guardrails in place. We have human-in-the-loop in the place, which I would say is our driving principle right now. And we are making sure that we are evaluating and reviewing those contacts. So that's one.

Number 2, we work with our both our regulatory partners, our internal risk partners, and our vendors to make sure that these systems and these capabilities, we are always at the forefront of bringing in the latest and greatest, not just technology, but also adhering to the compliance and regulations to. And that it is in line with our risk posture and with our clients' expectations of how we interact with them.

Final point I'll bring on this one is even before we roll out any capabilities, we want to make sure that it is done in such a fashion that we are mindful of our customers' expectations and of their perception of us.

Michael Krigsman: Your reference point, again, continually seems to be: How does what we're doing with AI affect our customers? Is that an accurate perception on my part, understanding?

Prashant Mehrotra: Absolutely. Our clients are the center of everything we do.

Leadership Structure and Cross-Functional Collaboration

Michael Krigsman: We have another question from, this is now from Lisa Pratico, says: "What does the leadership organization structure look like, and is there a steering committee that includes you, your chief sustainability officer, if that role exists, and your CIO on strategy, assessments, and execution of, I'm assuming, AI projects or proposed projects?"

Prashant Mehrotra: We have a core group between the chief, I already mentioned, as chief AI officer, our chief data product officer, and the chief technology officer. We also work very closely, I also work very closely with our managing committee, and different teams across the bank.

We have regular touch bases, and they are, they're not just learning about, the 3 of us, we are not just learning about what we are doing, but we are strategizing, we are creating these plans, and we are understanding the impact of the decisions we are making and working and building those in the plans together.

AI by itself doesn't exist outside, as I said, and Michael, thanks again for pointing it out, is we talked about putting clients at the middle, at the center of it. It's our stakeholders, it's our shareholder, it's the community that we serve and that we live in. We need to make sure that we are bringing all of these things together, and our strategy and our vision serves that.

Data as the Foundation of AI

Michael Krigsman: Let's talk about data. We have a question from Arsalan Khan, who is asking about the value of data. Do you see data as an asset, and how do you think about the value of that data? And wouldn't different senior leaders place a different value on data? And so how do you manage that type of thinking?

Prashant Mehrotra: One of the unique things that I feel like U.S. Bank has done is we have put our AI group together with our chief data product officer, and our digital team. So our group is called Digital, Data, and AI. And in this role, and the second point, and I'll elaborate it a little in a minute, is AI without data, I know we heard a lot about hallucination in generative AI, but AI without data itself is a hallucination. So you really need to have a very good and trustworthy data foundation for us to be able to leverage AI. So it's essential. I cannot emphasize how foundational that is.

The other thing is, as I was mentioning about digital data and AI, is from building a very good foundation of data and being able to and interacting with our customers and clients and employees in a digital layer, AI is that intelligence layer that bounds it together and makes that interactions not just more efficient, it makes it really fast, it makes it much more holistic. And bringing these three groups together under one leader has really helped us accelerate rolling out these capabilities, increasing the number of pilots to production and scale, but also making sure that we are coming across to our customers, our clients, and our employees as one entity.

Michael Krigsman: I keep saying this, and I'm struck by, is the reference point for you on pretty much everything you're doing is what will the impact be on your clients? That's always the North Star, it sounds like.

Prashant Mehrotra: Absolutely. Client is our North Star. They're the center of everything, and I might have mentioned this before, is. And we also want to make sure that our various different stakeholders, like community, are part of that consideration. But clients are North Star and the center of everything we do.

Technology Platforms and Vendor Partnerships

Michael Krigsman: This is from Grace Pappas, who says, "A simple, quick question: Can you talk about the technologies or platforms that you use to drive these initiatives?"

Prashant Mehrotra: We already have a lot of technology and vendor partnership, we work with different hyperscalers. But when it comes to AI, we are bringing in new capabilities, and we are working with a lot of startups and entrepreneurs, who are thinking of making available or solving some niche problems that come together with AI. Guardrails is one example that we are partnering with a vendor partner of ours. I unfortunately won't be able to name names for us, but there are some unique ones, and we are also leveraging our pre-existing partnership across the board.

Build vs. Buy: Leveraging Foundation Models

Michael Krigsman: This is from Shawn Tumanov on LinkedIn, who asks: "As we shift from a build-from-scratch era to fine-tuning, leveraging open-source foundation models on proprietary data, how must a bank's organizational strategy evolve, specifically structural changes required to transition from model development to high-stakes model integration?"

Prashant Mehrotra: Build versus buy has always been a interesting conversation when it comes to any new technology. And there is not a single answer when it comes to build versus buy. It can be various different lines of business, your different use cases, your different internal customers, and employees who are using it. So there's a lot of art to it, in addition to the science about it.

What's really unique about generative AI is, for the first time, that we are able to leverage external models at scale without modifying them. And what we do is provide a unique perspective in terms of the user, the client, the prompts, and the data that we provided.

And over the past year and a half, at the bank and well before that, is I have worked with various different risk teams, various different audit and compliance teams, in making sure that we are evolving our process in evaluating and managing that risk. Because if you look back at November 2022 and before, 90% of the models that any organization used to use was really homegrown or in-built and had a high level of customization and control. When models like transcription and image analysis came by, those were the first time that we started to use external models at scale without modification. But the balance is now tilting in favor of models for hire more than models you build.

AI as Strategic Investment: Creating Durable Value

Michael Krigsman: Here's a question from Austin Dirksen on LinkedIn, who is an MBA student, a finance student, and he says this. He has a good question. From: "How should we think about AI as a strategic investment rather than just a technology cost? What signals tell you that an AI initiative is actually creating durable value for a bank like U.S. Bank?" It's a great question.

Prashant Mehrotra: We are measuring the value at every step of the way. We are understanding the applicability and the reach of the AI capabilities that we are building, adopting, or rolling out, and we ensure that it aligns with overall with our risk posture, and it's something that we want to add to the capabilities that we offer to our clients.

Prioritizing Use Cases for Testing

Michael Krigsman: This is from Eric Voge, who says: "When you're scaling AI use cases, how do you prioritize use cases to start testing, that to ensure that they align with your business objectives, feasibility, and readiness requirements?" So how do you prioritize when it's time to test?

Prashant Mehrotra: In terms of prioritization, we work and depend a lot on our business partners and business leaders on what makes, what is important for them and their customers. In terms of when we are ready to test, I think it's more. And maybe I'm just not getting the question right. It's more of a question of what capabilities that we can build or roll out or adopt fast, and when is our user community who's able to use that is ready? Because change, when it comes to AI, a educated workforce and a change-ready workforce is really important.

Personalization Without Being Invasive

Michael Krigsman: U.S. Bank has significant insight into client behavior. How do you translate that insight into AI experiences that customers will find valuable, but without feeling invasive or crossing that line into creepiness?

Prashant Mehrotra: Many of us have actually bought homes, cars, or any other capability, any other asset over a period of time. How would it feel like if your bank, who you borrow for your mortgage, sends you a marketing material or an offer 3 months after you have closed, borrowed from them? And tries to sell you a better mortgage. And it's like, you know me, I've banked at my banks for decades and decades. Why are you not offering me a more intelligent service?

Our expectations as a consumer have evolved over time, where we almost expect with these institutions that we interact with to know us better and interact with us in a smart, intelligent, and knowledgeable way. And so one of the things we try to do is make things more contextual, take into account the right information that we are aware of and have of our customers, and make sure whatever we are doing with them feels relevant to them.

Michael Krigsman: It's a very fine line between using data to give the customer an intuitive experience versus making them uncomfortable because you know too much.

Prashant Mehrotra: I would agree with that, and that's why we also have a responsible AI principles that we established at the outset of the program. And as I said before, let's make sure that those interactions are relevant, timely, and we need to contextualize that based on a customer's expectation of us.

Conclusion

Michael Krigsman: We are out of time. Prashant Mehrotra, U.S. Bank's Chief AI Officer, thank you so much for taking your time to be with us today. I'm very grateful to you, Prashant.

Prashant Mehrotra: Thank you.

Michael Krigsman: And everybody, thank you for watching, and thank you for your awesome questions. Before you go, check out cxotalk.com and subscribe to our newsletter so we can keep you up to date, and you can join us for upcoming episodes, which we have great ones. Thanks so much, everybody. We'll see you soon. Have a great day. Bye-bye.