# AI Agents in Finance with HPE's Chief Financial Officer (CFO) | CXOTalk 914

- **Show:** CXOTalk
- **Published:** 10 Apr 2026
- **Source:** https://podcasters.spotify.com/pod/show/cxotalk/episodes/AI-Agents-in-Finance-with-HPEs-Chief-Financial-Officer-CFO--CXOTalk-914-e3hnqir
- **Transcript:** https://podcasts.opennash.com/shows/cxotalk/ai-agents-in-finance-with-hpes-chief-financial-officer-cfo-cxotalk-914-5a5982/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

Marie Myers, HPE's CFO, argues that the value of AI in finance has to be judged on more than hard savings. She separates direct ROI from indirect gains such as speed, accuracy and fewer errors. She says finance AI only works if answers are consistent, data is clean and reconciled, and a human stays accountable for the result.

The main tension is between moving fast and staying trustworthy. HPE experiments in small, agile steps and will stop investments that do not pay off. Yet finance cannot tolerate wrong numbers, so it built a consistent-answer platform, kept data on private infrastructure and kept humans in the loop. Myers also says that changing people's habits is harder than the technology.

## Key insights

1. **Indirect AI value can outweigh direct ROI** Myers counts speed, fewer invoice errors and lower fraud as indirect benefits that can be quantified. She says they can end up bigger than the initial hard savings.
2. **Finance AI had to give identical answers** A question such as yesterday's shipped revenue must return the same number for everyone, so HPE co-engineered with Nvidia on a NIM (a packaged AI model service). She calls getting this right the hardest part of building Alfred.
3. **Stage gates let HPE kill weak AI projects** Projects are checked against a scorecard at set points. Some experiments failed, were stopped, and the money was moved elsewhere, and Myers says the investments stayed small enough to manage.
4. **Fix workflows and data before adding agents** HPE centralized its FP&A team and standardized its work, building on an existing data platform, before layering on agents. The goal was one source of truth.
5. **Change management is the hardest part** Myers says the human side of change gets too little attention. She and her team trained more than 3,000 people, and she suggests many failed pilots skipped workflow redesign.

## Questions this episode answers

### How does a CFO measure the return on AI when the benefits are not all savings?

Myers splits value into direct returns, which are easy to put numbers on, and indirect ones such as speed, accuracy, fewer errors and reduced fraud. She says the indirect bucket can be quantified and can end up larger.

### Why does it matter that AI be deterministic in finance?

Deterministic means the same question always gets the same answer. Myers says a figure like yesterday's shipped revenue cannot change depending on who asks, so HPE worked with Nvidia on a NIM to make Alfred consistent.

### What is Alfred at HPE?

Alfred is HPE's name for its finance AI platform, built on Deloitte's Zora platform. It started with accounts payable and was extended to the weekly operations call, and it runs on private on-premises infrastructure.

### Does AI make the decisions in HPE's finance team?

No. Myers says there is always a human in the loop, with AI used to gather, synthesize and point to issues while people judge and stay accountable. She says that may change in two or three years.

### What should a CFO do to get ready for AI?

Myers says a CFO needs technology understanding on top of financial skill, because boards are asking how AI is used and what it delivers. She recommends learning by doing and working with the CIO and business partners.

## Facts

- **Michael Krigsman** [0:00:14]: "where she's deploying AI agents across 3,600 person finance organization."
- **Marie Myers** [0:07:19]: "we collaborated with Deloitte on called Zora. We call it Alfred at HPE."
- **Marie Myers** [0:00:00]: "we actually co-engineered with Nvidia, uh, on a nim to help crack determinism, because you just can't get finance data wrong."
- **Marie Myers** [0:12:14]: "we have about half a million data elements in Alfred, and it it is just amazing to me."
- **Marie Myers** [0:11:05]: "private cloud AI architecture, which is basically on prem infrastructure to house the data"
- **Marie Myers** [0:10:14]: "So the ops call, which we have every Monday at about midday around this time, is probably one of the most important processes that finance in the company, you know, actually drives on a weekly basis."
- **Marie Myers** [0:10:14]: "we've called our platform inside of HPE Alfred, which is actually Batman's wingman, cause, you know, we sort of see Alfred as our wing person, frankly."
- **Marie Myers** [0:37:20]: "I'd say on Alfred, for example, I think we're on version sixty-five plus."
- **Marie Myers** [0:46:17]: "literally training more than 3,000 people in our organization."
- **Marie Myers** [0:33:49]: "we actually decided to pull all the FP&A team together under one central organization and then look end to end across their work."
- **Marie Myers** [0:04:14]: "I spent about a decade working in in areas like RPA, which is robotics, process automation and sort of ML, and then evolved into AI."
- **Marie Myers** [0:04:45]: "I went on actually to be the CFO of a fairly large, uh, RPA startup."
- **Marie Myers** [0:14:31]: "So I think software engineering, code development is is fast becoming probably the hottest area for the deployment of, uh, gener, you know, generative AI."
- **Marie Myers** [0:27:38]: "They actually have an Excel bot as an example, which helps you, you know, really rapidly, uh, enable and use Excel and and helps FP&A teams, frankly, get their analytical work done."
- **Marie Myers** [0:13:04]: "I'm an ex-auditor, so I have a fairly, how do you say, strong risk appetite, and and I'm perhaps a willing to be an early adopter of technologies"
- **Marie Myers** [0:17:24]: "I was back then a bit of a pivot table queen."
- **Michael Krigsman** [0:42:19]: "there was that one in particular from MIT that described, uh, pilots and how of the vast number of yes, failed pilots."
- **Marie Myers** [0:41:39]: "I worked with some professors and we worked on all these theories, and actually even wrote a book about it, Michael, on how to drive organizational change with with technologies like RPA and ML."
- **Marie Myers** [0:49:38]: "today, I sit on boards, and it's in the boardroom."
- **Marie Myers** [0:48:47]: "this weekend I'm hoping to go play with with open claw. I ordered myself a Mac Mini to sort of play at home, teach the kids."
- **Michael Krigsman** [0:08:40]: "our last episode was with Bill Briggs, Deloitte's chief technology officer, and he was telling us about your AI deployment."

## Transcript

**[0:00:00] Marie Myers:** Cracking the code on determinism was actually the hardest part of the journey that we embarked on. We actually co-engineered with Nvidia, uh, on a nim to help crack determinism, because you just can't get finance data wrong.

**[0:00:14] Michael Krigsman:** Every company says AI creates value, but few can prove it in the numbers. Marie Myers is chief financial officer of HPE, where she's deploying AI agents across 3,600 person finance organization. Marie, how do you assess the value that generative AI brings to a business?

**[0:00:37] Marie Myers:** As a CFO, you know, I spent my life and my career really assessing value. When you look at AI, it is a little bit different, Michael, I've gotta say. I think you you have to look at it through a much more broader aperture and a much broader lens. So I tend to look at a set of discrete and sort of more subjective variables. I tend to break it up into what are the direct impacts that I expect AI to have, but then what are those indirect ones as well?

**[0:01:01] Marie Myers:** And sometimes what you find, Michael, is the indirect may sort of actually be much greater than some of those initial direct ROI benefits. So you have to step back and take a much broader sort of thought process around how you think about ROI on AI.

**[0:01:18] Michael Krigsman:** Can you elaborate on that? Because the the value of AI doesn't necessarily show up in the hard numbers.

**[0:01:28] Marie Myers:** First of all, I'd say is ultimately the objective is that you will see a return on your investment. So I just want to start there. You know, you do want to see, you know, what you spend, you're going to get a discernible, you know, sort of outcome from that investment that you make. But I I think what I've tended to do, and I've, I've done this over the years with a lot of automation and sort of early projects I was involved in.

**[0:01:50] Marie Myers:** You know, I. We looked very discreetly at the direct, which is relatively easy to put hard numbers around. Some of the the areas that are a little bit more difficult to really put the hard numbers around are things like speed. Like, AI has a huge impact on the speed and just the efficiency of how you actually get work done. So you have to find a way to sort of say, okay, if I'm going to introduce AI or agentic AI into my organization, what benefit am I going to get in terms of that sort of overall process?

**[0:02:21] Marie Myers:** Can I do my work faster? So I would use that as one example of where, you know, you'd look at speed and try to assess what impact would that have quantitatively or qualitatively on your organization? Other areas that, you know, look, I think about, particularly as a finance professional, are areas just like accuracy and errors, right? You know, over the course of many, many years, a lot of finance work has been really transactional. Humans have done a ton of fat fingering on invoicing, and let's face it, we've always had error rates.

**[0:02:52] Marie Myers:** So can AI help you reduce those error rates? can AI help you reduce fraud? You know, all of that. I put a little bit more in the indirect bucket, but you can put numbers around that stuff, Michael.

**[0:03:03] Michael Krigsman:** So you're looking both at the hard, hard direct numbers, as well as a set, a br- a relatively broad set of indirect, as you just to use your term.

**[0:03:18] Marie Myers:** Absolutely, and you know, what we do is we actually sort of define that indirect bucket so that we can help to. As we see initiatives and projects come up and we're gonna have to take some place, some bets. We can use that framework to assess how that project really plays out. And sometimes, look, you know, sometimes some projects are going to be, they're gonna have a better scorecard. And and others, you may get into it and find out, hey, what I thought I was gonna achieve, I'm not going to.

**[0:03:44] Marie Myers:** So you wanna know that too, at certain stage gates along the way, so that you can decide whether to keep going or actually to stop the investment. Cause, I mean, there are going to be some false starts and and this type of work as well.

**[0:03:57] Michael Krigsman:** To what extent is your evaluation of the AI value the same or different from traditional, more well-known technologies, whether it's ERP or CRM or or other areas that are just more well established?

**[0:04:14] Marie Myers:** I spent about a decade working in in areas like RPA, which is robotics, process automation and sort of ML, and then evolved into AI. This framework really came from when I first sort of started out my career as a global controller, and then as a CFO, where I started working on implementing, uh, bots into a lot of the transactional side of finance. And what we needed was a way to assess how, you know, the expense of the licenses, frankly, and the impact that they were having on the organization.

**[0:04:45] Marie Myers:** So we came up with this framework, and then I went on actually to be the CFO of a fairly large, uh, RPA startup. And it became clear that the industry needed a framework to really help, you know, CFOs like myself get comfortable with these investments. So use that framework over the course and evolved it over the course of about of a decade. So it wasn't something I picked up in a textbook and sort of ran with Michael.

**[0:05:08] Marie Myers:** It was really something that evolved through RPA. Now I've, I've been on a number of ERPs, and I would say that when we've done, you know, ERP investments, it's not that much, it's not that dissimilar. I mean, you know, what we do with these gigantic capital investments that you make on the IT front, you'll always have, you know, a very prescriptive sort of ROI bet that you make right up front. Which may include, you know, I'm going to have obviously a much higher productivity rate, I'll see these processes get better.

**[0:05:39] Marie Myers:** But sometimes on those big initiatives, it's really hard to see the benefits. Cause they're over. They're multi-year journeys, they're not necessarily journeys that are very short and agile. So I think that's the difference. Some of these IT programs take years to see these productivity benefits, sort of, you know, really play out. Whereas with AI, you could see those productivity benefits and those indirect benefits that I mentioned much faster and much sooner. So that's why I've always been a fan of RPA, and as it evolved now into agentic AI, you can see benefits on a much more rapid, rapid pace.

**[0:06:15] Michael Krigsman:** That's really interesting. So it also has implications for the IT organization and the nature of the value that IT delivers back to the organization. Even though there's more, uh, can we say uncertainty with generative AI? Because it's not, it's probabilistic, it's not deterministic. So at the end, you don't really have the certainty of where it's gonna end up in the way that you would say, implementing a CRM system.

**[0:06:49] Marie Myers:** This is a team sport. You need everybody at the table here. You need to have the business leaders with the business, the with the acumen around the process. You need your IT team, they are the folks who are typically the custodians of a lot of the data. You need your compliance teams. You need everybody sort of playing their role and being part of a successful, you know, AI sort of driven initiative in an organization. Uh, so that that that's sort of how I thought about it, which is much more of a collaborative engagement model.

**[0:07:19] Marie Myers:** That's where this works best. And then to the second part of your question, which is about determinism. It's a subject near and dear to my heart because, uh, myself and a lot of my team have worked on a platform that we collaborated with Deloitte on called Zora. We call it Alfred at HPE. And one of the problems we had to tackle and solve right up front was exactly the issue you raised around determinism. Because as a finance professional, you know, if I was to embark on a journey where we would use AI, yeah, in a broad enterprise capacity, it's simply not good enough to have a deterministic answer.

**[0:07:55] Marie Myers:** Which, as you know, means I can't, you know, sort of use a model, ask it a question, and there's no guarantees that every time I ask that question, which is, hey, what was the revenue, uh, that the company shipped yesterday? That number is different. That number needs to be correct, irrespective of who asked the model, where in the world it it needs to be. You know, it, it cannot be probabilistic, it needs to be deterministic. So for us, going through that journey of really calibrating, uh, you know, the right answer from the model every single time was really important.

**[0:08:26] Marie Myers:** We actually had to work with Nvidia on their Nims to really move from probabilism into determinism, which was a very important journey for us. And we we sort of found that as being literally foundational to the work that we've done on AI.

**[0:08:40] Michael Krigsman:** We have some questions that are coming in, and if you're watching on LinkedIn, pop your question into the LinkedIn chat. If you're watching on Twitter X, use the hashtag CXO talk. And I urge you to ask questions because gen- truly, when else will you have the opportunity to ask Marie? Myers, the chief financial officer of HPE? You know, pretty much whatever you want. So ask your questions, take advantage of this. Marie, you mentioned Deloitte, uh, just coincidentally, our last episode was with Bill Briggs, Deloitte's chief technology officer, and he was telling us about your AI deployment.

**[0:09:28] Michael Krigsman:** But give us a sense of the the scope of that deployment, give us some context. And then we have questions that are coming in and we'll, we'll jump over to those.

**[0:09:38] Marie Myers:** We formed a great partnership, I would say, with Deloitte on this journey. We decided to use the Zora platform, which Deloitte has really been at the sort of forefront of developing, uh, to help us, you know, start from what I would call the transactional side of finance. So we looked at how we could implement Zora's capabilities and really drive efficiency, uh, in speed in areas like accounts payable. And then we sort of took on a challenge to take that platform and really see how we could scale it into a core enterprise process, which was our operations call.

**[0:10:14] Marie Myers:** And through that journey, we've called our platform inside of HPE Alfred, which is actually Batman's wingman, cause, you know, we sort of see Alfred as our wing person, frankly. So the ops call, which we have every Monday at about midday around this time, is probably one of the most important processes that finance in the company, you know, actually drives on a weekly basis. And cracking the code on what was a very laborious, very sort of old school powerpoint, you know, revision process.

**[0:10:42] Marie Myers:** And really taking agentic AI capabilities and using that platform was one of the the key objectives we had over the last twelve months. And what was different about that journey and and why we collaborated with Deloitte is one, I wanted to work with somebody that I knew, uh, could be a trusted partner that took compliance and governance really seriously. And secondly, we partnered with Deloitte on on an on prem solution. I'm sure we'll talk about this.

**[0:11:05] Marie Myers:** We actually decided, in order to really retain the security and the compliance we wanted, we use what we call private cloud AI architecture, which is basically on prem infrastructure to house the data.

**[0:11:16] Michael Krigsman:** We have an interesting question from Twitter. This is from Chris Peterson, who says, How do you deal with the non-determinism and lack of explainability when you apply gen AI in a financial context? I think this is the question that we we all wanna know.

**[0:11:36] Marie Myers:** Cracking the code on determinism was actually the hardest part of the journey that we embarked on in really calibrating Alfred, the platform, and it took time. And in fact, I would say we actually co-engineered with Nvidia, uh, on a nim to help crack determinism, because you just can't get finance data wrong. It like, as a CFO, I need to be. Before we could actually even trust Alfred, we needed to be sure that if we engaged in this agentic AI journey, that the prompts that we were going to use were going to generate the right outcome and the same outcome.

**[0:12:14] Marie Myers:** And if every time. So, uh, we have about half a million data elements in Alfred, and it it is just amazing to me. I'm in Alfred almost every day now, as we're getting ready to go live across our company testing it to see, do I get the same answer every time? And I'm pleased to say so far, the answer is yes, Michael.

**[0:12:36] Michael Krigsman:** You have moved from using AI as an advisor into executing real financial work as you expand into areas like accounts payable, credit collections. Where do you allow the AI to act independently, and where must humans people stay in control? This is such a fundamental agentic AI issue.

**[0:13:04] Marie Myers:** I think you kinda hit it the core of the the evolution of AI, and specifically agentic AI. I'd I'd start out by saying we're still in the really early innings of the maturation of gen AI and agentic AI in inside enterprises. You know, I'm an ex-auditor, so I have a fairly, how do you say, strong risk appetite, and and I'm perhaps a willing to be an early adopter of technologies, but to do so in a very thoughtful, risk-averse way.

**[0:13:36] Marie Myers:** So for me, the mantra has always been human in the loop. So we use AI to help us, you know, really drive faster outcomes. But there is a critical sort of well-understood role for the human that we document as part of the process. So there's always a human in the loop in how we're deploying AI. So that's like I said to you, I think we're at the early innings and the early stage of this evolution. I'm not to say how things are going to look, you know, maybe two, three years from now.

**[0:14:07] Marie Myers:** But for now, you know, having that human engagement and the human involvement is is super important, Michael.

**[0:14:13] Michael Krigsman:** We have a and really another interesting question this time from Nick Koreshi on LinkedIn, and Nick says, What are the top three use cases with the highest ROI through AI, in your expert opinion?

**[0:14:31] Marie Myers:** At an enterprise level, I think, look, the the top use cases that are clear out there today, and I don't think there's any sort of dispute that these areas are widely acknowledged as having fairly extensive AI applied to them today. The first one is a lot of the software engineering. Uh, I've talked to a lot of board members who see pretty impressive productivity gains, uh, across different types of software engineering. So I think software engineering, code development is is fast becoming probably the hottest area for the deployment of, uh, gener, you know, generative AI.

**[0:15:06] Marie Myers:** The other one is marketing, so a lot of image creation, uh, a lot of campaigns these days. Uh, you know, you're really seeing this sort of rapid expansion of generative AI in that space. And then the last one I'd say is IT itself, I mean, just to help IT teams process a lot of the sort of workflows in IT. So those are sort of the three top enterprise use cases. And when it comes to finance, I would say it's still from an ROI perspective at the transactional layer.

**[0:15:32] Marie Myers:** So wherever it's like the early days of RPA, you know, a lot of it was all those manual invoices we used to get. A lot of that stuff we had the bots processing. You know, a lot of that area today is where still you can see AI is having, you know, greater impacts than perhaps, you know, a lot of the RP. As so transactional finance, wherever there's a lot of routine sort of repetitive tasks, is where you're seeing AI really have an impact in finance.

**[0:15:58] Marie Myers:** And then it's gonna scale, Michael, as you sort of nail a lot of those workflows and you get into more complex areas like forecasting. Uh, you, you'll start to see those applications broaden out.

**[0:16:09] Michael Krigsman:** Can you dive a little bit into the types of applications? Uh, because I know these are AI agents, and so maybe give us some insight into what kinds of applica- AI applications these are.

**[0:16:23] Marie Myers:** The areas that, you know, for finance that are critically important for AI, you know, I, I would say, starting in the transactional world. And and honestly, when I was a global controller, I had spent a lot of time with, uh, deploying RPA in these areas over a decade ago. And they're exactly the same areas, coincidentally, where you're seeing generative AI really lean in and take, uh, a very sort of have a high, high impact. And I would say it was accounts payable, a lot of the credit and collection areas, and then and and a lot of the traditional accounting areas.

**[0:16:55] Marie Myers:** Because this is a lot of sort of contractual analysis that needs to go on that becomes, frankly, you know, a a as a ex-analyst, I used to do some of this work myself. It was taking a contract, going through the terms and just trying to apply it to an accounting standard. You can have the AI do a lot of that, that reconciliation today, all the way into internal audit, whether it's controls checking. So a lot of that repetitive work that you saw financial professionals having to do in the past, uh, can can frankly be done pretty easily by AI today.

**[0:17:24] Marie Myers:** And what it does do, it allows, you know, a lot of people to free up time to actually do the analysis that you just didn't have time to do. If I look back on my first job, Michael, I mean, I love to tell this story. I was a, you know, I was an analyst in a tech company. It was my first job out of graduate school, and I consider myself. I was back then a bit of a pivot table queen.

**[0:17:44] Marie Myers:** I used to be able to take tons of data and build, you know, amazing sort of analytical mind, you know, analytical pivot tables around that data today. And that used to take me hours and hours. I remember as a young girl, I'd be up to ten o'clock at night, sort of building these pivot tables. I didn't have time to spend on the analysis to really determine what was happening with all this data. That would take me well past midnight into the early hours of the morning to do.

**[0:18:10] Marie Myers:** I like to think that today, because of AI, the AI can run a lot of preliminary analytics on all that data for you today. So you don't need to do what I did back thirty plus years ago. And I think as a professional, that's a better use of my time to spend my time analyzing those results and really interpreting the information, as opposed to sort of pulling all that data together to even sort of start on the analysis.

**[0:18:35] Michael Krigsman:** And then as a follow-up, we have a an excellent question from LinkedIn, and this is from Katarina Collins Sarah, who is herself a global CFO. And she asks this, Have you seen any degradation in decision quality when teams rely too heavily on AI outputs, and how are you mitigating that?

**[0:19:00] Marie Myers:** There's been some sort of commentary out there in the market where folks call it call it, call it AI slop, uh, which is where people get kinda lazy. Let let's face it, let's put away the AI slop, but let's just get into work slop. I mean, it happens in work, people get, you know, they get comfortable when they do the same thing over and over again. And this look with AI, people can use it as a sort of easy way to get work done.

**[0:19:24] Marie Myers:** So your standards can't drop, Cater, you know, so that's what I'd say. I I see it with my own teenage kids, you know, they try to use the AI to get the homework done, and they come and show me the homework. I'm like, come on, guys, you're not even answering the question that the essay was supposed to be written on. So you have to, you have to keep the standards, irrespective of whether you're using AI, you're not using AI.

**[0:19:43] Marie Myers:** You can't let, you know, AI become an excuse for low-quality work. And I think in the e- early initial days, there certainly was a fair bit of that going on. People were like, Hey, I'm just gonna use the AI to write a memo, and if the memo is not really like, you know, we have to prepare memos for a lot on on to sort of summarize a lot of our analytical packs and and my staff were using them.

**[0:20:06] Marie Myers:** And I'd read the memo, I'm like, guys, this memo, really, it doesn't even sort of summarize what's in the deck. You need to really read the the quality of the AI memo to see if it's it's any good. So that's just part of look, forcing your own folks not to become too lazy with AI. You know, I think it's very powerful on data, I think on the memo, writing and and some of that where I see folks, you know, they, they kinda can get a little lazy sometimes.

**[0:20:31] Michael Krigsman:** She has a follow-up question, and I I I love the questions from the audience. You guys in the audience are so awesome. Let me mention this would be an excellent time to subscribe to the CXO talk newsletter. So go to CXO talk dot com and subscribe so we can notify you about upcoming shows just like this. Cause you guys in the audience, we love your questions and you should attend and join us. Uh, okay, so the follow-up from Katarina is also, how do you balance speed from AI with the need for human judgment and skepticism in financial decision-making?

**[0:21:14] Marie Myers:** You need to have that balance. Let's go back to what I said earlier about there has to be a role. I call it, you know, what I've called it is human in the loop. There needs to be a piece of the equation where the human is involved in, uh, evaluating some of the AI output and, uh, applying some of that judgment. Because what I would say, the AI today, I I see it much more, uh, in an augmentation stage, as opposed to sort of replacing a lot of the human judgment.

**[0:21:44] Marie Myers:** Uh, I'll give you an example with with Alfred, where we were, you know, preparing to use Alfred for all of our operational calls. What I do like is that it will analyze all the data on the performance of the business over the last week. And then it will be very prescriptive in giving me targeted areas of insight that require the next level of of judgment. So I deliberately and consciously use the AI to help narrow the lens on issues in the company.

**[0:22:13] Marie Myers:** And then I go back to my controllers, I go back to my CFOs and ask them to go and dig in and validate what really is going on. So in the past, I would have felt like I would have had to go looking for a needle in a haystack to figure out what the issue was. Today, I can be much more surgical and pointed and say, look, guys, I see these three things coming through the data.

**[0:22:31] Marie Myers:** Now, please go and give me the insight or give me the plans on what you're gonna do to go fix those issues. So I'm not relying today on the AI to replace and solving the issue, I'm relying on the AI to provide much deeper focus on the issues we need to go solve. And then bringing the human, the benefit of human judgment to help solve the issues. And I think that's the piece that I'm still somewhat skeptical about AI to actually take all those years of experience and all that judgment and sort of, you know, replace me, frankly.

**[0:23:02] Michael Krigsman:** Would it be accurate to say that you're using the AI to gather information, data and analysis to present to you, which you then, or someone on your on your team then analyzes or applies human judgment to? Is that

**[0:23:24] Marie Myers:** Yeah, I would say takes you, synthesize and really provide, you know, clarity on what the issues are, and then the decision on what to do is being driven by the human today. I mean, like I said, Michael, I think over time, you know, that will continue to evolve. And we're talking about a much more complex workflow than just sort of invoice processing, you know, contract analysis, which I call sort of transactional workflows. I'm I'm m moving up the stack in terms of more complex workflows.

**[0:23:54] Michael Krigsman:** I use various AI tools every day? So, for example, preparing for our discussion, and it goes out and can research hundreds and hundreds and hundreds of sources. But at the end, when it comes down to our conversation, I have to check every fact, every, every stat or fact it comes back with before I include it in a conversation like this. I verify it myself.

**[0:24:26] Marie Myers:** I think that's a great example, Michael. You're doing exactly what I would think what we do, which is you're the human in the loop. Like, you're using it to gather, you're using it to synthesize. But when it comes down to that final judgment call, you're making that. A And that's what I meant when I go back to my first job, I mean, all that work, just gathering the data, synthesizing the data. My question for you is, how long did it take you to do that without AI?

**[0:24:51] Michael Krigsman:** For me, the AI is drives time, efficiency, and also breadth, and therefore quality.

**[0:24:59] Marie Myers:** Yeah, exactly. So I think it's another great example of, you know, where the power is at today. Now, I think what we've got to see is how it scales over time. And that last mile of the judgment call that you mentioned that you're doing, I'm doing the same thing today in my office.

**[0:25:14] Michael Krigsman:** This question is from AJ Sharma on LinkedIn, and he says, Marie, how do you ensure the agent is acting on trusted, reconciled financial data, not fragmented or or stale sources across systems?

**[0:25:32] Marie Myers:** The most important part of any AI journey is data. Data quality, data cleanliness is the number one issue that I think holds back a lot of companies and organizations and really embarking on AI journeys. Without clean, good data, you're really, it's impossible, frankly, almost, Michael, to move forward. So what we did at HPE, for example, is that, you know, we worked, we spent, you know, countless amount of time really ensuring that we had the right data layer in place with the right quality of data.

**[0:26:07] Marie Myers:** So we were fortunate enough that we did have inside the company. A, you know, a sort of data platform that had been largely architected and, you know, credibly built over a period of time. And that was the foundational layer that we actually used for the work that we did around Zora and Alfred. So without that, though, you you couldn't even get, you know, on the monopoly board, you couldn't even begin to pass go, Michael.

**[0:26:31] Michael Krigsman:** We have a question from Elizabeth Shaw on Twitter, who says, What are the, uh, uses and potential uses specifically of AI agents and agentic AI for FP&A financial planning and analysis? And she's also interested in the kind of autonomy that you allow the agents to maintain.

**[0:26:58] Marie Myers:** For FP&A, certainly, my team, we we made a conscious effort to actually first embark on a workflow transformation inside of FP&A to allow that team then to start to build and use agents. I think in FP&A, there is just an enormous amount of opportunity on a lot of that basic block and tackling on data that we talked about earlier. So whether it's being able to build analytical views, you've got so many sort of easily, uh, usable agents inside products, even like Microsoft Copilot today, for example, they have agentic workflows that are already there that FP&A teams can easily use with confidence.

**[0:27:38] Marie Myers:** They actually have an Excel bot as an example, which helps you, you know, really rapidly, uh, enable and use Excel and and helps FP&A teams, frankly, get their analytical work done. So a lot of progress made in whether you're a Microsoft customer. And there are other sort of LLMs out there as well that have all that sort of really pre-configured agents out there that you can use. So I'd say FP&A is a great customer for agentic AI.

**[0:28:04] Michael Krigsman:** Marie, there was an article in Fortune that said, You are positioning the chief financial officer role as a steward of AI across the enterprise. When it comes to AI, how should a CFO work with other leaders, such as the chief AI officer or CIO?

**[0:28:27] Marie Myers:** It's a team sport. I think, like we spoke about earlier, Michael, there is a really important role for, you know, a lot of key positions in the company, whether it's the IT partnership, that's essential, the link back with the business, that's essential. I would actually say the link with the controls and the audit, absolutely essential as the company embarks on really the early days of AI and governance and controls. But where I think the CFO has a unique sort of perspective is on the ROI conversation, which is actually where we started the call.

**[0:29:02] Marie Myers:** Because, you know, if you think about it, the CFO is really the sort of the stew of capital of the organization. So you can help the enterprise and the company really navigate the decisions and the investments in AI, and then help them to evaluate those outcomes as well. Because I think this is an area that if you look at a lot of the literature that's been written about AI, it's perhaps the one area that most folks have struggled to really build a clear framework around.

**[0:29:32] Marie Myers:** So I think there is a great role for the CFO to be able to be the steward of capital and help to really navigate ROI, you know, projects for AI.

**[0:29:42] Michael Krigsman:** Is your relationship with other team members, other leaders at HPE different when it comes to generative AI and agentic AI than with other areas of technology or or other areas in general?

**[0:30:00] Marie Myers:** In some ways, it probably is, Michael, because, you know, in my team today, I have a group that's actually in my strategy organization. My chief strategy officer helps to sort of drive a lot of the, uh, transformation initiatives in the company, and we obviously have transformation initiatives that the backbone is actually the deployment of generative AI. And these are examples of where the team is interfacing and working, you know, collaboratively with these organizations to help drive transformations where AI is the cornerstone of success.

**[0:30:33] Marie Myers:** And that's a little bit different than in the past, you know, I think in the past we would've, you know, perhaps helped those organizations, you know, through ERP implementations, et cetera. And you would've stepped back and sort of said, look, here is the here is the plan, you know, like the typical finance person, you know, I'll check off every quarter to see whether you've made progress against your plan and whether or not you've hit your milestones.

**[0:30:55] Marie Myers:** But what's different about AI is it's much more agile. So one of the initiatives we're working with our supply chain team, for example, involves really deploying generative AI in their customer, uh, organization, helping them really manage their customer service all around the world. And so as the team has been deploying generative AI, they've become very successful and they found new and different ideas that emerged, even sort of six months into the initiative. So in order for my team to really help them evaluate whether or not these investments are paying off, you have to be really close to that organization.

**[0:31:28] Marie Myers:** So you have to understand the work they're doing, uh, the success they're having, some of the obstacles they're hitting. Whereas in traditional sort of IT projects, you just wouldn't have had that level of agile engagement. So I think the agility of of deploying AI means that it's a different, in different engagement model than what we've seen in the past, say, with typical IT projects.

**[0:31:50] Michael Krigsman:** We do have quite a lot of CIOs who watch CXO talk, and I'm wondering, how does gen AI and everything we're talking about change the role of the CIO? And and obviously, you're looking at it through the lens of being a chief financial officer, but any thoughts on that? And also any advice for CIOs who are trying to navigate this difficult transition period from before AI to we don't really know where it's going exactly.

**[0:32:29] Marie Myers:** I think this is a great opportunity for CIOs to, you know, really become, really sort of have a pivotal role in the company. I think CIO, I always look at it as a super important role for the company because technology is the backbone and sort of is the foundational ingredient for, uh, a lot of companies. And so the role of the CIO, to me, has always been one that's essential and important. I just think today for CIOs, you know, they need to be, you know, right at the table a and have a really important seat around AI.

**[0:33:05] Marie Myers:** Because they are typically the custodians of a lot of the data, they understand the financial architecture, they are driving that financial architecture in the company. So they have to play a really important role in controls and governance. So the relationship between the CFO and the CIO, you know, kind of close out there and saying it's a really important one.

**[0:33:24] Michael Krigsman:** Katarina Collin Sarah has yet another question. She's asking great questions. She's a CFO and I I love she's bringing that insider knowledge. And she says, as AI reduces the need for data collection and consolidation, how are you redesigning the finance organization and redefining the core capabilities you expect from your team?

**[0:33:49] Marie Myers:** You know, as we sort of prepared for some of the the generative AI and agentic AI work we've been doing, we focused very intentionally on actually redesigning some of the critical processes in in the finance organization. And I'll I'll use FP&A because we talked about it a moment ago as one of those examples. So we actually decided to pull all the FP&A team together under one central organization and then look end to end across their work.

**[0:34:20] Marie Myers:** And try to drive a much more standardized approach to how we get the work done, so that then we could actually layer on top a lot of the agentic AI or generative AI capabilities. So we were deliberately, uh, let's just say deliberately went about the workflow first. We we didn't just apply the AI without sort of redesigning the operating model and then looking at the workflows themselves. And then figuring out where in that workflow could we then leverage the AI so that we would have a much more sort of one.

**[0:34:58] Marie Myers:** One thing I really think is super important as a CFO is you wanna have, you know, data one time. You want one time data, you want it to be the voice of the truth. You don't wanna have the multiple. A lot of companies struggle with. I've got a a version of a, you know, I've got a report that comes from one team that contradicts another team, so you want this one source of the truth. So for us, the sort of guiding mantle was one source of the truth.

**[0:35:20] Marie Myers:** A and that helped us drive this whole transformation that we're trying to drive with with the FP&A organization.

**[0:35:26] Michael Krigsman:** One of the things I find most interesting about this is you've got this technology generative AI that is driving change through many different parts of the organization. Uh, the tentacles are extending in in so many different directions.

**[0:35:48] Marie Myers:** Absolutely, you know, it is certainly areas like software, marketing, you you're starting to see agents, you know, working in all different sort of areas of enterprises. I think the real question is, how is this all going to work together? I I don't quite have a good, clear answer for you yet. Uh, that is something I personally need to spend time on, which is how does this sort of system work together in the future?

**[0:36:14] Michael Krigsman:** Let's talk about capital. AI demands enormous capital commitments in an environment where the technology evolves faster than the investment cycle. Models become obsolete in months, infrastructure costs are unpredictable, and returns often appear as speed and agility rather than hard savings. How do you navigate this AI investment minefield and place large consequen- consequential bets on a target that keeps shifting?

**[0:36:52] Marie Myers:** You really have to experiment, Michael. I mean, you cannot sit here and wait for the ultimate nirvana to emerge. Because, as I mentioned earlier, AI is iterative, so you kind of have to be prepared to learn as you go. It's a much more agile way of working. I, you know, worked in a comp, you know, worked over the decades. Many of us were using sort of very waterfall approaches to how we got work done, how we did, you know, IT, uh, sort of, you know, big IT, RPA implementations.

**[0:37:20] Marie Myers:** Today, with with AI is agile, you're gonna get better as you go. I'd say on Alfred, for example, I think we're on version sixty-five plus. So, you know, if we'd started this journey a year ago, we might have taken a very different approach to even how we're taking this today. So, but what was really important was the learning that the team went through and the expertise and the knowledge that they had today. If I'd lost that whole year, I'd had to start from ground zero today.

**[0:37:45] Marie Myers:** So I hear you, I, and there is so much change. But if you don't develop a sort of core set of skills and competencies and a learning capability, then you're gonna be just so far behind a year from now. So I I do believe it's important to jump in and learn and acknowledge that it's going to change and there's gonna be some throwaway work along the way.

**[0:38:07] Michael Krigsman:** Therefore, what is your tolerance for supporting AI initiatives before they before you expect that they deliver a measurable direct return?

**[0:38:20] Marie Myers:** I suppose I do acknowledge, I and I do that right up front. I acknowledge there's going to have to be a a an investment stage, uh, that we're gonna have to make, and that's why I mentioned earlier. You know, some of these investments you'll you'll make, and then you'll realize a few months in this is not gonna pay off. So you have to pull the plug and then redirect that capital elsewhere. So you have to be very vigilant in monitoring success and outcomes.

**[0:38:46] Marie Myers:** And I think that's that's why I'm go back to the same point about agility and the ability to be able to pivot and make different determinations. And, you know, we have, frankly, in finance, I'm just gonna talk very selfishly about the work I've done with my own team. I mean, we tried, we made some, we tried some experiments, some of them didn't work, Michael, so we pulled the plug and we went and made a different investment elsewhere.

**[0:39:09] Marie Myers:** So we have been there's not hasn't been a prescriptive roadmap out there. We probably went through some uncharted territory, and it was a mindset that I did have to make some investments, but those investments were not, uh, they weren't particularly sizable, they were manageable, I would say, in terms of my expense envelope.

**[0:39:28] Michael Krigsman:** Across the industry, major players are financing one another's growth. Chip chip makers are funding model firms, cloud providers are funding the startups that rent their compute. As a CFO, how do you determine whether the the demand you see is sustainable and based on real economic value or just financial maneuvering through this circular financing, as it's called?

**[0:40:01] Marie Myers:** I'd certainly say it's definitely been in the news lately, Michael. You know, as a company, as a CFO of HPE, we're really focused on customer value, and, you know, that's sort of the North Star and the lens that we really use around our business.

**[0:40:16] Michael Krigsman:** What about the implication for change and processes? You touched on that, but this is such a a core point. And also, uh, the culture and the implications of AI on culture change.

**[0:40:35] Marie Myers:** I believe that the whole area, uh, around management of change is probably the hardest part of AI, which is not, you know, with all that. We spend so much time sometimes on the technical side of of implementation. And the piece that doesn't get the attention it deserves is the human side of change. You know, let's face it, as humans, Michael, we're somewhat, you know, many of us are resistant to change. And so taking an organization through a journey like this, I gotta say, it's no easy feat.

**[0:41:07] Marie Myers:** And you've got to go into this with one, acknowledging that that is going to be part of the journey. And two, you have to be very deliberate and intentional from sort of day one around how to really drive the mindset change of your organization. So, you know, it's interesting, uh, there is more being written, I think, today, and and acknowledging that this cultural sort of empowerment from AI in an organization is really a big part of what's going to determine success.

**[0:41:39] Marie Myers:** And I'd almost argue, Michael, you go back and read some of those early case studies about there were so many AI pilots, and they all failed. What didn't get told, I think, was the whole story, which was, did those organizations actually really start intentionally from the beginning with a whole sort of approach around management of change in their organization? I would say I was very fortunate because when I did a lot of early days of RPA implementation, I worked with some professors and we worked on all these theories, and actually even wrote a book about it, Michael, on how to drive organizational change with with technologies like RPA and ML.

**[0:42:15] Marie Myers:** And I've used a lot of that learning today for for AI, frankly.

**[0:42:19] Michael Krigsman:** Those studies there, there was that one in particular from MIT that described, uh, pilots and how of the vast number of yes, failed pilots. But I think the other problem with that particular study is the nature of pilots is they are designed to learn and to experiment, and they will fail. I mean, I think you alluded to that yourself, and as a as CFO, it sounds like you're willing to accept a certain amount of that.

**[0:42:53] Marie Myers:** Hm, hm, I think you have to go into AI with that mindset from the beginning, that you're going to experiment, learn, and particularly because we're still in the early days of sort of the maturation of AI. There's going to be lessons learned that will have big payoffs, and then there's going to be some that will not materialize. But I would say that even starting on the journey, and maybe some of those pilots too, Michael, I'd say you, you would want to look back and see.

**[0:43:20] Marie Myers:** Maybe they failed because they didn't really work on how you were going to address workflows and process, because that part of the change is just as important as the technology itself. Like the the discussion we had about my FP&A team going through redesigning that workflow, centralizing the the actual work itself helped us then deploy agents much more precisely into the organization. So if you were just doing a pilot, you deployed an agent and nobody used it.

**[0:43:47] Marie Myers:** Well, yeah, you could say the pilot failed.

**[0:43:49] Michael Krigsman:** We have another question from AJ Sharma on LinkedIn. You can see I prioritize the questions that come in from the audience, and I encourage folks in the audience, there's still time, we have a few minutes left, so ask your questions. AJ Sharma says, To what extent have you entrusted regulatory interpretation to autonomous AI systems, and what governance mechanisms ensure those interpretations remain accurate and compliant across diverse jurisdictions? To me, this is the same core issue of H- how to what extent do you rely on the AI to give you crucial information that's accurate?

**[0:44:37] Marie Myers:** We help the AI in terms of we use the AI to help us speed up gathering the data or gathering the facts, and one area would like the, you know, tax, helping you collect a lot of the data that you need to make an out, uh, meet, to determine an outcome. And when it comes to sort of the judgmental side, the human is absolutely engaged in ensuring that they're reviewing the information, particularly regulatory. I have a sort of high bar on a lot of regulatory, uh, interpretation because that is a bit more gray.

**[0:45:06] Marie Myers:** And that is absolutely where, you know, we may use the AI to help us speed up the analysis, but the human is then obviously determining the final outcome.

**[0:45:15] Michael Krigsman:** So, at the end of the day, accountability remains with the responsible person.

**[0:45:22] Marie Myers:** Absolutely. I mean, you know, we're not at a point in this journey today where you can devolve accountability. And I I look at my role, I mean, as a CFO, I'm accountable for I'm the financial steward of the organization, so, you know, I'm accountable for those financial outcomes, Michael.

**[0:45:38] Michael Krigsman:** And we have a question from Swami via Jonathan, and Swami says, as you approach this initiative iteratively and have to start again in some cases, as you were just describing, are teams building organizational context, not just technology, but the organization? And that context must be valuable from an overall AI transformation journey. But how do you put a number to that from a financial standpoint, because the investment is truly valuable and you don't wanna throw it away?

**[0:46:17] Marie Myers:** Organizational learning agility, because what you're doing is actually learning through, you know, perhaps through failure. And that means that the next time you try to approach, uh, another implementation, you're probably gonna go a lot faster. So learning agility is super important. And actually, Michael, I mean, I'll be honest, I spent myself and my team spent a lot of time over the last couple of years, literally training more than 3,000 people in our organization. And we didn't sort of go out there, educate people once and then walk away and say, Hey, you're done.

**[0:46:49] Marie Myers:** No, it is a constant process because things are changing, as we talked about. So raising the bar on knowledge and sophistication of knowledge in your organization is like building, it's like building your house. You're just building that foundation and it's rock solid, so it means, you know, you're like an athlete, you're getting faster and faster each time you actually try to implement, uh, a different, you know, a different initiative or different project.

**[0:47:15] Michael Krigsman:** Marie, can you talk about, uh, the CFO role and how CFOs can and should, in your view, relate to all of these changes that are going on? Your background is highly technical and unique. You worked for an RPA company, but the average CFO doesn't have that benefit. So what should, what should a CFO do who's listening and knows they need to get up to speed with on AI, but they're not sure the best approach?

**[0:47:50] Marie Myers:** The role of the CFO is certainly evolving from, you know, what has been a very much, uh, one where we were the guardian of financials, the steward of capital, to now having a really important role in AI inside a company. So, you know, certainly for myself, I think the the background, the experience I gained over the course of the last couple of decades in tech has been, you know, I've been very grateful for having that background.

**[0:48:17] Marie Myers:** And it has helped me, I think, become a much more earlier, probably helped me become an earlier adopter of a lot of technologies like agentic AI. And I would encourage CFOs today, I think it's you. You can't just rely on your financial acumen, you need to use that plus your technology acumen to really help guide the enterprise. Because CFOs are becoming strategic business partners, and this is going to be one of the probably top three agenda items or top three items for any company in the next few years.

**[0:48:47] Marie Myers:** So if it's in that list as a CFO, I think you really need to develop expertise and acumen. And the good news, Michael, is a lot of things you can do to educate yourself. Part, what I would advise CFOs to do is, you know, learn as quickly as you can. The best thing you do is learn by doing. Sometimes, I mean, this weekend I'm hoping to go play with with open claw. I ordered myself a Mac Mini to sort of play at home, teach the kids.

**[0:49:11] Marie Myers:** So I would encourage you to to learn by doing and experimenting. And then, you know, really figure out what role you can play inside the enterprise. What can you do to help some of your business partners? What can you do to help the CIO in the company? So find a role for yourself as well where you can learn and grow, and therefore, you know, build the acumen that you're going to to need in your role. Because it's gonna be one of the top items, it's certainly a boardroom topic.

**[0:49:38] Marie Myers:** Michael, today, I sit on boards, and it's in the boardroom. Board members wanna know how companies are really deploying and using AI in the company. So you, you're gonna get asked the questions, so you wanna get ahead of it.

**[0:49:50] Michael Krigsman:** How technical does a CFO need to be today in order to be successful in this world?

**[0:49:57] Marie Myers:** All that experience that you've had around financial acumen is it's bread and butter, it's absolute table stakes. But developing a level of technology acumen is going to become increasingly important, Michael, because boards are going to, you know, they're gonna ask those questions, and a CFO is going to help to help guide some of those answers. So developing a a little bit of technology acumen, I'll just say a level of tech acumen that is, uh, relevant, is going to become increasingly more important.

**[0:50:28] Michael Krigsman:** Could we say conversational tech acumen?

**[0:50:32] Marie Myers:** I think conversational, but I actually, I'd push it, I'd say understanding. I don't think it's just being able to have a conversation about it. I think understanding, like the question you asked me, like, where in the enterprise is, you know, agentic AI really having an impact? I think it's important for a CFO to understand that and be able to therefore guide the company in those investment decisions.

**[0:50:54] Michael Krigsman:** And obviously, if you don't understand the logic and the dynamics behind it, how can you be effective That's, yeah, guiding the company?

**[0:51:03] Marie Myers:** it's gonna be very difficult. And then the board is going to ask you about, have you, can you realistically, you know, measure those impacts? You're gonna have to be able to answer those questions with numbers.

**[0:51:14] Michael Krigsman:** To what extent, or to what level of AI education, uh, should board members or must board members have to be effective?

**[0:51:25] Marie Myers:** I think it's super important for every board member today to have a good understanding of how AI is affecting companies. It is a question that, uh, most boards are starting to ask, and I think as a board member, you don't wanna be left out of the conversation because you don't understand. So I think at a bare minimum, you have to be somewhat up to speed with what is going on and what's driving companies. Because the, you know, many industries, as you know, Michael, are going to get disrupted with AI.

**[0:51:58] Marie Myers:** We're already starting to see some of that playing out in the market with impacts on software. So as a board member, in terms of understanding risks and having a strong, you know, engagement in risk in a company, I think it's absolutely it's, it's essential to understand that today.

**[0:52:14] Michael Krigsman:** Miles Sewers question Ian BeCroft said in his South by Southwest talk that we are heading for a world where agentic AI does the execution. If this is so, what does the finance organization look like in the future? How many people and what skills with retained finance will retained finance professionals have?

**[0:52:39] Marie Myers:** Look, I do think if you consider that, you know, agentic AI will do a lot of that foundational work. Uh, hopefully, finance professionals don't need to spend as much time on data gathering, data manipulation, they can spend much more time on the analytics. So you, I think you're going to see roles evolve and the skills evolve, and the ability to actually apply the judgment that you and I have talked about through the talk today is going to be important.

**[0:53:02] Marie Myers:** And the human side of your skills, whether it's the conversation we're having today or the sort of the the softer skills are going to continue, is going to continue to be a, you know, a weight on those skills going forward in finance. So perhaps it means less technical, you know, less of the spec, sort of technical specificity, and maybe more of the general with judgment, which means that you've got to learn over time, because otherwise judgment is not something, Michael, you you get the first year out of university.

**[0:53:31] Michael Krigsman:** And coffee on LinkedIn says, as CFO, what corporate functions do you think will benefit the most from agentic AI?

**[0:53:41] Marie Myers:** Yeah, I think we spoke a little about this earlier, Michael. I do think, uh, a lot of software coding organizations, marketing, IT departments are going to be some of the earliest, uh, places where we'll see AI have a big impact.

**[0:53:54] Michael Krigsman:** And the final question I have to ask, uh, junior finance staff have traditionally learned the business by performing that first round of analysis. If AI now handles those basic tasks, how do you prepare the next generation of finance leaders?

**[0:54:10] Marie Myers:** That is our responsibility, Michael. You know, I take that personally very seriously. I look at my teenage daughter who's about to go to university, and my advice to to young graduates is your jobs are going to evolve. So what's super important is to get a foundational understanding of AI, but still ensure you develop the analytical skills, because that combined with judgment is gonna be important about how you interact with AI. So perhaps there'll be less of what I did many, many years ago, Michael, pulling data, building these pivot tables, and much more around your analytical skills, the softer skills that come with it.

**[0:54:47] Marie Myers:** So you know, it's gonna be important to get that that education.

**[0:54:51] Michael Krigsman:** And with that, I'm afraid we're out of time. A huge thank you to Marie Myers from HPE, the chief financial officer of HPE. Marie, thank you so much for taking your time to be with us. I'm very grateful to you.

**[0:55:07] Marie Myers:** Thank you, Michael. I really appreciate it and enjoyed the time today.

**[0:55:10] Michael Krigsman:** And a huge thank you to everybody who watched. Before you go, check out cxotalk.com. Subscribe to our newsletter, and we have extraordinary shows coming up, so check it out. Thanks, everybody. Have a great day.