From Formula 1 to FinOps: Lars Kielhorn on Agentic AI and Cloud Efficiency

Chapter 1 — Welcome to Unclouded: The Agentic AI FinOps Podcast

Kevin: Hello and welcome to Unclouded, the Agentic AI FinOps podcast by Cloudgov.ai. I am your host Kevin Rosenquist. Thank you so much for joining us on our first episode. I’m really excited about this show and I’m really excited about our first guest.

Chapter 2 — Meet Lars Kielhorn: CIO, Innovator, and Formula 1 Technologist

Kevin: I’m joined by Lars Kielhorn, a CIO who’s led digital transformations across pharma, private equity, and even Formula 1. He’s the president of SIM New York Metro and a lifelong believer in the power of technology to change how organizations think, act, and perform. Lars, welcome. Great to have you.

Lars: I’m so excited to be here, Kevin. Thank you for having me.

Kevin: Absolutely. I want to start here because this was fascinating to me. I grew up on racing. My dad worked in the industry. He was mostly NASCAR and Indy Car, but I definitely remember him getting up early on Sunday mornings watching Formula 1 races. So I’m actually still a big fan. I’m curious, how did you come to work with the Williams F1 team and what was your role there?

Chapter 3 — How Lars Helped Dorilton Capital Acquire Williams Racing

Lars: So Dorilton, at Dorilton Capital, I was their CIO. And as part of that role was also to provide shared services to the portfolio companies that Dorilton Capital owned. And one unexpected day, I got kind of a call from one of the due diligence people inside and said, “You know what, I’ve got a couple of questions regarding buying a Formula 1 team and what you think we should look out for in technology.”

Kevin: Yeah, before.

Lars: Yes, exactly. And so while I’m familiar with Formula 1, certainly was not an expert in it. But I put myself into the shoes, thought about it about data, real time data, the massive amounts of data that you would get in, you know, not optimal places like a racetrack because they can be wherever they are. And so I started working on that, and you know, a couple of weeks later we bought Williams Racing, Dorilton Capital.

Chapter 4 — Implementing Technology and Data Systems in Formula 1

Lars: After that I had the real pleasure and honor to work with the Williams Racing team on implementing technology with and for them over the course of two years. And I thought I knew a little bit about racing before that, and I really learned a lot of this fascinating world of Formula 1. And had the real pleasure of really working with great people there, and helping them implement their technology to bring the team further ahead.

Chapter 5 — How Tech and AI Are Fueling Williams Racing’s Comeback

Lars: If you are a fan of Formula 1 racing, you would see that this season actually they’re doing, compared to all the previous seasons, quite well. And I think part, not the only one but one part of that journey that helped there, is certainly the implementation of technology and leveraging technology in a more comprehensive way.

Kevin: Yeah, I actually am a big fan and they have struggled in recent years but they are definitely looking up. This has been a good year for them. They’ve got a couple of good drivers in Albon and Sainz. So I’m excited about it. Williams was once a powerhouse. So you believe that the data insights and stuff like that could be part of the reason for the turnaround?

Lars: Certainly. I left Dorilton about two years ago so I’m not up to date with the latest developments, in full disclosure. But as closely as I worked with them, I absolutely believe that that’s part of it. And it really is around integrating workflows and systems. And we had just at that point in time touched the surface of AI and ML. I’m sure in the meantime they have progressed on that, because there’s enormous amounts of data, enormous amounts of patterns that you can see in racing, and to correlate those and have an iterative cycle of improvement using AI/ML.

Chapter 6 — Data, AI, and Faster Iteration in Formula 1 Performance

Lars: Formula 1 is, with amounts of data and the complexity that comes with it, AI is uniquely really suited to bring improvements to a car and have faster iteration cycles and uncover things that you as a human, because of the breadth of data, might not as easily uncover.

Kevin: Yeah. And certainly in Formula 1 there is no shortage of data, no shortage of analytics, no shortage of telemetry. There’s so much going on. Most casual fans have no idea just how deep they go with that.

Chapter 7 — Why Racing Is a Perfect Example of Data Driven Strategy

Lars: I think that’s a key point that whoever is not familiar with Formula 1, specifically because it’s kind of the premier league so to speak of racing, it’s not just about going around on circuits in circles. There is so much information, data, and strategy that goes into every step, and all of that is data driven and systems driven. Not even to talk about manufacturing the car. And before, you know, you had a plan to build a new front wing and the car crashes, what then? So now you put that on the back burner, you got to get some replacement parts. So that kind of volatility is also something I think that people don’t appreciate in what it takes to keep two cars running on a circuit, where more often than not, including the last race, you got some crash.

Kevin: Yep. Yep. That’s an interesting point. I never thought of it like that as far as just having to pivot so quickly. When you’re trying to use your data and do something with it and then you crash or something happens or a motor blows or whatever it is. I guess that doesn’t happen as much as it used to, but yeah, having to pivot quickly, that’s definitely interesting.

Chapter 8 — Pivoting from Racing to FinOps

Kevin: Let’s pivot to, speaking of pivoting, let’s pivot to some FinOps talk. I’m excited to talk to you about this. With Agentic AI coming around, there’s kind of a new era of FinOps. I feel like it used to be about looking backward at cloud bills and whatnot. And now when you have Agentic AI, you have systems that can think and decide and act on their own.

Chapter 9 — The New Era of FinOps with Agentic AI

Kevin: How do you see this transforming how organizations manage their cloud costs and make financial decisions in real time?

Lars: So I think cloud is an incredible blessing. I’m going to start with the cloud itself. At Dorilton Capital, we actually did not operate a single physical server. We’re all cloud.

Chapter 10 — Why Cloud Is a Blessing and a Cost Challenge

Lars: And the beauty of that is that you can ramp up quickly. You can deploy resources as you need it. And particularly if you’re building things from scratch, if you’re building out capabilities, it is incredible because of the ease to ramp up.

The flip side, and now we’re getting to FinOps, as easy is it is to ramp up, as easy it is to have enormous amount of costs that you need to manage. And sometimes that’s justified and sometimes that’s not. And what you see is that of course naturally the cloud providers will make it very easy for you to spin up resources. You want a development server, you want to expand production capacity, that’s a very easy process. What is harder is then to optimize the spend.

Chapter 11 — The FinOps Dilemma: Easy to Scale, Hard to Control

Lars: Do you really need that? Is this on the right schedule? And that is the challenge of cost containment. And I saw this as I was building the technology for the Dorilton Capital and portfolio companies. We grew the technology footprint 10x, 20x. And so at some point in time I needed to be able to manage that, to be able to on an ongoing basis optimize.

And that’s also where Cloudgov came in, and this is how I first got exposed to Cloudgov as a new provider. I was looking for help to really manage that spend. That’s a couple years ago before ChatGPT and whatever completely took over. So it was a more subdued environment, optimistic, but it didn’t have quite the hype that it has today.

Chapter 12 — How Cloudgov.ai Helped Manage Multi Cloud Costs

Lars: And so what I wanted to do is I wanted to get some provider that helps you with that, that is independent of the cloud provider. I didn’t want to have like the fox in the hen house kind of thing, if you think about it, right?

Chapter 13 — Why Independent FinOps Tools Matter

Lars: I wanted to have an independent provider that can go across various footprints, be it Azure, be it Snowflake, be it GCP, all of that. And at the same time, knowing as much as I knew about AI and machine learning, I felt that pattern recognition, what are optimized patterns for usage and extracting that, AI and ML would be extremely good at doing that, if there’s a product that’s kind of geared towards that. So that’s kind of the thing first: understanding what you do.

Chapter 14 — Turning Cloud Data into Action with Agentic AI

Lars: What has since happened with the Agentic piece of it is really then making this into a call for action. So what am I going to do with it? A lot of the first step is understanding what you have. Are there opportunities to optimize your spend and find where you’re maybe over or under resourced, and how do you translate it into dollars?

The second piece is really then actually making it happen. Because it’s one thing to have all the data, and I’m sure a lot of people have the situation where there’s a lot of data in front of them. If you had all the time to act on it, you could actually do something. Because all the knowledge in this FinOps space does not help you unless you do something about it. Unless if it just sits there.

Kevin: Yeah. If it’s sitting there, like cool data.

Chapter 15 — From Insight to Automation: Making AI Take Action

Lars: Exactly. Now, knowledge is the first step. Action is number two. Actionable things. And now with Agentic AI you pair the ability to understand the pricing model of the cloud providers, right? That’s one piece. Pair that against your usage patterns. And out of that extract knowledge of actionable items. And now Agentic AI comes in and says, “Hey, you know what? I’m not just going to give you visibility, I’m giving you a course for action.”

And initially it was more like, here are some scripts, right? Here are some scripts that you can do to ramp up or ramp down some resources. And I think that’s extremely useful, and particularly since it was incorporated into a ticket system like Jira, because as a mature organization, everything should be ticket driven. So you have to have that type of integration, that whatever action comes out has an audit trail, has somebody assigned who is responsible to it, and you also track completion.

Chapter 16 — Building Confidence Through Human in the Loop FinOps

Lars: And so now we’re in that stage where you already have initiated that. And then the next step that you’re going through right now, once you have built some confidence, is to take the human more and more out of the loop.

Chapter 17 — When to Let AI Run Without Human Intervention

Lars: You still have a human in the loop, and I still think you should do this for critical resources. Initially there’s a learning curve, but the route is clear. As the reliability of the recommendations for actions improve, and as your confidence (which you have to have over time) improves, you can take the human more out of the loop and still get the right results.

Chapter 18 — What a Self Optimizing Cloud Environment Looks Like

Lars: And that’s kind of I think what the Agentic AI and Cloudgov does is really this marrying of information with action, if that makes sense.

Kevin: Yeah, absolutely. What would a truly self optimizing cloud environment look like?

Lars: I think there’s always a component, I’m going to call it, in terms of new capabilities. Because first you have to do something new. I think, at least for the foreseeable future, there is this step of saying, “Hey, am I supposed to roll out a new system? Is there anything else?” So that one, I think at least in the near term and mid term, I think a lot more can be done with AI. But in the near term there’s this human component to still translate, “Here is a business requirement, I need to spin up some resources.”

Now AI will also already be able to help you, exactly, say what it means in some capacity, more and more. How many resources do you think you’re going to need, what kind of resources are there? And then once you have that, then you get into this operational mode.

Chapter 19 — How AI Learns and Anticipates Cloud Usage Patterns

Lars: And I think where AI is at the moment, the step to go to AI without human in the loop is a little smaller. But you didn’t really say, “Now it’s running, tell me if you’re reaching capacity, tell me whether I have a production environment that sits idle for about 29 days a month. And by the way, it only comes up right now, I notice, even though you didn’t tell me it, I noticed it comes up typically the second and fourth Monday because that’s your sprint cycle, whatever.”

And it will be able to tell you that. And based on that, making recommendations. I think that’s the beauty. In the past you would have to tell it a lot more. I think what AI is able to do to look at and observe and, without you being explicit about it, making recommendations. And I think that’s the piece that is good, because infrastructure teams are always underresourced most of the time.

Chapter 20 — Why Over Provisioned Resources Go Unnoticed

Lars: And so these type of things fall into the category of important but not urgent, right? And this is where Cloudgov will help, because they’re typically running to put out a fire somewhere that’s, still in this modern world, still always have it somewhere.

Kevin: Right.

Lars: Yeah. And what they may have had on their agenda for that afternoon to go and look through the resources that are there and figure out what’s maybe a little bit over resourced, or maybe some areas where it’s under resourced, that typically falls a little bit on the wayside. And the over resourced even more, because under resourced eventually somebody will complain, right? It’s not running fast enough. We don’t have the response time or something’s not working. Over resourced, nobody notices.

Kevin: Yeah. Right. It works. It works. It’s fine.

Lars: And so that’s the piece where it’s even more important that you have the tools that allow you to make that job as easy as possible. Not having to do the research, not maybe even having to do the action. Start maybe with reviewing it until you have confidence.

Chapter 21 — Starting AI Automation Safely on Non Critical Systems

Lars: But at some point in time you may have enough confidence to say, “You know what, I’ll let the system run,” particularly if it’s not critical systems. Because that’s where you’re going to start. You’re going to start with non production systems or test systems, so not great if it’s down, but you’re not immediately affecting revenue, so to speak.

Kevin: Yeah, it’s not crippling. Yeah. So what do you think stops teams from acting even when they know the waste exists? Is it a time thing, a resource?

Chapter 22 — Why Teams Ignore Cloud Waste (Even When They See It)

Lars: It is a combination of both. I generally find, particularly good infrastructure engineers, they kind of even understand the charging model. Maybe not in all its nuances, but they could certainly make some headway. They may not get it all because the charging models constantly change. But they would be getting something.

I think the biggest piece is really the constraint of time, and the urgent winning over the important, the operational drag, I call it.

Chapter 23 — The Time and Resource Challenge in FinOps

Lars: And so, the relatively new discipline of FinOps in that way. Nobody talked about FinOps 10 or 15 years ago. It was the recognition that you need to manage that explicitly. Now, if you have a larger organization, maybe you’re able to devote somebody explicitly for that purpose. And you have to make a case. So there you might get some dedicated individual, and therefore get some better traction because that’s their job. That’s their priority.

But in more heterogeneous environments and the smaller teams, you’re not typically, at least when I was there, you would not get a dedicated FinOps person. So it is the people who are running and managing and understanding the resources that then in essence also are kind of tasked to work on cost control. But again, by heart they’re infrastructure engineers. They got to make sure that everything’s up and running, that nothing’s down, that all the customers are happy, that the new projects are all on time. And so that takes away.

Now I do believe, as I said, that the AI tools being fed the constantly changing infrastructure and cloud cost structure, right, for each product and service that you do, it is a challenge.

Chapter 24 — Why Cloud Pricing Models Are So Hard to Track

Lars: So that’s kind of the second piece. Who wants to read through the product catalog of AWS or Azure every week or every other week to make sure what tweaks you can make and how things change? And in this world, it is a little bit like compared to my cable bill in the olden days. I’m a cord cutter, but I think we’re all still familiar with, “Hey, it’s only $20 a month.”

Kevin: Yeah, I know. Yeah, totally. You’re like, wait a minute. What? Why is my bill 85? Why is my bill 85? Right. Exactly.

Lars: And that is to some degree, understandably, so that’s not a judgment call, that’s just a business practice call on the cloud provider. That’s what’s happening there as well.

Chapter 25 — Snowflake Case Study: When Query Costs Spike Unexpectedly

Lars: And it’s often distributed. So I give you an example from my own company. We have Snowflake for a data warehouse, and so we had data scientists and so on write some queries. And suddenly we found out, oh, the bill has increased for Snowflake. And that’s just the way the code was written in terms of the number of accesses and how it accessed the data. It was not a necessity. It was just, if you don’t know that it has a significant cost impact, you just don’t program towards it when you do your queries.

And that’s the kind of thing like, “I never had to deal with this. I’m supposed to produce data. I don’t know the relationship to the cost that comes out.” And so, you find out a month or two later, you look at the bill and like, “Why is this so high?” And then you start looking at it. And this is now the human, right? A month later, too. You just noticed because it was so significant.

Kevin: Yeah. Yeah. Just because it shocked you.

Lars: It caught even the human eye, so to speak. And it was something that was easily fixed. It had no impact on the data availability and quality. It’s just a matter of not optimizing towards cloud spend, just not being an objective and knowledge of the person who in essence programmed those queries.

Chapter 26 — The Coming Era of AI Driven Self Healing Cloud Guardrails

Lars: And that’s the big ones. And thank god we caught it. But in relative terms, there’s a gazillion smaller ones that you will not easily notice.

Kevin: Yeah. And what do you think about, if the cloud can protect itself, if it can automatically shut down unused systems, block risky deployments, manage costs. Are we close to this real time self healing kind of guardrail?

Lars: I think we are. I do think we are. Yes, I do think we are.

Kevin: Okay. How close?

Lars: I think products like Cloudgov are close. I think what we have to overcome is proving, real life proving, and making sure that the reliability is there. Building that confidence.

Chapter 27 — Building Trust in AI for Autonomous Cloud Management

Lars: And then making the decision to take out the human. That’s step one and step two. And again, this is not an all or nothing. This may be gradually.

Kevin: So I think people are going to be a little nervous. Right. Let’s give you a nervous kind of situation.

Lars: As much as I believe in this in concept, at the end of the day, it’s still, show me. Because you as a CIO, you’re next in the news, so to speak. If something doesn’t work and goes haywire and you’ve deployed these solutions, and for whatever reason there is a bug or something, you’re rightfully responsible for that. And that’s a good thing, because you don’t want to take unnecessary risks. So I’m not complaining.

Chapter 28 — The CIO’s Risk and Responsibility in AI Adoption

Lars: Somebody like me who loves the opportunity to innovate sometimes has to have a little bit of a brake pedal, just a little bit here and there like, “Hold on, y, it’s great concept but can it deliver in reality?” And I think Cloudgov over the last couple years, because I’ve worked with Cloudgov for a couple years, have come a long way in that confidence curve, so to speak. And really honed the product in that way that, I’m going to call it, confidence instilling, if that makes sense.

Kevin: So obviously cloud is just one piece of this modern spend. We’ve got AI infrastructure, SaaS, data platforms, just lots of stuff going on here. How do you see FinOps expanding to manage this broader technology economy?

Chapter 29 — FinOps Beyond the Cloud: Managing the AI Economy

Lars: Kind of the funny thing is, we all can see the writing on the wall, meaning the new exploding spend is AI. And obviously there’s pricing models behind AI. And so what was the cloud spend in the past, so to speak, I guess infrastructure as a service to be more precise. Now the next significant spend that needs to be controlled is really around AI. So the funny thing is, now we’re going to have AI help us curb AI spend or manage AI spend properly. So that’s where we’re heading in terms of, where’s the big cost block that was on nobody’s radar?

Kevin: Yeah, because we’re all just adjusting on the fly here, right? We’re all seeing it.

Chapter 30 — The Next Big Spend: AI Infrastructure and Cost Control

Lars: And though we’re early stage as well in that commercialization, we don’t really know where it’s heading. Because if you look at the investments that companies like OpenAI make, somewhere there’s got to be the return on investment and the revenue and revenue models. And how much of this is other sources, I’m no expert in this, but other sources where, just like search engine optimization, it’s prompt results optimization. If you take that, it’s part of that to get products and so on in front of people using AI. And how much revenue that would generate versus how much you’re going to do through subscriptions, I think has to still sort out.

Chapter 31 — How AI Will Help Manage Its Own Costs

Lars: But all we know is that the massive investments in AI that have been made certainly, at least to my knowledge, will be made up by the revenue that’s currently generated. That’s all about future revenue. And so where I’m going with this is, now let’s think of, whatever part of that remains subscription based so to speak, that’s the part of course that falls in a similar category then you have for cloud spend or SaaS spend. And so tools like Cloudgov.ai and others are able to help with that spend, as the next piece.

Kevin: Well, I can feel your excitement about it. You can tell you’re into this. You can tell you’re jazzed up about it. So if we fast forward a few years, in your opinion, what’s your ideal FinOps future once AI kind of takes over the heavy lifting, which we kind of all assume it will?

Chapter 32 — The Future of FinOps: Full Lifecycle Automation

Lars: I think overall in terms of FinOps, well, I think there’s the two sides to it, right? There is the monetary side to manage, and there’s the other side, which is like, “What do I need? What products, what services do I need?”

Kevin: Yeah. What do I need to do with this?

Lars: Exactly. What do I need to do with it? And I think you will get a full cycle, right? From the deployment or definition, requirements in the old fashioned world called requirements gathering, to spin up and then eventually spin down. So the entire life cycle. And I think you should, or at least I would always assume that, I want to have an independent party doing this for me across, you know, also cloud provider independent.

Chapter 33 — Independent, Multi Cloud FinOps Tools Are the Future

Lars: The question for you is like, you all have Google Cloud, Azure, whatever Oracle, whatever, and maybe others, OpenAI products as selling their service independently. And it’ll be this management of this entire life cycle. So go from, “I tell it what I need in a way, almost in natural language,” and that starts to tell me what I need from the start, and transcribe that into a set of products and services I need to purchase. And ideal world tells me, “If you do it on Google it costs X. If you do it in Azure it costs Y.”

So you spin it up on the best cloud, you manage it through its life cycle, and then you in essence spin it down at the end. Doing that mostly Agentic will be a blessing. It will fundamentally change what we as technology leaders have to manage. And maybe it worries people but it doesn’t worry me. But it means your job is going to be enormously different.

Chapter 34 — How AI Will Redefine the CIO’s Role

Lars: In the old world where I started to grow up, it was all about physical servers and building data centers. And now I don’t need anybody to do any wiring in a data center. I just take whatever cloud I want and spin it up. So that’s gone. But just in today’s world, you still need a good infrastructure engineer that tells you, “Here are the products, here’s the sizing, here’s the different levels of RPO or recovery point objectives,” and so on, to do all of that.

And the next thing will be, a infrastructure, I don’t know what the title is, somebody who knows how to ask the right questions. That will not go away. Even with AI, still somebody needs to provide some guidance in the direction. And it’s going to be broader and broader the better they get. Less focused on details, but there’s still going to be some sort of, I’m going to say oversight (I don’t really like that term, but oversight) that make sure it’s generally going the right direction, even though the level at which it operates, you have to operate, and the details you have to know, are going to get less and less important because AI will be able to do that.

Kevin: And then from that, got to have the human in the loop.

Lars: The human in the loop, right? Yeah. But most of it will be taken out. All the details will be taken out. Just like you don’t need a cabling guy if you have the cloud infrastructure. That level of detail is gone. And just extrapolate that. It will tell you what sizing. It will tell you what products to choose. It will tell you integration points. All of those things it will do. Because AI is good at that type, or ML actually to be more specific, is good at this type of pattern recognition and from that drawing the right conclusions.

Chapter 35 — The Merging of FinOps and Infrastructure Operations

Kevin: Is there any one particular FinOps process that you would just like to see automated forever? Just this is done now. This is automated.

Lars: The entire process. Ideally, ideally is what we’re talking about right now. I have something I’m running, help me optimize it. I want the entire life cycle done. Help me the cheapest way to meet my requirements, and spin it up in the right service provider. Against service provider independent. Not talk to AWS or Azure or any. You tell me if I want these requirements, I want to do this in this broader context, go to provider X, spin it up the right way, manage it all the way, and then spin it down.

So I think we will have to, the separation of FinOps and infrastructure operations will merge. It’s not going to be two separate things. It’s just coming together. That’s actually maybe the quintessence of what I’m saying here. Really, like, so we have FinOps and we have infrastructure operations. I think in the age of AI and AI capabilities, these will be products that will merge to do that.

And yet, I do see it has to be separate. It can’t be, you know, if you want to have the real true product, it can’t be AWS, it can’t be Azure, it can’t be GCP, because they’re tilted to their solution. So you need an independent solution just like Cloudgov that goes across all of them, and gives you the best results across all of them, and not just geared to optimizing the product of one company.

Kevin: Right. Yeah. Instead of it being just part of whatever one that’s trying to get your business, you’re talking about across all platforms.

Lars: Yes. But infrastructure operations and FinOps will be called out separately. That would be my prediction.

Chapter 36 — Why FinOps and ITOps Will Eventually Become One

Kevin: Interesting. All right, well, Lars, I’m going to put you on the spot here for a second. It’s time for our quick Cloudfire rapid fire round. I’m going to start a sentence. I want you to finish it for me. Okay. All right. FinOps becomes truly autonomous when…

Lars: When we start having enough confidence to take us out as humans.

Kevin: Yeah. That’s true. The technology is almost there. We just need to be comfortable with it, right? Confidence. Yes. Okay. I like that. All right. The biggest myth about cloud cost optimization is…

Chapter 37 — Quick Cloudfire Round: FinOps, Myths, and the Future

Lars: That people will do this automatically when they don’t have a designated job to do this as their primary responsibility.

Kevin: Give me one word to describe the future of FinOps.

Lars: Autonomous.

Kevin: Autonomous. All right. Yeah, that’s pretty easy I suppose, considering our entire conversation. All right, well Lars Kielhorn, thank you so much for joining. If anybody’s curious about getting your take on anything else, what’s a good way to reach out to you?

Chapter 38 — Where to Connect with Lars Kielhorn

Lars: Probably the easiest, just go through to my LinkedIn page. LinkedIn, that’s probably the easiest way. I am regularly on LinkedIn, so that’s really good. Message me.

Kevin: Okay.

Lars: My profile is, it’s just LKielhorn, and I was one of the first, I think, 100,000 LinkedIn users ever.

Kevin: Really?

Lars: Don’t put me exactly. 2003 or something like that, 2004, whatever it was.

Kevin: Either way, you were early. Nobody was on LinkedIn. Nobody knew what LinkedIn was, but yeah. Awesome. All right, Lars. Well, thanks again. Really appreciate your time, and looking forward to staying in touch and hopefully we’ll have you on again.

Chapter 39 — Closing Thoughts: Stay Unclouded, Stay Curious

Lars: Thank you Kevin for having me, and I really enjoyed my time in the interview with you.

Kevin: Thank you and thank you all for joining us on our first episode of Unclouded. If today’s conversation got you thinking about how to make your FinOps truly self driving, check out Cloudgov.ai, where Agentic AI turns cloud cost management into autonomous action. Especially if you’re tired of legacy vendors that overpromise and underdeliver, it’s time to move to a platform that actually evolves with you. Visit Cloudgov.ai and experience how constant innovation and AI powered automation are redefining the future of FinOps. I’m Kevin Rosenquist, and until next time, stay unclouded and stay curious.