FinOps X 2026 | Brad Payne, PointClickCare

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Brad Payne

Sr. Manager FinOps

PointClickCare

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Brad Payne, PointClickCare

Brad Payne

Sr. Manager FinOps PointClickCare

In this interview from FinOps X 2026 in San Diego, Brad Payne, senior manager of cloud FinOps at PointClickCare Technologies, joins theCUBE's John Furrier and theCUBE Research's Paul Nashawaty to discuss how the FinOps discipline is expanding beyond cloud cost management into an enterprise-wide model for AI value accountability. Payne draws a direct parallel between the cloud cost visibility frameworks his team built at PointClickCare and the emerging challenge of managing token spend — arguing that the same discipline of mapping spend to specific products an... Read more

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How should organizations apply FinOps to gain visibility into and optimize AI token (cloud) spending across products and services? add

How do you view FinOps as both an engineering and a cultural practice, and how will AI affect FinOps work—particularly financial planning/analysis, procurement/vendor negotiations, and relationship-building? add

What gaps need to be filled right now to help practitioners effectively adopt and measure the value of AI (e.g., data, models, invoicing transparency, and FinOps/value intelligence)? add

What does the phrase "the big rocks are taken care of" mean? add

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Brad Payne, PointClickCare

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John Furrier

>> Welcome back to theCUBE's livestream here in San Diego, FinOps X 2026. I'm John Furrier, host of theCUBE with Paul Nashawaty, my co-host with theCUBE Research. Breaking down the AI for FinOps, and the market's certainly changing, the ecosystem's changing. It's buzzing with open source. You got practitioners, you got vendors all working together to build the platforms and the tools to usher in an era of instrumenting all the money. And cloud spend, which is now cloud and AI. Brad Payne is here, senior manager of FinOps for PointClickCare. Brad, thanks for coming on. Really appreciate it.

>> Thank you, sir. Thanks very much.

John Furrier

>> I love when this dialogue before we go on camera because my brain's already thinking what we can talk about, but we were just riffing on the changing role of FinOps in the broader scope as we might be looking back at this point in time as a moment.

>> Yep. Yeah, I think absolutely. Whether it's looking back next year when we're at Tokenomicon and reminiscing about FinOps, but also what you and I were talking about was the lessons of the past. Some of the things that we've built and the practices we've built within FinOps is going to pay dividends moving forward as we apply AI to our FinOps practice.

Paul Nashawaty

>> Absolutely. I mean, Brad, you had mentioned the lessons in the past. FinOps is changing, FinOps X is changing. We have a new kind of show that we're planning on for next year. With this evolution in the FinOps world, there's been a lot of adoption, a lot of understanding, a lot of education that's been going on. What are your thoughts around what you're learning here that will be applied for not just now, but what's happening in the next generation of where this is going?

>> Yeah, what I'm learning is exactly that. The foundations that we built as part of our FinOps capabilities will pay off as we start managing token spend. And so we were just talking a moment ago, token spend is cloud spend. It is spend that our organizations are going to incur, and we're going to need to have a visibility to that and then the ability to understand how to optimize that. And where we started our FinOps journey at PointClickCare, it was very much in reaction to needing to understand for what was this spend. Not within the context of, "Oh, this is our VM spend, this is our database spend," but we need to understand product X, product Y, service A, service B was spending this amount. And from there we could understand, okay, what value are we getting from that spend? That's all we're talking about again with AI. We're about to see token consumption, token generation happening. We're going to need to be able to pinpoint that to those items.

John Furrier

>> Yeah, what's interesting too is that cloud spend was great. If you look at the evolution of the cloud, look at AWS, the pioneer, now you got Azure and Google Cloud kicking ass and taking names. The early adopters was Netflix, Capital One. The end users became clouds, and they were SaaS-based. Enterprises became SaaS-based, some more than others, but everybody will be AI-based. So you start to see a real enterprise piece. So you have the cloud, but also their own businesses. So that is now crossing over and intersecting with tokenomics and FinOps. They have to be their own.

>> Yeah. I think you better answer your question before of what am I learning? It's exactly that. Our procurement teams, our financial planning and analysis teams, they're likely aware. We're probably seeing in some of our invoices today that what used to just be seat-based license is now seat-based plus. And maybe it's some tokens, maybe it's some usage allowance, but we're either going to be sitting here next year or two years from now, a lot of that software subscription stuff that organizations had a lot of predictability to of, "Oh, well, I know how many seats we have in engineering, so there's a correlation between seats and licenses," that's going to change.

Paul Nashawaty

>> Yeah, yeah. So we look at AI for FinOps, this is what we're talking about, there's obviously an opportunity for optimizing FinOps using AI, and it depends on where you are on your maturity and how you want to drive that forward. There's also a place where AI doesn't fit. And let's talk a little bit about that. From your perspective, where does AI not fit in the world of FinOps? When we look at everything that's been going on in the announcements, there's a lot of this crawl-walk-run approach. Where doesn't AI fit?

>> So it's funny because I often think about ... FinOps is two things. I believe it's an engineering practice. We want all our engineers to be frugal and mindful of cost and manage those. But then it's the cultural practice. And the cultural practice shows up when you're going across those disciplines. And so I think AI is going to be super helpful to my financial planning and analysis teams and the way that they interface with data. But AI is going to assist with that interface of data, but it's not going to replace the conversations that FinOps and procurement are having. It's not going to replace the conversations that FinOps teams are having with cloud vendors. There's negotiation, there's trust-building, there's a lot of the establishing those relationships that we're just going to need FinOps practitioners as humans.

Paul Nashawaty

>> So BASF had the tagline: we don't make something, we make it better. They made that ... And in my mind, for those conversations, AI doesn't make something, it makes it better. So if you do your education, you do enablement, and then you take that in learnings, and then apply it to the conversations, that's where it makes it better. Is that fair to say?

>> Yeah, I agree, 100%. Yeah. I'm really excited by what's now at the fingertips of our financial planning analysis teams as they interface with data. We've always been that proxy. Help us understand our hosting bill. It's a million lines or it's a many dozen page invoice. How can accounting read this? How can we class it to the right cost centers and to the general ledger? AI's going to add a layer there that just allows them to interface with cloud cost data in a much easier fashion.

John Furrier

>> I want to get your thoughts on this because you mentioned cloud before. One of the themes that's coming out of our interviews and we see it in the hallways and the talks is cloud was a great precursor in the cloud journey, but not everyone went to the cloud. Some did a little lift and shift. Some went in, like Netflix and others I mentioned earlier, they went cloud-native, but that was a slower adoption so the cloud players and hyperscalers could get the telemetry and do all the things they got to do, and cloud spend then kicks up and then gets going. AI is not that way. It's all in. Everyone's jumping in the pool at the same time. So there's a little bit of AI anxiety.

>> Sure.

John Furrier

>> Take me through your thoughts on how do you see that, one, and what is it? Is it real, or how do people minimize the anxiety?

>> Yeah. I mean, you see it from every angle. We rolled out enterprise Anthropic subscription to every individual in the organization, and you run the gamut of anxiety and excitement within every department. And I think engineers are going to find their own ways of embracing that, but so is every other department as well. And I think I haven't had a lot of conversations. If I think of my financial planning analysis team, they're not so much anxious about, "Oh, some AI's here that can support my job." It's, "Actually, I'm excited about how to start interfacing with this and what's capable."

John Furrier

>> Yeah.

Paul Nashawaty

>> Yeah. When we start looking at the intersection of the disciplines that you're talking about here, the anxiety that John's mentioning, I can see it being increasing pressures for people to do more effectively, operational efficiencies, productivity gains, and they're looking at that. So there's a little bit of that anxiety, but I also think that that anxiety gets lifted if you're using it appropriately.

>> 100%.

Paul Nashawaty

>> Right? Does that make sense?

>> Makes sense to me. I think it's one of the things that we hear about it a lot. The model router has been a big talking point. And I think that's certainly not something we've yet engaged with, but there's going to be those layers that we need to put in to ease, especially if we're thinking of anxiety around costs. How are we making sure the right business functions are getting the access to the right models, to the right level of intelligence, and that that continues to support what the business needs?

John Furrier

>> What's your thoughts on the show? What's your vibe? What's highlights that you like? What jumped out at you here? What's jumping off the page?

Paul Nashawaty

>> It's funny, I feel like I have a very basic answer, but coming in, I was very excited about yesterday's keynote. Some of the preamble and some of the pre-conversation was we knew this was an inflection point, we knew some ... a paradigm shift was about to happen. So that's been exciting to witness that and be part of those conversations. And then the other thing that's exciting is previous years has really been about how the breadth and depth of FinOps is changing. Scopes had us start talking about data center and SaaS and licensing, and we increased some of the capabilities. FinOps teams go deeper on less being a reporting function and start exercising some engineering functions and operations functions. This year now it seems like it's something different or something new. It's not more of the same. There's something new to look forward to. Your comment about AI adoption versus cloud adoption, 100% there's going to be faster, way more AI adoption. And so a lot of, again, go back, a lot of the lessons, a lot of the things that we've built in strong in FinOps practices are going to immediately be applicable and need to be applied to AI. And I think more organizations are going to need to mature some of that faster than perhaps we've had to.

John Furrier

>> We heard from Amazon and Google Cloud players, they have that history too. They did their work to help provide data, data sets. The model guys, they're pedaling as fast as they can, but there's still gaps. What do you see as some gaps that need to be filled superfast right now that would be super helpful for practitioners?

Paul Nashawaty

>> I think there is a gap between what practitioners are seeing in invoicing for AI and how to be able ... How do you break that open and really understand what we're getting? The value FinOps teams are going to provide isn't in an accounting of what AI products are we spending on and who's spending , but what's inside of that, what value are we getting from that? And so if we think of FinOps teams and their role in AI being less accounting, more about value intelligence, I know today what appears from my AI spend, and it's very large, and it's very nebulous. We need some ability to get into what's that tokenization ratios, where are those tokens being applied? And so teams can really-

John Furrier

>> Value intel, I love that term because it speaks to the value creation going on, the adoption.

John Furrier

>> Yep.

Paul Nashawaty

>> Well, let's double click down. I mean, there's a couple of announcements that you talked about in some of the themes that you're alluding to. There was FOCUS 1.4 was released. And there's the boardroom conversation, board-level conversations for FinOps right now. Do you see some of the announcements that came out of the keynotes yesterday and today? And some of the things that were interesting, are they helpful to what you're looking at? Are you implementing it today, and is it really a board-level conversation, or is it still early for that?

>> Real quick, I think the announcements for FOCUS 1.4 were actually super exciting, and there's a real maturation in what's there. And again, moving from just how do you align from accounting perspective, but now get some outcomes where they were talking about what's the impact of changing.

Paul Nashawaty

>> You were talking about harmonizing that data.

>> Exactly.

Paul Nashawaty

>> Yeah.

>> From an executive perspective, what I think is interesting is I feel like I have to go back to my organization and ask for a new mandate. It's not just FinOps. It is something around token management, tokenomics, AI spend, and FinOps.

John Furrier

>> Okay. My question for you, I love this conversation, Brad. So pretend that we're starting a new team. We get called in from a company's boss, "We're going to hire you guys, John, Paul, and Brad."

>> Love it.

John Furrier

>> "Build me a FinOps team for the future." What does that look like? Clean sheet of paper, what would we do? What would you advise? How would you set it up? Who would be on the team?

>> I would start with really curious engineers. It's our practice today, primarily individuals with engineering backgrounds, PhDs in computer science. And then individuals that are skeptical and curious.

John Furrier

>> As an engineer, right on the money right there.

>> Sure, sure. LLM coding, vibe coding, prompt engineering is going to really blow open our ability to engineer solutions. And so the right engineers solving those right problems, assisted with prompt engineering, we can get the data into the hands of the individuals. It goes back to the cultural aspect of FinOps. Engineering practice to build the platform, engineering practice of engineering teams taking cost responsibly, but then the cultural practice becomes so much easier because we can get the right data to the right teams and .

John Furrier

>> The phrase that's being kicked around this week is the big rocks are taken care of. What does that mean?

>> I can give a personal anecdote of, in the last three months, having moved our forecasting capability from quarterly to near real time, because I had the ability, assisted with an LLM in my IDE, to be able to create those functions, to be able to systematize it in an amount of time that previously I'm like, "Yeah, I could do it, but it'll just be in time for next quarter, so why don't I just forecast next quarter?"

John Furrier

>> That's awesome. Brad, great to have you on. Just what are you looking forward to doing? What's your focus? What are you optimizing for going forward into next year as the whole world is evolving very, very fast?

>> Yeah. Two things I'm thinking about optimizing is some of the practices that we have today, I'm optimizing forecasting. I want to optimize ... I'm notorious for loving to look at my reserved instances every morning with my cup of coffee. I think there's a very easy way to ...

John Furrier

>> I could take you down a little bit. Squeeze a little bit out of that.

>> And then the future, the new stuff is really cracking open the tokens. I've got a line item for Azure Foundry, but what's in that, who's consuming that, to what benefit? Getting that crisp, that's a common theme ...

Paul Nashawaty

>> .

John Furrier

>> Cracking the code, baby....

John Furrier

>> common theme.

John Furrier

>> Cracking the code.

Paul Nashawaty

>> That's it.

John Furrier

>> The money code.

>> That's it.

John Furrier

>> There's money in there somewhere.

>> Yeah.

John Furrier

>> Okay. Thanks for coming on. I really appreciate it.

>> . Thank you both.

John Furrier

>> Yeah, the perspective of the practitioners really is an engineering practice, but it's integrating into the C-suite because once you can instrument the data, which is the money and the cost and the values being created, it's going to create very new profit equations all being built out here in this community. We're doing our part to bring you the data on here on theCUBE. I'm John Furrier with Paul Nashawaty. Thanks for watching.