Flexera SVP Jay Litkey on Crafting FinOps Playbooks to Maximize AI ROI

Hello, and welcome to the latest edition of the Techstrong AI Leadership Insights series. I'm your host, Mike Huard today with Jay Littky, who's senior Vice President for Cloud and finops at flexera. And we're talking about, well, the need for a finops playbook for ai.

Jay, welcome to the show. Thank you very much, Mike. Everybody, at least most folks now are starting to become aware of the cost of ai.

It's rather significant. And most folks that I talk to do not have a plan, and yet we do have this notion of finops that we've been using to kind of try to optimize cloud spending. So can we apply finops to ai?

I think the, uh, to cut to the chase, the short answer is yes, and it should be. And having been around the block before this feels like cloud did, right? When the hyperscalers came along, it was, uh, at first cloud was there, and then it was, well, we're not using it.

We're not allowed to use it, to, all of a sudden seems like everybody's using it, and nobody knew how much of it they were using. And, uh, that's how, as you know, finops came to be born, was to try and get a handle on managing and tracking those cloud spending. So this patterns repeating itself with ai.

So, back to my succinct answer, I think finops is timely and well positioned to, uh, help here with ai. Is this kind of one of those proverbial back to the future moments, or is there something different about AI workloads that we should consider as we build out this new playbook? I think there's many patterns that are similar.

If I take it back to basics, right? Finops was invented largely to help control manage, right, uh, cloud and to put, you know, understand the business value of cloud and to justify it. Finops, as you know, has expanded since to include other scopes.

So it now in its purview does this for SaaS services. It does this for data center, it does this for, you know, it's looking at software licenses. And so AI is just another type of technology with a lot of unique things about it.

We can talk about those unique things, um, that, uh, that finops principles can be applied to. So I'd say there's a little bit of a, you know, back to the future, we've seen this movie before, but also there's enough differences with what ai, uh, and considerations to take in where this isn't just a, uh, you know, apples to apples, rinse, repeat. So how do we set that up?

'cause I think when people hear the word playbook, they're like, okay, well, there's some sort of document that sits on a shelf, but is this more of a programmatic approach where we have actual policies and governance capabilities that help us execute what's in the playbook Emerging? So the first thing I do, when somebody asks me where should they start, I steer them to the finops Foundation. Now, full disclaimer, I'm on the governing board of the finops Foundation, so I do have some bias, but there's lots of content from some really smart practitioners that's being published now around how to start viewing this area, um, how it's similar and different, right?

Than traditional finops approaches and ways to start skinning the skinning the cat here, and ways to start, uh, start tackling this. So, you know, that's the place I steer people. The good news is people don't have to start with a clean sheet of paper, and there's a lot of momentum that people aren't aware of it already.

Again, coming outta the finops Foundation, There's a couple issues when I talk to folks about all this, but one that comes up over and over again is we seem to all wanna rush to get the latest and greatest GPU, but can we be smarter about what models run where, and maybe especially in inference or there lower cost processors to think about here. I mean, it feels like to me, the playbook needs to start with some of the basic fundamentals of what can run where. Yeah, let me do the up level then of where I've seen, um, people doing this.

Well, or when you think about taking it back to basics, because what, what, what's very clear, like with cloud, when cloud came on, many people just jumped in and then realized they sucked at doing it, right? Because it wasn't all thought out, it wasn't governed. They never thought about what it was gonna cost.

So sort of, um, take it up a level, if I think about AI and the challenges people have, you are right. People are realizing now it costs a lot of money, right? And a lot of people are asking the questions now around, do we understand the business value of ai?

And again, this sounds like cloud did when cloud came along and everybody said, why are we doing this? And so maybe if it makes sense, I'll start just talking about, you know, the, the ROI of AI initiatives. We'll start there and then we'll talk about the cost management.

AI doesn't fit in the traditional neatly and at least into the traditional cost benefit models. So when you think about that from a very basics, the ROI of of AI is often delayed, there's a long tail to it. There's soft benefits, like improved decision making, operational efficiencies, not just the quick hits of maybe immediate revenue or cost savings.

So when I think about ai, first of all, and people start thinking about the ROI of it, and then we'll kind of back into the cost savings. As I say, the benefits are sometimes delayed. Um, companies often don't have a baseline or a control group, so they don't really have a pre AI benchmark for some of the things they're trying to solve for.

Uh, uh, I'll give you an example. Some people are saying, let's apply AI to automate a process, right? That's all the rage people are thinking of.

What can we automate? Automating a process on its own is not a success metric. That alone indicates the business value.

So, you know, starting from scratch, people need to think about, again, what kind of, what are the reasons why I am adopting ai? How am I gonna measure that success? And let's align some stakeholders before we get too far down the path.

Because leaders often have different success metrics than, say, a data scientist, right? A, a leader may be looking for the impacts to be on revenue or cost savings, data scientists, other people might be thinking about efficiencies or throughput and other kind of things, right? Um, very differently.

So I'd say aligning people on what to measure is the first step here, so that when you're starting to measure costs and benefits, you've got something to measure against, and you've agreed what success looks like. So along with the answer, and I can dig into a bunch of these areas for you more, but I like to start a lot of people talking about the why are you doing AI and what does success look like and how are you gonna measure this? Because measuring it like cloud shouldn't just be about how much you're spending.

I also think the space is evolving. And if I look at a lot of the data centers that run AI today, they kind of feel like very large mainframes. And, but every time we have that kind of monolithic approach, we eventually discover or rediscover distributed computing.

So will these workloads over time get more distributed so that we can be more efficient about using different classes of processors to run this? Yes. I think just sort of take up level here, there's many people doing AI that have a data center, and you're right, there's use cases about why they want to do some AI things in their data center, and they might wanna do other AI things in the cloud.

And so they're distributed. There's many organizations out there that are born in the cloud that have never had and never will have a data center. And so their entire usage of AI is entirely in the cloud.

And I think the reality we've learned from the cloud journey is, you know, we still have lots of hybrid environments, we still have lots of data centers, as you said, we still have mainframes, is there's no one size fits all about how AI is gonna be consumed here. And that's one of the challenges of, of there's no playbook that fits everybody's situation, but what finops brings is a bunch of general principles that you can apply in any situation you're in. Mm-hmm.

Finops, of course, assumed that the finance department was talking to the IT department and they created some level of, uh, collaboration. But do the finance people even understand AI yet and what the implications are? Or is it a little too early for them to get involved?

I don't think it's too early for them to get involved, but no, they don't understand it. Mm-hmm. But many stakeholders don't understand it yet.

Right now, AI adoption for many people is reactive fear of missing out, right? This disruptive thing is here, let's go jump in. And as I mentioned earlier, they haven't articulated what the business value targets are, what they're trying to accomplish, that they're gonna measure against.

So what's happening in real time where I see finance people getting involved is, whoa, it seems like we're spending a lot on these AI pilots or projects. Finance doesn't even know how many of them are happening out there. So the first step is they don't have full visibility of all the things happening in ai.

Um, and so I think they should be involved, but I think those business value and business outcome discussions should be asked earlier in the adoption cycle rather than reacting. But this is the same movie we saw. Play it with cloud.

It's no different than the public cloud adoption. Hmm. So what's your best advice to folks as you look at all this?

Is there some rational way to go do this upfront, or will we once again wait for the inevitable crisis before cooler heads start to prevail? I think there's lessons learned, as I say, the finops Foundation, where you see these practitioners arrows in their back, right? They've made mistakes, they've learned how to do this the right way.

There's emerging, you know, um, ways of thinking here. And again, I, I keep, I'm biased to talking about the finops Foundation, what they're recommending. But if I think about that, you know, I talked about developing outcome-based AI strategies.

Start with the why we're doing this. Define some of the business goals so that we all know whether you're in finops, whether you're in finance, whether you're, you know, the data scientist, what is success or how are we gonna measure this so we're not debating how much money we spent later and that we're spending too much money. I think establishing, um, an ROI framework, and this is something that you want to have finance people aligned with, finops aligned with the people doing ai, and to agree upfront that this isn't, shouldn't just be a quantitative, right.

ROI framework. Yes, cost revenue efficiencies should be in there, but there's also agree about qualitative KPIs, right? How you're gonna do this when you, when you go into it further things that customer satisfaction, employee retention, there's other benefits to AI adoption beyond the quantitative.

And you gotta, you gotta get the stakeholders aligned about how are we gonna define success and should success only be measured on how much we can save? And one of the things that finops goes out of its way, right, to preach and talk about instead, is finops isn't just about helping people reduce their cloud bill. In fact, many people doing finops well increase their cloud spending because they're proving and demonstrating and measuring, they're getting real business value.

Same thing with ai, is we all agree why we're doing it and what the business values we're trying to accomplish. And again, not just trying to automate a process, but why are you trying to automate the process, right? What's the outcome?

If we can agree on those things, it becomes less about how much we're spending. 'cause spending more on AI might be a great thing, right? And that's the journey that finops taught us with cloud is finops isn't just about saving money, you're missing the picture.

If you view it that way, it's about justifying the business value of the technology, right? That you're consuming. So again, rinse, repeat, apply it to ai, same kind of thing.

And I spent a lot of time, and I know I've, I've said it this, this idea of cross-functional alignment. What, what finops also evolved and has learned is it's a team sport. When you look at a a finops team, there's not just one role of person on that team.

Finance should be plugged into it. Engineering should be plugged into it, right? Different constituents.

AI is very similar here, but the number of constituents and the personas has broadened. When I think about cloud consumption, who uses AWS or Azure? You know, you could go through who uses that and who drives cloud adoption.

But you think about ai, the question's almost now, who's not trying to explore AI pretty much every function in a company. Mm-hmm. So again, the, the importance of getting cross-functional stakeholders aligned on what success is and how are we gonna measure the success of AI becomes more important.

There's more people pile into the party Right now. You hear a lot of folks talking about pilot projects that failed and things are not quite going in terms of the ROI as expected. So I think we all thought that 2026 was gonna be the year of AI in production, but I can't help but wonder maybe if we may need a mulligan here, and when we start over again and get it right, Well mo most technologies come along as, you know, there's the hype cycle, right?

We all get excited. We say it's gonna solve world hunger. Everybody's doing it.

Why aren't I, we all jump in and then there's lots of disappointments. This projects don't succeed. Somebody's asking me, why are you spending so much money?

What value do we get from it? And then everybody starts going into some negativity, right? Like, it's not all it's cut out to be.

We're going through the similar cycle here with, with ai. I, I'm convicted, it's a game changer, right? I'm convicted it's gonna change the world, but like cloud and every other technology wave that we saw come along, it doesn't entirely consume the world and everybody doesn't win outta the gates.

And we've seen this in successive waves of technology patterns, right? We saw it with virtualization, we saw it with cloud, we're now seeing it with ai. The things go through this, again, peak of inflated expectations and hype.

And then you go into some kind of trough where not everybody is winning and there's lots of questioning around should we be doing this? And then the real success, you know, there's a slower ramp even though the world's evolving very quick. So to me, this is just going through that, that kind of same pattern.

And I think, you know, there are successes happening out there, and sometimes people don't even realize they're having those successes. You know, I hit on the ROI, there's lack of clear use cases. So there's just trying to throw things at the wall and kind of see what stick, and in that there's maybe good things happening, but it's obfuscated by all the, all, all the noise.

ROI was an afterthought. So after the fact, they're trying to think about the outcomes that they were trying to achieve. Um, you know, there's people running at this, you know, too quickly because they have poor data quality.

I think you, you probably know that AI is lifeblood, it runs on good ai, you know, or sorry, good data, structured data. And so if your data's not structured and formatted in a good format, readily accessible, you're gonna stumble. You're not gonna get the benefits from ai.

And many people overlook that. They run into trying to apply ai, but they haven't cleaned up their data. And then I'd say, you know, another one is just, you know, the, the over rotation in minds, the over reliance on cost avoidance expectations and narratives.

Hey, we're gonna use AI 'cause we're gonna save a lot of money. And like cloud, if that's your only view, you have too narrow of a view. The benefit of public cloud isn't to save money.

I think most people now know that same with ai, but again, there's an over alliance because of the amount of money being spent and because leaders like to hear, save money, generate revenue, there's, there's a big over alliance or over focus I say right now and using AI to save money. And there's a lot of other benefits. So again, to me it feels like we went through this journey with cloud.

There's a lot of, a lot of repetition about the technology adoption cycle here. And finops again grew and I think has done a very good job applying itself to managing cloud and other technology spending. Same thing again, why I said I think this is perfect timing to apply finops to ai.

I think it was Bill Gates folks who said that, uh, we overestimate the impact of innovation in the short term and underestimate its value long term. I think AI is probably another one of those instances. But in the meantime, hold onto your wallets, Jay.

Thanks being on the show. Thanks so much, Mike. All right.

Thank you for all watching the latest episode of the Techstrong AI Leadership series. You can find this episode and others on our website. We invite, should check those out.

Until then, we'll see you next time.