Flexera: Why 2026 Is AI's 'Back to Basics' Moment

[00:00:03] AI has become one of the biggest priorities in business today. And yet a recent survey from PwC found that only 12% of CEOs say that AI has delivered both revenue and cost savings. Now that gap between ambition and very real outcomes is raising some uncomfortable questions across enterprises around the world right now.

[00:00:30] But today I'm joined by Jay Littke. He's from a company called Flexera. And together we're going to unpack why so many AI initiatives still struggle to deliver measurable value. And also why many leaders might need to go back to basics with financial discipline, governance and clearer success metrics from the outset.

[00:00:54] But we're not just talking about the problem today. I want to focus on the solution too. And Jay is the perfect guest for just that. So enough scene setting from me. I want to give a quick thank you that partners like NordLayer make it possible for me to attend events, speak with industry leaders and bring those insights right back to you here on every podcast, every episode on the Tech Talks Daily. And I was recently at a tech conference where Mark Templeton said that the browser is now the computer.

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[00:02:03] It's one of those ideas that feels so obvious once you hear it. But if you want to know exactly what that shift looks like in practice, please pop over to NordLayer.com slash browser to find all the information you need. And please come back to me. Let me know your thoughts on it. But now on with today's show. Let me beam your ears all the way to Canada where Jay is waiting to speak with us today. So thank you for joining me on the podcast today, Jay.

[00:02:31] Can you tell everyone listening a little about who you are and what you do? Jay Lidke Yeah, I'm Jay Lidke from Flexera. I'm also a governing board member of the FinOps Foundation, and my work largely centers on helping organizations make smarter technology investment decisions. And right now that especially includes helping people move beyond AI excitement and starting to answer tougher questions around value.

[00:02:58] Jay Lidke And that is such a critical conversation right now. I think we're seeing this all around the world. And before joining you today, I was reading PwC's recent survey that found that only 12%, that's one, two, 12% of CEOs say AI has delivered both revenue growth and cost savings. So from your vantage point at Flexera, why is there such a wide gap between ambition and real measurable results? What's happening here?

[00:03:26] Jay Lidke I think that the gap is really an execution gap. Organizations haven't lacked ambition, that's for sure, around AI. They've moved really fast, but many move faster on adoption than on accountability. And we see that same pattern in Flexera's recent IT priorities report.

[00:03:45] 94% of IT leaders say they're looking to integrate AI into their tech stack, but only 19% say demonstrating AI effectiveness or ROI as a priority. And that's a pretty revealing disconnect, I think. It shows many organizations are still treating AI as something to roll out rather than something to measure and optimize. So when, you know, PwC finds only 12%, as you say, CEOs are seeing both cost savings and revenue growth.

[00:04:15] It makes sense to me. It makes sense to me. The issue isn't really the technology itself, though. It's that too many AI initiatives were launched without clearly defined business outcomes. And there's no shared ownership across teams. So I think the punchline is that AI adoption has moved faster than AI accountability. A hundred percent with you.

[00:04:37] And I was reading how you've also said previously that technology, that the issue isn't technology itself, but the absence of clear outcomes and financial discipline. So I've got to ask, what are the most common mistakes that you see organizations making when launching those AI initiatives without those defined success metrics from the outset? I think the biggest mistake is starting with the tool instead of the business problem, classic technology adoption.

[00:05:05] Teams ask, how can we use AI before they ask, what are we trying to improve and how will we know if it worked? You know, common issue is, you know, vague or absent success criteria. If the goal is just better productivity or more innovation, that's hard to measure and even harder to justify financially. Leaders need concrete targets, you know, lower support costs, shorter cycle times, higher conversion, things like things that you can actually track.

[00:05:33] And then there's the cost side. So our researcher at Flexera found that 80% of IT leaders report increased spending on AI applications and more than a third believe they're overspending. And that tells us that companies are scaling spend before they build the discipline to connect that spend to outcomes. So that, you know, too many companies optimize for experimentation before they optimize for outcomes.

[00:06:00] And as early AI experiments continue to disappoint, you're encouraging leaders to just go back to basics. But I've got to ask, what does that look like in practice for a CIO or CFO who finds themselves in that position where they need to justify their AI investments this year? There's that strong focus on ROI on every tech project now. But what should they be doing? For me, going back to basics means reintroducing investment discipline.

[00:06:29] You know, AI shouldn't be exempt from the same questions that leaders are asking of any other strategic initiative. For a CIO, that means narrowing the focus to the use cases that most closely impact business value. You need to be clear on ownership, on cost, expected impact. You know, for a CFO, it means asking what problem are we solving? What metrics should improve? You know, what's the baseline today?

[00:06:56] What's it cost to run this, not just as a pilot, but to scale? And our research also shows that AI has remained the top strategic focus for IT leaders for the past three years. And 33% name AI integration as their leading priority for the coming year. So the appetite, you know, it's clearly there. But after several years of investment, the conversation is shifting. It's no longer just how do we adopt AI, but how do we prove that it's working?

[00:07:26] So I see 2026 as a reset year. 2026 is the year that AI stops being funded on belief and starts being funded on evidence. And when many people are listening to our conversation today, when they hear FinOps, they often associate it with cloud cost management. So how does a FinOps roadmap apply specifically to AI workloads, which can be unpredictable and resource intensive too?

[00:07:55] AI is exactly where a FinOps mindset becomes even more important. Yeah. So these workloads can scale quickly. I think, you know, costs can become less predictable. It's easy for experimentation to outpace visibility. And if FinOps roadmap for AI starts with visibility. So first understand what teams, use cases, business units are, are driving the usage and spend of AI. And then accountability, make sure that spend has an owner.

[00:08:23] And that optimization, you know, uses the right model, the right infrastructure, the right controls for the job. And this matters because, you know, Flexera, you know, our research also found that 80% of IT leaders are increasing spending on AI applications. And over a third believe they're overspending. So this isn't a theoretical issue. Organizations are already feeling the pressure between enthusiasm and efficiency. FinOps helps leaders ask better questions.

[00:08:53] Not just can we run this AI workload, but should we run it this way at this cost for this business outcome? AI doesn't reduce the need for FinOps discipline. It raises the stakes for it. And one of the things I always try and do for everyone listening is give everybody a valuable takeaway.

[00:09:13] And for people that are nodding their head in agreement to everything we're saying here, looking at doing things differently and focusing on those value add activities and measurable difference and impact. How can leaders listening and indeed their organizations tie AI usage directly to business goals and ROI rather than treating it as an almost innovation lab expense? What should they be doing here?

[00:09:38] You know, as I've highlighted so far, they need to connect every AI initiative to a business priority that leadership already cares about. It could be reducing service costs, you know, improving employee productivity, speeding up revenue cycles, increasing retention, lowering operational risk. Then they need a baseline and a target. If you can't say what metrics should move and by how much it becomes very difficult to prove value later.

[00:10:03] And what our data shows is that organizations are still much stronger on intent than proof. Ninety four percent are integrating AI. Only 19 percent prioritize demonstrating the ROI tells us many teams are still treating AI as strategic in theory, but not managing it as accountable in practice. So AI becomes a business investment when usage can be tied to a measurable outcome and a budget owner.

[00:10:30] So that's when ROI stops becoming a vague aspiration, become something leaders can actually defend. See, you can't call something transformational if you can't measure what it changed. A hundred percent with you on that. And despite that, there will be some perception of people listening out there that when they hear FinOps, they'll think about cutting spend. And it's not always like that.

[00:10:54] So can you tell me how you would reframe FinOps as a way to align finance, engineering and business teams around shared accountability and performance? Because it's a bit of a myth, isn't it? It's just about cutting spend. Yeah. FinOps is not just about cutting spend. It's probably the biggest misconception about FinOps. FinOps is not about shutting things down or saying no to innovation. It's about helping organizations spend with more intention, you know, and align that spend to business priorities, improve value.

[00:11:25] So what I like about FinOps, and as I mentioned, I'm on the governing board of the FinOps Foundation. So I'm pretty biased, but what I like about FinOps is that it creates a common language across teams that often operate with very different incentives. FinOps is a team sport. You know, finance cares about return and predictability. Engineering cares about performance and speed. The business cares about outcomes. FinOps helps bring those groups together around shared accountability.

[00:11:51] And that's especially important now because our research shows that AI remains the top strategic focus for leaders and spending continues to rise. So if those teams aren't aligned, you end up with more investment, but not necessarily more impact. So I'd, I'd reframe FinOps this way. It's not a cost cutting exercise. It's a coordination model for turning technology spend into business performance and value. FinOps isn't about spending less.

[00:12:21] It's about spending smarter together. Again, it's a team sport. And if we look ahead as AI adoption scales across the enterprise, are there any governance and financial guardrails that leaders should be thinking about putting in place right now, rather than repeating the same execution gaps over the next few years? Because we've seen what happens there in the past as well. So from that point of view, from financial guardrails and governance, any advice there?

[00:12:52] The first thing leaders need is visibility. They need to know where AI is being used, you know, which teams are using it for what purpose, at what cost. And without that governance is mostly guesswork. You know, second, they need accountability. Every meaningful AI initiative should have an executive sponsor, an operational owner and agreed, you know, success metrics. And third, they need financial and policy guardrails around scale.

[00:13:19] So that budget thresholds, approved tools and models, review processes, you know, clear criteria for when a pilot moves into production, right? Or get shut down. And the reason this matters is because when the demand signal is as strong as it is for AI right now, you know, our data says that 33% of IT leaders still rank AI integration as their top priority. And spending continues to increase. So leaders should assume AI adoption is going to keep accelerating. The question is whether governance scales with it.

[00:13:48] The companies that avoid the next wave of disappointment will be the ones that put discipline in place before the spend becomes unmanageable. So governance should not arrive after scale. It should enable scale. And we have busted a few myths and misconceptions around FinOps today. Loved every minute of it.

[00:14:11] And before I let you go, I've got to ask, is there anything else that people misunderstand most about your industry or any other myths about your job? Or area of expertise that we can lay to rest today? Anything else we can dispel? Yeah. One of the biggest myths is that financial governance somehow slows innovation down. In my experience, you know, the opposite's true. Yeah. A lack of governance creates ultimately waste, confusion, mistrust, finger pointing.

[00:14:39] And that's what eventually slows down innovation. Another misunderstanding is that ROI is just a finance conversation. It's not. Especially with AI. ROI has to be shared across finance, engineering the business. You know, like FinOps, AI is a team sport. Everyone has a role in defining what success looks like and how value gets measured. And honestly, you know, the research reinforces why that matters.

[00:15:03] When 94% of organizations are moving towards AI integration, but only 19% are prioritizing, you know, proof of effectiveness, shows the market still has work to do, right? And connecting strategy to accountability. So the myth I'd challenge is that governance and innovation are in conflict. Done well, governance is what makes innovation sustainable. So good governance doesn't kill innovation. It gives it staying power.

[00:15:31] And I think that is a powerful moment to end on. But before I do let you go, for anybody listening interested in continuing this conversation we started today, not just about the myths and misconceptions, but the real value that we're talking here and how they can work with you and keep up to speed with announcements coming out of Flexera throughout the year. Well, do you like me to point everyone listening? You know, the best place is to start with Flexera online. We regularly publish research insights on AI FinOps, broad technology investment trends.

[00:16:01] And in particular, our IT priorities report. It's a useful resource for anyone trying to understand how organizations are thinking about AI adoption, spending, ROI. It's a good backdrop, lots of themes we talked about today. But I also encourage folks to dig into the FinOps Foundation, you know, where some excellent work's being done by some very smart AI leaders in the industry, including within a number of dedicated working groups that people are able to participate in.

[00:16:28] Well, there are so many big things I'll be taking away today from that huge figure from the report. Only 12% of CEOs say AI has delivered both revenue growth and cost saving. An absence of clear outcomes and financial discipline being one of the big causes of that. And FinOps busting myths and misconceptions around that and how it's actually a way to align finance, engineering and business teams around shared accountability and performance.

[00:16:53] I will include links to that powerful report that we've talked about today, Flexera, everything else you mentioned. I would urge everyone listening to go check those out and feedback both to Jay and indeed myself at Tech Talks Network. Love to hear your insights and experiences too. But more than anything, Jay, thank you for shining a light on this today. Really appreciate your time. Thank you.

[00:17:15] So if AI is going to move from experimentation to real business impact, it really seems that the answer might lie less in new technology and more in accountability, governance and financial clarity. So big thank you to Jay for sharing his insights with us today, talking about Flexera, busting a few myths and misconceptions. And for you listening, how are you measuring the real business value of AI inside your organization right now?

[00:17:44] I hear a lot about it on my news feeds and LinkedIn, etc. I hear it in conversations I have in this podcast, but I want to hear your insights, your experiences. This is a monologue, not a dialogue. And it's so important to get your voices out of here. I don't just want to be talking into your ears every day. You have so much to share too. And I genuinely mean that. So please, techtalksnetwork.com. You'll find a myriad of ways you can contact me there.

[00:18:12] Let's keep this conversation going and learn from each other. But that's it for today. So I'll be back again tomorrow with another guest. But thank you as always for joining me today. And I'll meet you in the podcast feed tomorrow morning. Bye for now. Bye for now. Bye for now. Bye for now.