Most IT leaders struggle to demonstrate cloud ROI
Cloud cost management is one of those areas where confidence runs high, but clarity doesn’t always follow. Many IT leaders report that their FinOps practices, financial operations for cloud, are mature and mostly automated. On the surface, that looks like progress. But behind the scenes, real ROI is still elusive. According to a 2023 survey by CloudBolt Software and Wakefield Research, 78% of senior tech leaders said they have trouble demonstrating return on cloud investments.
What’s happening here? A lot of organizations are pointing to dashboards and automation scripts and calling it maturity. But proving real value, revenue growth, higher productivity, or measurable savings, is more complex. Saying you have automation doesn’t mean that automation is driving decisions, reducing costs, or speeding up the process between having insights and acting on them.
This disconnect between perception and performance comes down to execution. If a company says its FinOps model is solid, but it’s still fighting to trace cloud spend back to actual outcomes, then those processes aren’t fully operational yet. They’re too theoretical. You can’t justify a cloud bill just by claiming efficiency. You need numbers tied to impact.
Don’t mistake confidence for results. That kind of thinking slows transformation. C-suite leaders need to move past the assumption that FinOps automation alone translates to ROI. It doesn’t, not without constant evaluation, measurable results, and executive-level accountability.
Kyle Campos, Chief Technology and Product Officer at CloudBolt, put it clearly: “A good percentage of organizations may be taking a victory lap before even navigating the first turn.” He’s right. Confidence gaps like this don’t just affect budgeting, they distort the entire way companies evaluate their digital transformation progress.
Kubernetes adoption is driving cloud costs higher while its optimization remains a challenge
Kubernetes is everywhere now. It’s become the go-to strategy for deploying, scaling, and managing containers. It’s powerful technology, no question. The problem is, most companies aren’t controlling the cost that comes with it. Almost every IT leader surveyed, 98%, recognized Kubernetes as a key driver of cloud spending. Still, 91% admitted they aren’t effectively optimizing their Kubernetes clusters. That’s not just a red flag, it’s a sign that companies are scaling faster than they can manage.
Here’s the issue. Kubernetes offers a lot of flexibility, but that flexibility requires governance. Without standard practices, cost tracking, and real-time automation, it’s easy for spending to balloon without delivering proportionate business value. And that’s exactly what’s happening in many enterprises. They roll out containers, scale Kubernetes workloads, and only later realize they lack visibility or control over what those workloads are actually using, computationally and financially.
For executives, the takeaway is straightforward: you can adopt Kubernetes to stay competitive, but if you’re not building the operational muscle to manage it, you’re just inflating your cloud bill. Efficient Kubernetes usage isn’t just about deploying the tech, it’s about having the guardrails in place to optimize the way it runs.
This challenge isn’t going away. Containers will keep evolving, and workloads will get more dynamic. But if your FinOps team isn’t aligned with your architecture and DevOps teams, the result is uncontrolled cost growth, masked by the belief that you’re already optimized.
Use the data. Use automation. But don’t trust either unless you’ve got the operational discipline to back it up.
Automation in cloud cost management is often overestimated
Many leaders talk about automation like it’s already taken care of the problem. But looking at the data, that certainty doesn’t hold up. According to CloudBolt’s latest report, 66% of IT leaders say their cloud environments are mostly or fully automated when it comes to spend optimization and waste management. That sounds impressive, until you see that 58% still say it takes weeks or months to find and fix cloud-cost inefficiencies. The numbers don’t align.
This points to a key misunderstanding: having automation tools in place isn’t the same as achieving automated results. If your systems tell you where the waste is, but it still takes human intervention and internal coordination to act on it, you’re not operating efficiently. Automations that generate reports without triggering action don’t save money; they delay it.
For business leaders, the priority is not just implementing automation, it’s operationalizing it. It’s about reducing time between detection and action. That means having clean governance layers, enforceable policies, and tightly integrated tools. Looking at metrics once a quarter is too slow. Automation should be driving daily optimization, not passively delivering data that takes teams weeks to process.
The insight-to-action gap is measurable. And it directly impacts cloud ROI. If your teams need weeks to remediate overspending, then savings are consistently delayed. Over time, those delays become financial liabilities. It’s not about adding more tools, it’s about ensuring the tools are working at the speed the business needs.
Key barriers to demonstrating ROI
More tech doesn’t automatically translate into more value. One of the biggest barriers IT leaders face is linking cloud costs to real business outcomes. In the survey, 55% of respondents said they struggle to make that connection. That’s more than half of organizations unable to explain what they’re getting in return for their cloud investment.
This issue goes beyond tooling. Many companies are dealing with fragmented reporting structures, poor cloud tagging, and teams working in silos. According to the report, 48% cited organizational misalignment as a serious challenge. Another 44% called out inconsistent tagging and accountability. These aren’t technical bugs, they’re structural problems.
If finance, engineering, and operations are using different metrics, they won’t speak the same language when it comes to ROI. That creates delays, misinterpretations, and disconnected strategic decisions. When cloud usage isn’t carefully tagged and assigned, you can’t track it back to functional outcomes or cost centers. That makes reporting both slow and inaccurate.
Executives should push for unified visibility, not isolated dashboards. It means standardizing how service usage is labeled, making accountability part of provisioning, and driving collaboration across departments. This isn’t just an IT problem, it’s a company-wide opportunity to align cloud strategy with business goals.
Linking cost to value requires intentional architecture, both technical and organizational. Without it, cloud continues to be seen as a cost center instead of an innovation driver. That perception hurts investment confidence, slows key initiatives, and weakens the credibility of IT in the boardroom.
Hybrid multi-cloud management and AI/ML workload optimization
Cloud strategies are evolving fast, and priority areas are shifting just as quickly. The recent CloudBolt-Wakefield report shows that enterprises are beginning to focus cloud cost optimization efforts in two major areas: hybrid multi-cloud environments and AI/ML workloads. These aren’t experimental initiatives, they’re becoming central to the way large organizations run scalable, high-performing systems.
According to the data, 40% of surveyed leaders stated they’re prioritizing AI/ML workload optimization. Close behind, 39% said hybrid cloud management will receive direct budget focus in the next 6 to 12 months. That makes these two categories the most actively funded areas of cloud financial strategy, signaling where organizations believe the greatest efficiency gains can be achieved next.
Hybrid environments, involving combinations of public cloud, private cloud, and on-premise infrastructure, introduce complexity at scale. Cost control isn’t just about monitoring one platform; it’s about managing all of them, in real-time, with the ability to shift resources based on utilization, performance, and business demand. Lack of integration across environments translates into inefficiencies and missed opportunities to optimize.
For AI and ML, the spend grows fast. These workloads are compute-intensive and often scaled rapidly without oversight. If not continuously optimized, both in training and inference stages, they can dominate cloud costs very quickly. Organizations investing in machine learning need to pair those efforts with financial discipline or risk unsustainable operating expenses.
For executive teams, this means two things: first, clearer governance models across hybrid cloud platforms must be a top priority. Second, AI/ML workload teams can’t operate in isolation from financial oversight. Cost engineering needs to be a core part of your machine learning stack.
You can’t manage what you don’t standardize. Funding priorities are shifting toward areas with growing cost exposure and business dependency. The goal here is sustainable velocity, innovation at scale, without spending beyond what delivers real business return.
Key takeaways for decision-makers
- Struggles with cloud ROI: Most IT leaders still can’t tie cloud spending to measurable business value. Leaders should reassess FinOps execution to ensure it’s driving clear outcomes like revenue, productivity, or savings.
- Kubernetes cost blind spots: Kubernetes adoption is accelerating, but optimization is lagging. Executives must allocate resources toward improving Kubernetes governance to prevent runaway spend and performance issues.
- Overestimated automation: Automation is overstated in many organizations; remediation still takes weeks. Leaders should focus on tightening the feedback loop between insight and action to realize real cost savings.
- Operational barriers to ROI: Key blockers include poor tagging, siloed teams, and mismatched KPIs. To improve ROI visibility, organizations should align cross-functional accountability, standardize tagging, and strengthen cost ownership practices.
- Emerging priorities for optimization: Hybrid and AI/ML cost optimization are rising priorities with direct budget backing. Decision-makers should fund these areas proactively to maintain cost control as complexity and compute demands grow.
