Rising cloud spend, AI complexity and board scrutiny are pushing CFOs to rethink how cloud investments are governed and optimised.
Azul has released its CFO Cloud Cost Optimisation Report, revealing how finance leaders are reshaping cloud governance as cloud costs rise, AI adoption accelerates and cloud economics become a board-level issue.
Based on a Censuswide survey of 300 US CFOs and senior finance leaders, the research shows that cloud spending is no longer viewed as a passthrough IT expense but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience.
Cloud costs are rising, and CFOs are taking notice
Nearly nine in ten respondents (88%) report that their cloud spending is increasing, with one-third describing the rise as ‘significant’, underscoring growing pressure to improve financial discipline as cloud usage scales. Only 9% say their cloud spend is staying flat, reinforcing the urgency to address waste as usage continues to grow.
This concern is now reaching the highest levels of governance. Two-thirds (66%) of CFOs say cloud spend has become a board-level issue, signaling a decisive shift in accountability from IT alone to the executive suite and boardroom.
AI investment creates a new financial tension
While cloud costs continue to climb, CFOs are under growing pressure to invest in innovation. More than half (56%) of CFOs cite AI and automation as their top financial priority, followed by improving cash flow and working capital efficiency (52%) and reducing overall cloud costs (40%). Yet AI adoption is also making cloud cost management more difficult with 43% of CFOs saying AI is adding new layers of workload complexity, complicating forecasting and cost control efforts at a time when predictability is increasingly critical.
This dynamic has created a clear mandate for finance leaders: fund AI initiatives by reducing costs in other areas. With cloud spend accounting for a significant portion of overall IT budgets, organisations are forced to rethink how efficiently their applications consume cloud resources.
CFOs acknowledge structural cloud waste
As scrutiny increases, finance leaders are also recognising the scale of inefficiency embedded in many cloud environments. More than two-thirds of CFOs believe that up to 30% of their cloud spend is wasted, representing a significant drag on profitability and financial predictability. Rather than viewing cloud waste as an isolated issue, the report shows CFOs increasingly see it as a structural efficiency problem – one that requires better governance, deeper visibility and more effective optimisation across infrastructure and applications.
CFOs turn to new optimisation levers
To regain control, organisations are deploying a mix of cloud cost management approaches. The most widely used tools focus on visibility and forecasting, including AI-powered cloud spend analytics (45%) and native cloud provider tools (44%).
Notably, CFOs are beginning to embrace deeper technical levers that directly influence cost efficiency. Sixteen per cent of organisations already use Java runtime optimisation or JVM tuning. In addition, re-platforming or application modernisation initiatives (24%) and workload or infrastructure optimisation vendors (29%) are cited as strategies finance leaders are using to manage cloud costs. These modernisation efforts help organisations improve performance efficiency, reduce resource consumption and modernise legacy systems that drive overspending.
Cloud optimisation as an enabler of innovation
CFOs do not view cloud optimisation purely as a cost-cutting exercise. At the top of the list of main financial benefits they would prioritise, 45% of CFOs say the primary financial benefit of cloud cost optimization is increased budget flexibility to fund innovation, including AI and digital initiatives. Other top benefits include improved margins and profitability (42%), better forecasting and budgeting (39%) and stronger alignment between IT spend and business outcomes (39%). Another 37% cite ‘higher utilisation of existing infrastructure’, signaling a desire to extract more value from current systems.
Rather than a tactical cost exercise, cloud optimisation is emerging as a strategic financial lever for CFOs to fund innovation, protect margins and ensure cloud investments deliver measurable business returns.
CFO top priorities for the next 12 months
CFOs and finance leaders shared their top cloud-related priorities for the next year, balancing innovation goals with heightened financial discipline as cloud spending continues to rise. Improving performance and uptime (43%) topped the list, followed by reducing overall cloud costs (39%) and maximising profitable growth from cloud investments (38%). In addition, gaining visibility into current spending (35%), compliance and governance (34%) and supporting AI/ML initiatives also made the list. Together, these priorities highlight a clear mandate for finance leaders to ensure cloud investments support AI-driven innovation while delivering predictable, measurable returns and protecting margins.
“With nearly nine in ten CFOs seeing cloud costs rise and AI now a top investment priority, finance leaders are being forced to rethink how efficiently their applications consume cloud resources,” said Scott Sellers, co-founder and CEO, Azul.
“Cloud optimisation has become a strategic lever – one that allows organisations to fund AI innovation, protect margins and bring greater predictability and accountability to cloud investments. Organisations that optimise at the infrastructure level, starting with how their software consumes compute resources, gain a meaningful advantage in funding the innovations that drive growth.”
Q&A
How much cloud spend do enterprise organisations actually waste?
According to a 2026 Censuswide survey of 300 U.S. CFOs and finance leaders, the average estimated cloud waste sits at nearly a quarter (23%) of total spend. The vast majority of finance leaders are concerned about their organisation’s current cloud cost levels. Nearly nine in ten say cloud spending is increasing, yet two-thirds report it has escalated to a board-level concern, signaling that waste is no longer an IT problem but a financial governance issue. Azul’s CFO Cloud Cost Optimisation Report finds that the gap between what organisations spend and what they actually consume is driven less by overspending and more by structural inefficiency in how applications consume compute resources.
Why aren’t cloud cost management tools actually reducing cloud waste?
Most enterprises are investing in the wrong layer of the problem. According to Azul’s 2026 CFO Cloud Cost Optimisation Report, nearly half of organisations use AI-powered cloud spend analytics and native cloud provider tools, but only 16% use Java runtime optimisation or JVM tuning, which directly reduces the compute resources applications consume. Visibility tools tell organisations where money is going; they do not change how efficiently applications use the infrastructure they are running on. For the nearly half of CFOs who say their top financial benefit from cloud optimisation would be freeing budget to fund AI and innovation, the enabling strategy is actually reducing consumption at the application layer, not just monitoring it.
How are CFOs freeing up budget for AI without increasing overall technology spend?
With the majority of CFOs citing AI and automation investment as their top financial priority for 2026, the pressure to fund new initiatives without expanding budgets has made cloud cost optimisation a strategic lever rather than a back-office exercise. Azul’s CFO Cloud Cost Optimisation Report finds that nearly half of finance leaders say their primary benefit from cloud optimisation is increased budget flexibility to fund innovation, making cloud efficiency directly tied to AI investment capacity. Organisations that optimise at the infrastructure level, particularly through application-level compute efficiency, can recapture meaningful portions of their wasted cloud spend without arduous re-platforming, re-architecture or migrating workloads.
