News

Cloud Infrastructure Faces GenAI Turning Point, Report Says

• By David Ramel
• 10/02/2025

Nearly every enterprise cloud team is hitting barriers to scaling and resilience as AI-driven workloads surge, according to a new report from ControlMonkey. The study of 300 cloud, DevOps, and infrastructure leaders found that 98% face obstacles, with security and governance (37%), lack of real-time visibility (36%), and resource allocation (32%) topping the list.

The findings in the report, titled "The Gen AI Readiness Report: Cloud Infra at the Turning Point," come as organizations anticipate a 50% jump in AI-related workload demand over the next 12 to 24 months, creating what the report calls a "turning point" for cloud infrastructure readiness.

Alongside the near-universal blockers, nearly half of DevOps teams say they lack bandwidth for innovation, while just 46% of organizations report being fully prepared to scale automation for AI workloads. Together, the numbers paint a picture of cloud teams stretched thin, with legacy weaknesses magnified by the rapid expansion of generative AI.

"Workloads aren't just growing, they're exploding," said the company in a blog post last month announcing the report. "Teams expect a 50% increase in AI-driven workloads in the next 12-24 months, with almost 40% predicting exponential growth.

"Think about what that means: substantially more clusters, pipelines, policies ... and more risk. Because AI doesn't just add scale. It accelerates the pace of change, magnifying every weakness in your infrastructure."

ControlMonkey describes itself as "the industry leader in IaC automation and cloud governance, helping enterprises gain complete control over their cloud infrastructure," and offers a platform designed to fill in many of the gaps mentioned in the report.

Blockers to Scale Are Nearly Universal
The report's most striking number is that 98% of organizations face barriers to both scale and resilience. The leading issues are security and governance challenges (37%), lack of real-time infrastructure visibility (36%), and resource allocation struggles (32%).

ControlMonkey characterized these as foundational cracks in cloud infrastructure readiness: "Without visibility, security, and AI-aligned workflows, even the most forward-looking teams risk being overwhelmed before they ever hit full stride."

DevOps Teams Stretched Too Thin
The survey also highlights limited capacity among cloud and DevOps staff to address the growing demands of generative AI. Nearly half of respondents (46%) reported low or limited bandwidth for infrastructure innovation, with many engineers focused on firefighting instead of scaling.

The report notes that this lack of time to innovate could prevent organizations from preparing for the AI-driven workload surge expected over the next two years.

Automation Gaps Remain
Automation shortfalls add to the challenge. Only 46% of teams said they are fully prepared in terms of cloud automation readiness for AI workloads, while the rest admitted they are only somewhat prepared or not ready at all.

Average infrastructure-as-code (IaC) coverage stands at just 51%, and only 1% of teams report being fully automated. These gaps mean that even organizations confident in their readiness face exposure across performance, cost management, compliance, and skills.

Cloud at a Turning Point
The report concludes that the rapid growth of generative AI workloads is accelerating preexisting weaknesses in cloud infrastructure, forcing enterprises to confront them sooner than expected. With nearly every respondent citing obstacles, rising costs, and pressure on teams, the study frames cloud infrastructure as being at a "turning point" where security, visibility, and automation will decide whether organizations can scale successfully or fall behind.

Methodology
The findings are based on an online survey of 300 leaders across DevOps, Cloud Engineering, Platform Engineering, Cloud Ops, SRE and Infrastructure, conducted in June 2025 by Global Surveyz Research. Respondents work at companies with more than 1,000 employees across software, banking/financial services/insurance (BFSI), media and entertainment, and insurance, with 70% based in the United States and 30% in the United Kingdom. The average completion time was seven minutes, and the order of most non-numerical answer choices was randomized to reduce order bias. Demographic breakouts in the report show department, role, and tooling distributions (e.g., Terraform/OpenTofu) and company size cohorts.