Organizations Face Obstacles to Cloud Infrastructure Readiness in the Face of Increase in AI-Driven Workloads — Campus Technology

Organizations Face Obstacles to Cloud Infrastructure Readiness in the Face of Increase in AI-Driven Workloads

Enterprise cloud teams are hitting barriers to scaling and resilience as AI-driven workloads surge, according to a new report from ControlMonkey. The study of 300 IT 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. “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.

Roadblocks 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.”

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.