GenAI isn’t just hype anymore-it’s the business-critical workload flooding enterprise infrastructure. And it’s scaling faster than most cloud teams can keep up.
Among the findings of �The Gen AI Readiness Report‘ compiled by ControlMonkey, 83% of cloud, DevOps and infrastructure leaders expect AI workloads to rise in the next 12-24 months, with average growth forecasts of 50%. But 46% of enterprise leaders say DevOps has no bandwidth for innovation, even before AI workloads double. That means most will be forced to choose between fighting fires today or preparing for the flood tomorrow.
The report also found that organizations admit they’re hitting walls when trying to scale resilient infrastructure for AI. The problems aren’t new; they’re the usual suspects: rising costs (37%), no real-time visibility (36%), broken scaling and resource allocation (32%). These aren’t AI problems. They’re infrastructure problems, and if they’re not fixed now, cracks become chasms and teams will never catch up.
ControlMonkey’s 2025 Gen AI Readiness Report asked a simple question: Are DevOps and Cloud teams ready for the Age of AI? To find out, ControlMonkey polled 300 senior leaders from enterprises with 1,000+ employees across the U.S. and U.K. in finance, tech, media and insurance. The data paints a blunt picture: some teams are solid, some are already showing strain, and the rest are about to get swallowed by the AI tsunami.
Summary of key findings:
- AI Workload Growth is Inevitable. Almost no one is Ready. 83% of cloud and DevOps leaders expect AI-driven workloads to rise in the next 12-24 months, with average growth forecasts of 50%. Nearly 40% say the increase will be significant or exponential.
- DevOps Teams are Already out of Steam. 46% report limited or zero bandwidth for infrastructure innovation, even before AI workloads double. Teams will be forced to choose between keeping afloat and preparing for the future.
- Automation Gaps are Holding Back Scale. 54% admit they aren’t fully ready for cloud automation at AI scale. Familiar vulnerabilities could torpedo scaling AI’s speed and security. Top blockers: reliability (43%), skills gaps (39%), scalability limits (36%).
- Cost, Visibility, and Scale are top Roadblocks. Organizations report barriers to scaling resilient cloud infrastructure for AI. The top challenges include managing rising costs (37%), lack of real-time infrastructure visibility (36%), and difficulty with effective resource allocation and scaling (32%). These aren’t new or complex AI problems. They’re basic infrastructure issues and if they’re not fixed now, AI will make them worse.
- Governance Is the Next Choke Point. When asked about the impact of AI-driven workloads, 29% cited security governance as a top challenge, 29% compliance complexity, and 20% said they need standardized governance policies just to compete in AI at scale.
- What Leaders Say They Need to Succeed. 45% of leaders surveyed pointed to better training or visibility as their top need for managing AI workloads. Cost control (21%), governance (20%), and automation (14%) all rank lower. Without the skills and clarity to manage AI at scale, the rest doesn’t matter.
“The AI wave isn’t coming, it’s here. And cloud teams don’t get a time-out,” said Aharon Twizer, CEO of ControlMonkey. “Most are still patching the basics. The next twelve months will decide who scales and who sinks.”
The AI surge is already here, and the gap between ambition and readiness is only widening.
