Cloud Teams Face A GenAI Tsunami They’re Not Ready For

The rapid growth of artificial intelligence (AI) is transforming the way businesses operate, and its impact on enterprise infrastructure is being felt across the globe. According to a recent report by ControlMonkey, AI workloads are expected to surge by 50% in the next 12-24 months, with nearly four in ten leaders predicting a significant or exponential increase. This surge in AI adoption is not just a future wave, but a reality that is already breaking over enterprise infrastructure, leaving many cloud teams struggling to keep up.

The Challenge of Scaling AI

The report highlights the significant challenges that cloud teams face in supporting the growing demand for AI. With 46% of DevOps and cloud leaders admitting that their teams lack the bandwidth to innovate, it’s clear that the current infrastructure is struggling to cope with the pace of change. The pressure on cloud teams is intense, with most leaders wanting to push AI projects forward, but often finding that their teams are too busy plugging gaps to focus on innovation.

The automation of processes is supposed to be the key to unlocking the potential of AI, but the report reveals that only 46% of teams are fully prepared for AI-driven workloads. The rest admit that they are not ready for scale, with familiar gaps in reliability, skills shortages, and scalability limits blocking progress. These are not exotic “AI problems,” but old, boring infrastructure problems that have not been solved and are now colliding with the breakneck pace of AI adoption.

The Importance of Visibility and Governance

The report also highlights the usual pain points that have plagued cloud adoption for years, including costs, visibility, and governance. With 37% of leaders citing rising costs as their top infrastructure barrier, and another 36% pointing to a lack of real-time visibility, it’s clear that these issues are still a major concern. The inability to see what’s happening, or to trust that automation will respond, can grind innovation to a halt, making it essential for enterprises to invest in the fundamentals of automation, visibility, and governance.

Governance is another looming choke point, with nearly a third of respondents naming security governance and compliance complexity as top challenges. AI workloads often touch sensitive data and introduce new dependencies, making governance a moving target. Without standardized policies, enterprises risk either slowing innovation with red tape or accelerating recklessly into compliance disasters. It’s essential for businesses to get governance right, or risk being left behind in the AI revolution.

Closing the Cloud Skills Gap

The survey’s emphasis on training and expertise underscores a growing reality: success in scaling AI depends as much on people as it does on platforms. With the cloud skills gap widening, it’s essential for enterprises to invest in the people and systems that will support AI adoption. This includes providing training and visibility to help teams understand and manage AI workloads, as well as investing in automation and governance to ensure that AI is deployed securely and efficiently.

The introduction of tools like KoMo AI, which aims to ease Infrastructure-as-Code bottlenecks, is a step in the right direction. By generating Terraform code aligned with an organization’s existing modules and policies, KoMo can help reduce repetitive reviews and enable less-experienced team members to contribute more effectively. However, it’s just one part of the solution, and businesses must take a holistic approach to addressing the cloud skills gap and supporting AI adoption.

The Next 12 Months Will Decide

The next 12 months will be crucial in determining which businesses will thrive in the age of AI. With AI adoption as important today as getting on the internet was 30 years ago, it’s essential for enterprises to empower their teams to surface results quickly. This means investing in the fundamentals of automation, visibility, and governance, as well as providing training and expertise to help teams understand and manage AI workloads. The companies that survive this inflection point won’t necessarily be the ones with the most advanced AI models, but those whose infrastructure teams can absorb the surge without breaking.