Mike Vizard shares insights from a ControlMonkey survey, highlighting the preparedness gaps IT teams face in scaling and managing AI workloads, and the importance of automation and infrastructure as code.

Survey: Most IT Teams Not Prepared to Manage AI Workloads

Author: Mike Vizard

A recent survey conducted by Global Survey Research for ControlMonkey examined the preparedness of 300 senior IT leaders from large organizations in the U.S. and UK to manage artificial intelligence (AI) workloads at scale. Despite anticipating a 50% rise in AI-driven workloads over the next 12–24 months, most respondents admitted to serious readiness gaps in automation, reliability, and IT skills.

Key Findings:

  • Readiness Gaps: 54% of organizations are not fully ready for IT automation at AI scale.
  • Top Concerns: Reliability (43%), skills shortages (39%), scalability limits (36%), rising cloud costs (27%), overloaded compute/storage (20%), deployment bottlenecks (18%), security/compliance (18%), and observability (17%).
  • Resource Limitations: 46% have limited or no bandwidth for infrastructure innovation needed to support AI growth.
  • AI-Specific Challenges: Cost management (37%), lack of real-time visibility (36%), scaling resources (32%), security (29%), compliance (29%), and standardizing governance (20%).
  • Training and Automation: Training and visibility top priority (45%), followed by cost controls (21%), governance (20%), and automation (14%).

Infrastructure Modernization and IaC Adoption:

The survey notes that only a minority of IT environments currently use Infrastructure as Code (IaC) tools, such as those based on open source Terraform. While 80% claim some level of automation, only 1% have fully automated infrastructure management. Platform engineering and centralized management are desirable goals, but progress remains slow relative to AI-driven workload scaling.

Aharon Twizer, CEO of ControlMonkey, emphasizes that failing to modernize infrastructure management will exacerbate existing operational challenges as AI adoption accelerates.

Practical Takeaways:

  • Investment in automation, platform engineering, and IaC is increasingly essential for scaling AI.
  • Skills development and improved visibility are critical areas for organizations wanting to support AI-driven growth.
  • Addressing cost, governance, and security proactively can help avoid future crises as AI adoption puts more pressure on existing IT operations.

For further details, visit the original article on DevOps.com.

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