Survey: Most IT Teams Not Prepared to Manage AI Workloads

AI Workloads Deployment Readiness

A survey involving 300 senior IT leaders from organizations with over 1,000 employees in the U.S. and the UK indicates that many organizations are not yet prepared to deploy artificial intelligence (AI) workloads at scale. Conducted by Global Survey Research on behalf of ControlMonkey, the findings reveal that 83% of respondents anticipate a 50% rise in AI-driven workloads over the next 12–24 months.

Challenges in AI Workload Management

Despite the expected increase, 54% of those surveyed indicated they are not fully prepared for IT automation at AI scale. The primary concerns include:

• Reliability: 43%
• Skill gaps: 39%
• Scalability limits: 36%
• Rising cloud costs: 27%
• Overloaded compute and storage capacity: 20%
• Deployment bottlenecks: 18%
• Security and compliance issues: 18%
• Observability: 17%

Additionally, 46% reported limited or no bandwidth available for the necessary infrastructure innovation required to deploy AI workloads at scale.

Specific AI Workload Challenges

Survey participants highlighted several specific challenges related to AI workloads:

• Cost management: 37%
• Lack of real-time infrastructure visibility: 36%
• Difficulty in resource allocation and scaling: 32%
• Security and compliance: 29%
• Standardizing governance policies: 20%

Nearly half (45%) identified training and visibility as their top needs for managing AI workloads, followed by cost controls (21%), governance (20%), and automation (14%).

Infrastructure Management and Automation

The survey reveals that less than half of respondents manage their IT environments using infrastructure-as-code (IaC) tools and frameworks. While 80% reported achieving a moderate level of automation, only 1% indicated that infrastructure management is fully automated. ControlMonkey, a provider of an IT infrastructure automation platform based on open-source Terraform IaC tools, emphasizes the importance of modernizing IT management ahead of the anticipated increase in AI workloads.

As AI capabilities continue to expand, the ability to centralize IT infrastructure management becomes increasingly critical. Many organizations are adopting platform engineering methodologies to achieve this goal, though the pace of these efforts may not match the rate of new application deployments. Without proper automation guardrails, IT teams risk falling behind, resulting in ongoing operational challenges.

Efforts to address workload management challenges at scale should be prioritized to avoid potential crises. Implementing robust automation strategies will be essential in managing the expected increase in AI-driven applications and workloads.