Jamf has released findings from a survey of 687 IT and security leaders that reveal a growing challenge for organizations adopting AI: as might be expected, the more deeply AI becomes embedded into daily work, the more AI-related incidents occur.
The survey found that 72.9% of organizations have already deployed AI in some form. But adoption isn’t reducing risk. Organizations with deeply integrated AI are 40% more likely to report an AI-related incident than organizations still in the exploratory stage. There is also a widening visibility gap: as AI deployment deepens, the ability to see and govern what is running tends to fall behind.
More than one in five respondents (22.0%) said that their organization has already experienced an AI-related incident involving unexpected costs, a security issue, or both. Another 59.7% said they view an AI-related incident as a near-term risk.
The findings suggest that AI governance is quickly becoming an operational requirement rather than a future planning exercise.
When asked about their top AI priorities for the next 12 months, respondents identified three clear areas of focus:
- Automating IT operations (44.4%)
- Deploying AI productivity tools (41.0%)
- Establishing AI governance (36.7%)
Four challenges continue to surface
Across 178 open-ended responses, IT and security leaders described many of the same obstacles:
- Shadow AI remains a persistent concern as employees adopt AI tools without formal approval, often leaving IT teams with little visibility into how data is being used.
- Agentic and developer AI introduce new governance challenges through command-line tools, IDE extensions, embedded models, and autonomous workflows that traditional monitoring tools often miss.
- Vendor sprawl continues to accelerate as software providers rapidly add AI capabilities to existing products, increasing the number of tools organizations must evaluate and govern.
- Cost management remains difficult as usage-based pricing and overlapping subscriptions make it harder for organizations to understand where AI spending is occurring and which tools are delivering value.
