Nearly half of IT leaders say AI-driven automation is their top investment priority for managing company devices, yet most organizations are still flying blind when it comes to controlling how employees actually use AI at work. That's the central finding from a new survey of more than 500 IT decision-makers at companies with 2,000+ employees, conducted by device management company Fleet.
The survey reveals a stark gap between ambition and readiness. While 46.5% of IT leaders ranked AI-driven automation as their top future investment priority - ahead of vulnerability remediation (42.5%) and device visibility (42.1%) - most IT teams lack the tools to manage the AI tools already in use across their organizations.
Shadow AI is already widespread
The scale of unsanctioned AI use is significant. According to the survey, 78% of employees are already using personal AI tools at work. Fleet's data shows the average enterprise runs 14 AI tools internally, but IT is only aware of four of them.
That visibility gap creates real problems. One company burned through its entire 2026 token budget in four months after rolling out an AI coding tool to 5,000 engineers. IBM's Cost of a Data Breach report found that breaches caused by shadow AI cost an extra $670,000 on average compared with a typical breach.
IT teams are being asked to manage security, costs, vulnerabilities, and training for AI tools on top of their existing responsibilities - without additional headcount. "IT has been given the mission to drive AI usage in a healthy, secure, and cost-effective way, but really lacks the tools to do it across their fleets," the report concludes.
IT teams are absorbing another responsibility
The situation echoes the mobility era, when IT had to learn enterprise Wi-Fi management on top of existing roles. AI presents a similar challenge, but with higher stakes: security risks, budget overruns, and compliance exposure all land on IT's plate.
For IT leaders looking to build practical skills in this area, structured training can help close the gap between what's expected and what teams know how to execute. An AI for IT Managers learning path covers the operational realities of managing AI across a fleet, from policy to tooling. Broader AI for IT & Development resources address the technical side of integrating AI into existing workflows.
Why this matters for IT managers
The Fleet survey points to a concrete problem: IT leaders are being held accountable for AI outcomes they can't see or control. The gap between the 14 AI tools running internally and the four IT knows about represents real financial and security exposure.
For IT managers, the takeaway is that shadow AI is not a future risk - it's a current operational reality. The teams that will succeed are the ones that stop treating AI adoption as an event and start building the visibility, policy, and telemetry infrastructure to manage it as an ongoing process. That work starts now, not after the next budget cycle.
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