The conversation about AI in IT is shifting. No longer just a tool to optimize workflows, AI is quickly becoming a co-pilot in decision-making, automation, and even security. For IT teams, this marks a defining moment, not just in how they manage systems, but in how they help shape the future of business operations.
So what does this inflection point look like in practice?
As AI takes on more of the repetitive tasks that once consumed IT’s time, teams are moving beyond break-fix responsibilities and into roles that require orchestration and oversight. This includes defining automated workflows, managing access for non-human users, and aligning AI use with business and security priorities.
This shift is already underway. In JumpCloud’s recent IT Trends report, 42% of organizations say they plan to invest in AI-related IT tools within the next six months, and 77% expect to implement AI initiatives within the year.
How to adopt without overextending
With adoption accelerating, one of the most important questions facing IT leaders is pace. Move too fast, and you risk security gaps. Move too slow, and you miss opportunities for efficiency.
The recommendation? Start small. Begin with narrowly defined use cases—like automating ticket resolution or onboarding—and use those to build internal knowledge and trust. Measurable outcomes, such as reduced resolution times or fewer provisioning errors, can help validate next steps.
Security should always be the anchor. The report found that 67% of IT administrators believe AI is advancing faster than their organization’s ability to secure it. That’s not an argument against adoption, but a call for intentional governance.
Building teams for AI collaboration
Technical literacy is only part of the equation. Managing AI requires new skill sets, including data quality management, prompt engineering, and the ability to monitor and troubleshoot AI systems in production.
Equally important is the ability to collaborate. IT leaders will need to work across business units to identify the right problems for AI to solve and to ensure those solutions integrate with existing workflows.
Early AI wins are likely to come from operational areas where repetitive tasks are common. These include user provisioning and deprovisioning, common help desk queries, and automated threat detection. By automating these areas, IT teams can free up capacity for strategic work—like policy enforcement, compliance audits, and long-term infrastructure planning.
Rapid adoption demands strong governance. Organizations should implement clear frameworks for ethical AI use, data privacy, and model accountability. This includes the ability to detect bias, flag anomalies, and meet regulatory requirements. Without these safeguards, short-term gains can quickly become long-term liabilities.
This is more than a technological shift—it’s a leadership opportunity. The rise of AI challenges IT to evolve its role from system manager to strategic enabler. By adopting AI thoughtfully, focusing on practical use cases, and embedding governance from the start, IT can help lead the organization through this next wave of innovation.
Interested in learning more about how your peers are thinking about AI and other critical IT trends? Download JumpCloud’s full report here.
