AI is prompting IT teams to reimagine their core responsibilities. Instead of spending most of their time on hands-on, repetitive tasks, many professionals are now focused on overseeing and coordinating automated systems.

According to the SolarWinds IT Trends Report, 80% of IT professionals say their roles are shifting from operator to orchestrator. In practice, that means:

• Less time executing tasks manually (for example, routine troubleshooting or maintenance).
• More time governing systems and workflows that AI and automation execute on their behalf.
• More strategic and automation-driven work – 52% of respondents say their roles are becoming more strategic and automation-focused.
• More cross-functional collaboration – 47% report their roles are becoming more cross-functional as AI touches broader business processes.
• Increased complexity – 41% say their roles are becoming more complex as they manage AI-driven environments.

IT teams are also spending more time on proactive activities such as strategy and performance analysis, while some reactive tasks like incident troubleshooting are decreasing. In short, AI is helping IT move from “doing the work” to designing, supervising, and optimizing the work.

While AI is reshaping IT work, it also introduces a set of practical challenges around trust, governance, skills, and infrastructure.

Key challenges highlighted in the SolarWinds report include:

• Trust and verification
  • 71% of IT professionals say they need to double-check AI outputs.
  • 62% report difficulty trusting AI recommendations.
  • As a result, AI often adds a verification layer rather than fully replacing human judgment.
• More demanding roles
  • 71% say AI has made their roles more demanding, not less.
  • Time saved on manual tasks is offset by the need to manage risks, validate outputs, and oversee more complex systems.
• Readiness gap between leadership and practitioners
  • 47% of C-level leaders believe their organizations are extremely prepared for AI-driven change.
  • Only 13% of technical contributors feel the same, signaling a disconnect in expectations and readiness.
• Adoption barriers
  • About half of organizations have embraced AI (34% somewhat, 16% fully).
  • 37% report resistance, often due to infrastructure, budget, or complexity concerns.
  • Tool fragmentation and lack of integration also limit effective use.
• Skills, governance, and data quality
  • 56% say they need clearer AI policies and guardrails to adapt effectively.
  • 50% point to the need for formal AI training.
  • 83% say AI effectiveness depends heavily on the breadth and quality of available data.

Overall, AI is not making IT simpler; it’s making it more consequential. Teams that invest in governance, structure, and skills development are better positioned to trust and benefit from AI tools.

The report suggests that organizations need to rethink how they support IT teams as AI and automation become more central to operations. Over the next two to three years, 77% of respondents expect their organizations to become more proactive, driven by automation and data insights. To get there, several enablers stand out.

Practical steps include:

• Build clear AI governance and policies
  • More than half (56%) of respondents say clearer AI policies and guardrails would help them adapt.
  • Define where and how AI can be used, who is accountable, and how outputs are validated.
• Invest in skills and training
  • 50% of IT professionals highlight the need for formal AI training.
  • Training should cover not just tools, but also risk management, data literacy, and how to orchestrate AI-driven workflows.
• Prioritize data quality and integration
  • 83% say AI’s effectiveness depends on the breadth and quality of data.
  • Improving data pipelines, reducing tool fragmentation, and integrating systems are critical to getting reliable AI outcomes.
• Align leadership expectations with technical reality
  • With 47% of C-suite leaders feeling “extremely prepared” vs. only 13% of technical staff, organizations should close this gap through open communication and realistic roadmaps.
• Support the shift from operator to orchestrator
  • Redesign roles and performance metrics to recognize orchestration, governance, and cross-functional collaboration.
  • Encourage IT teams to spend more time on strategy, analysis, and proactive improvement, not just incident response.

Organizations that focus on governance, training, data quality, and realistic planning will be better equipped to reshape IT operations around AI while maintaining trust, control, and business value.