Among the expected business benefits of AI-driven network management are:
- Faster resolution of network problems: 54.1%
- Improve network performance/experience: 51.3%
- Reduced security risk: 48.7%
- Cost optimization: 47.8%
- Proactive problem prevention: 45.9%
- More time available for strategic projects: 41.9%
- Responsiveness to change: 37.8%
- Mitigation of network team’s skills/personnel gaps: 33%
“I think AI is going to help us respond to incidents quicker,” said a network infrastructure and operations manager with a Fortune 500 energy company in the EMA report. “It will help us diagnose yellow flags before they turn into red flags. And it will help us reduce our self-inflicted outages.”
Not ready for fully autonomous operations
Still, EMA found that only 35% of enterprises are completely successful with applying AI to network management. Organizations relying on simple interfaces or loosely integrated features are seeing less impact than those embedding AI deeply into workflows and decision-making processes. And just 31% of IT professionals say they fully trust the outputs of their AI tools.
The research also found that human oversight remains critical. In related research from EMA, 63% of organizations said they require human approval for AI-driven automation, which highlights continued reliance on “human-in-the-loop” models, according to McGillicuddy.
“No one’s quite ready for autonomous operations, but human in the loop, definitely, maybe human out of the loop in the future for certain things. These are the top barriers to autonomous agentic IT ops: layering humans and systems and processes together, developing policies and guardrails for compliance and data security, overcoming mistrust and fear, resource gaps and budget and skills gaps, and then establishing roles and responsibility for introducing into production,” McGillicuddy said.
