Hi ala,

New EMA research surveyed 458 IT professionals on AI-driven network management — and the results are a wake-up call for NetOps leaders.

Only 44 percent are fully confident their network data quality can support AI initiatives. Only 31 percent completely trust the insights their AI tools produce. And blind spots — assets that existing tools have never discovered — ranked among the top data quality challenges undermining AI success.

The problem isn't the AI. It's what the AI is working with.

In this webinar, Shamus McGillicuddy, VP of Research at EMA, joins Infoblox to unpack the research and explore what separates the 35 percent of organizations achieving complete AI success from everyone else.

You'll walk away with:

• The specific data quality gaps most likely to derail your AI and automation initiatives
• Why scanning-based discovery leaves 30-40 percent of assets undiscovered — and what that means for AI training data
• How authoritative DNS/DHCP-native asset intelligence closes the gap without new infrastructure
• Real-world examples of enterprises that found hundreds of unknown assets within weeks of deployment
• A framework for assessing your own network data readiness before your AI strategy hits a wall