Your AI agents are only as trustworthy as your data.

AI agents are already resolving tickets, provisioning access, and reclaiming licenses, but many of the humans overseeing them can’t trust the data they’re acting on. So we brought together a group of IT experts to break down the asset data gap holding back AI-driven IT, and how IT teams are closing it.

When asset data is scattered across systems that disagree with each other, automation doesn’t stop. It just keeps running on the wrong information. That’s the gap this panel talks about.

In 48 minutes, a research analyst from IDC, a hands-on IT leader from Asana, and Oomnitza’s head of product cover what it takes to close it.

What you’ll learn:

• Why asset, endpoint, and configuration data quality is the biggest obstacle to reliable AI in IT service management right now.
• How a pre-IPO company moved from spreadsheets to a single source of truth for its asset data.
• Why you should validate data quality before buying new automation, not after.
• How accurate inventory tightens hardware refresh budgets, expands software asset management savings, and surfaces shadow AI usage.
• How trusted asset data produces cleaner SOX audits, more reliable automation, and better financial reporting.
• Why measuring end-user experience matters as much as measuring resolution speed (or more).