Operations | Monitoring | ITSM | DevOps | Cloud

Software moves from code to production more quickly today, but it is still difficult to tell whether delivery is actually improving or just becoming more active. Most teams rely on dashboards filled with metrics like deployments, uptime, failures, and tickets. The numbers are available, but the meaning behind them is often unclear. DevOps metrics become useful only when grouped into clear categories: DORA metrics cover only delivery speed and stability, which is just part of the picture.

ServiceNow's internal IT operations now resolve more than 90% of employee requests through autonomous agents. The platform that demonstrated this in Las Vegas earlier this month is the same platform sitting in your environment right now. So why isn't your operation running the same way? This is the question every CIO should have walked out of Knowledge 2026 with.

Brandon Woods, Keysight Technologies, on the Difference Between a DEX-Powered IT Agent and a Generic AI Assistant “Spark’s real power is the Nexthink data behind it.

Stay ahead of printer supply shortages, expand software inventory coverage, and maintain more accurate inventory records.

The board approved the Agentforce roadmap. The CFO signed off on the consolidation business case. The Salesforce ITSM migration is the dependency that makes both commitments real, and it is green on the current status report. It is almost always green, right up until the quarter it isn't.

What keeps systems secure in real IT environments, applying fixes quickly or knowing what needs attention first? Most IT teams do not struggle because they lack tools or processes. They struggle because two critical functions are often mixed together. Patch management and vulnerability management. This creates a gap between what is being fixed and what actually needs to be fixed. The challenge is that teams deal with constant alerts, regular updates, and growing security risks.

How do you know if your network is actually healthy right now? For most IT teams, answering that question means jumping between multiple tools, dashboards, and alerts, only to end up with more uncertainty than clarity. The problem is not missing data. It is knowing which signals matter, what normal really looks like, and when performance issues start affecting users and business operations. Modern networks generate thousands of metrics every minute, but not every spike or alert deserves attention.

Security teams are drowning in vulnerabilities. We’re talking tens of thousands of findings per quarter. Hundreds of thousands at larger organizations. Today's IT environments have no boundaries and span across every OS platform. Managing and securing that estate in a linear fashion is no longer viable, and neither is a vulnerability remediation process that treats every fix as a simple, low-impact task.

Is your monitoring stack really giving you clarity, or just more alerts? Your monitoring stack is probably working exactly as designed. That is the problem. As systems grow, most IT and platform teams start to see the same patterns: At this point, traditional monitoring starts to feel limited. This is where teams begin exploring AI in observability. In this guide, we will explain what AI-powered observability actually means, how it works, and when it is useful.