Operations | Monitoring | ITSM | DevOps | Cloud

AI automation in ITOps fails because execution loses decision context, and context graphs turn incident history into durable execution memory that systems can actually reuse. AI automation for ITOps fails because it remembers what it did, but not why. Fixing an issue depends on what was tried last time, what failed, what worked, which exceptions were approved, and under what conditions. That information rarely lives in the system.

A VP of Engineering (from a company I’m not allowed to name) told me recently: "You helped us find and fix real user-facing issues. Now we need to convince our CTO why that matters more than the standard SLO’s and systems." Here's the thing: your CTO is not wrong in measuring the systems and basic uptime. That’s the baseline though. They’re all trying to watch everything, but they’re seeing nothing as it relates to users.

Most enterprises believe that once an asset is purchased and recorded, its value is automatically realized. In reality, the opposite is often true. Poor asset performance silently erodes budgets, reduces operational agility, and creates long term inefficiencies that remain hidden for years. Studies consistently show that nearly 30 percent of assets owned by large organizations remain unused or severely underutilized.

Your organization has invested heavily in monitoring tools for application performance, infrastructure monitoring tools for servers and databases, log monitoring tools, network monitoring tools, and third-party monitoring tools for specific services. But the actual problem is your IT team is drowning in that data. A single production issue generates 30+ alerts across applications, databases, servers, and monitoring tools, creating an alert flood that buries the actual problem.

In the previous post, we explored how AI accelerates delivery and compresses the time between change and user impact. As velocity increases, knowing that something has gone wrong before users do becomes a critical capability. But detection is only the beginning. Once alerts fire and dashboards light up, humans still have to interpret what’s happening, make decisions under pressure, and act.

India is no longer just a fast-growing cloud market; it is becoming a strategically vital one. What was once a race for cost efficiency and global hyperscaler expansion has evolved. Today, India’s cloud landscape is being reshaped by a new reality: the need for AI infrastructure, true data sovereignty, and the ambition to own its digital future. Following the discussion at Civo Navigate India 2025, one thing is clear: the status quo is shifting.

This guide covers some best practices that can make on-call handoffs a bit smoother. You’ll find suggestions on when to schedule handoffs, what to discuss during handoffs, and how to keep everyone updated on who’s currently on-call. Table of contents.

Harness AutoStopping helps FinOps teams eliminate up to 70% of idle cloud spend through intelligent, policy-driven automation. By automatically stopping and restarting unused resources without disrupting developers, organizations move from reactive cost reporting to continuous, proactive cloud cost optimization.

ITIL KPIs explained: how KPIs are defined in ITIL 4, what they measure, how to interpret them, and how AI enhances KPI insights.

Did you know the third week of January is the most common time for people to fail New Year’s Resolutions? It doesn’t matter whether it’s exercising more, learning a new language, or just trying to drink less coffee, that initial surge of fresh New Year’s energy is fading, and if you want to make a resolution stick, this is the key time to make a lasting change. The same is true with any reliability resolutions you might have made.