Operations | Monitoring | ITSM | DevOps | Cloud

IT help desk automation has evolved far beyond scripts and workflow triggers. It refers to intelligent, agentic systems that enrich, triage, diagnose, and recommend or execute actions before a human ever touches the ticket. Modern automation gathers the context engineers normally have to hunt for and presents issues as decision-ready cases. In IT, we love predicting the end of things: data centers, passwords, and yes, tickets. Zero Ticket IT often gets misunderstood in that same category.

There's a moment every engineering leader hits when implementing AI where they realize that no one really knows what they're doing. Not your competitors. Not the consultants. Not even the executives pressuring you to show results yesterday. Everyone is figuring this out in real time, and beneath the confident vendor pitches and LinkedIn thought leadership, the truth is messier than anyone wants to admit.

When the conversation turns to cybersecurity, people often think of firewalls, intrusion detection systems or state-of-the-art endpoint protection. Yet, beneath these sophisticated shields is an essential (and often unsung) foundation: robust IT Asset Management (ITAM). For CIOs guiding mid-sized and enterprise organizations through an increasingly perilous digital landscape, ITAM offers not just operational clarity, but a powerful first line of cyber threat defense.

FinOps teams have been expected to understand millions of dollars in cloud and AI spend using tools that a handful of (usually technical) specialists can operate. Dashboards, filters, exports, and SQL have been the norm. That era is over. CloudZero is now bringing AI directly into the FinOps workflow so anyone in the business can ask natural-language questions about cloud and AI spend, and get accurate answers back from the platform.

This blog is based on an Upsun livestream discussion featuring Guillaume Moigneu, Field Engineer, and Thomas di Luccio, Product Manager at Upsun. The conversation was moderated by Greg Qualls. We utilized AI tools for transcription and to enhance the structure and clarity of the content. When Black Friday approaches, the stakes are high for eCommerce businesses.

IT leaders want AI that moves the needle without blowing up risk, cost, or changing control. Your teams need a path to productize AI features on top of existing apps, connect safely to external models, and satisfy audit requirements without slowing delivery. Those are the core buying criteria we hear from IT middle management: buy over build, predictable outcomes, and a strong compliance posture.

Modern enterprises are generating more high-volume observability and security data than ever, which means the cost and complexity of getting analytics-ready data into Databricks are only growing. With the new Databricks Destination for Cribl Stream, organizations finally have a governed, scalable, and cost-efficient way to take full control of their data pipelines, accelerate AI-driven analytics, and unlock real business value from their Databricks investment.

Imagine sending a letter to your neighbour across the street, only for it to be routed through London or even Amsterdam before landing in their letterbox. This is effectively what happens to much of Scotland’s internet traffic. Despite physical proximity between users, businesses and services, digital data is frequently sent on needlessly long journeys, often leaving the country before reaching its destination.

Disclosure: This comparison is written by our product marketing team that works closely with IT operations and on-call workflows. While we build incident alerting software ourselves, this guide is designed to help teams understand how different tools fit different operational needs. We believe there is no single “best” tool. Only the right fit for a given team.

Traditional monitoring solutions provide valuable infrastructure metrics, they fundamentally lack the capability to understand what users actually experience. There is a significant technical gap between server-side metrics and client-side experience. Research shows that traditional monitoring fails to detect 52–68% of user-facing errors since they happen outside of the server infrastructure.