Operations | Monitoring | ITSM | DevOps | Cloud

As enterprise IT environments evolve into ever-greater complexity and scale, demands on operations teams are accelerating. In the traditional model, observability tools collect data, engineers manually correlate events, and remediation follows a ticketing trail. However, that approach no longer matches the speed and scale of today’s digital businesses. Even the most storied dashboards can’t address today’s operational needs.

You don’t need to be a power user to get powerful results. I’m not training models or prompting GPTs into poetry—I’m just using them to do what great managers already try to do: communicate clearly, prioritize outcomes, and lead with intention. Over the last few quarters, I’ve built a handful of custom GPTs to support my weekly, monthly, and quarterly workflows.

Developers have long used profilers to diagnose performance bottlenecks and improve the efficiency of their code. But a modern version of profiling, continuous profiling, is quietly redefining what profiling is and what it can do. By running nonstop in production with very low overhead, continuous profilers give teams always-on visibility into how their code behaves in the real world.

Great data is a prerequisite to all things AIOps and observability. Great observability data results in fewer observability gaps, better analysis and insights, and more confidence within teams that rely on the power of modern AIOps and observability technologies. Goals for improved automation, IT efficiencies, intelligent triage and remediation all become more achievable with better data.

When you manage complex IT infrastructure, it becomes critical to use tooling to understand what’s happening across all of your systems in terms of performance, reliability, and compliance. Monitoring key indicators manually is simply no longer possible at that scale. Puppet has long been known as a solution for managing large environments and collecting a vast amount of data about your infrastructure, but accessing and visualizing that data in a meaningful way can be a challenge.

If you’re unfamiliar with AWS Summits, these are conferences that occur on a yearly basis in different cities. The events are mostly used to announce new products and technologies. This year, the theme was AI, as evidenced by the keynote, a large majority of the talks, and a walk around the vendor floor. The keynote talk was hosted by Swami Sivasubramanian, VP of Agentic AI at AWS.

AI is being used to automate just about everything these days, from writing code to making coffee. Observability is no exception. But before we dive into how AI can actually help, it is worth stepping back to look at what already works, what does not, and where the real gaps are.

You’ve built something amazing. Your services are scaling, your users are happy, and your team is shipping code like never before. Then the cloud bill arrives, and one line item makes your eyes water: observability. That Datadog invoice feels less like a utility bill and more like a ransom note. It’s a modern engineering paradox. The tools that give you sight into your complex systems are the same ones that can blind you with runaway costs.

SAP Customer Experience (CX) has undergone a remarkable transformation over recent years, evolving from fragmented monitoring to a scalable, automated observability powerhouse. In a recent fireside chat, Martin Norato Auer, SAP CX’s VP of Observability, shed light on the strategies, practices, and measurable impacts behind SAP’s SLA, uptime, and responsiveness achievements.