Operations | Monitoring | ITSM | DevOps | Cloud

The risk of headline-grabbing incidents, like Samsung’s ChatGPT data leak, related to AI usage outside of the authorization and control of IT (a.k.a. shadow AI) is clear. Most IT teams recognize that a high-profile incident can have serious repercussions. However, the risk of shadow AI goes well beyond the risk of a single incident. In fact, the recent Komprise IT Survey indicates that 79% of organizations have experienced negative outcomes from sending corporate data to AI.

AI coding assistants like Claude Code are becoming core parts of modern development workflows. But as with any powerful tool, the question quickly arises: how do we measure and monitor its usage? Without proper visibility, it’s hard to understand adoption, performance, and the real value Claude brings to engineering teams. For leaders and platform engineers, that lack of observability can mean flying blind when it comes to understanding ROI, productivity gains, or system reliability.

Why the traditional model of monitoring and manual operations is collapsing–and what enterprises must do to survive The digital universe is expanding at a pace no enterprise can keep up with through traditional methods. Dependencies pull at each other in ways even experts can’t predict. What once could be managed with dashboards and siloed monitoring tools has become too vast, too interdependent, and too fast-moving, a new operating model is needed to master such complexity.

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed. In the early days, public cloud services were the obvious choice.

Agentic AI has sparked a ton of excitement in IT. These intelligent agents can analyze signals, interpret requests, and recommend actions with surprising accuracy. But left on their own, they struggle to translate those insights into reliable execution. The end result is a fragmented picture of great thinking... but limited doing. This is why orchestration matters.