Operations | Monitoring | ITSM | DevOps | Cloud

Discover how Megaport and Wasabi are expanding cloud object storage to support AI workloads, enterprise data growth, and predictable costs.

Every SRE team operates services it cannot see into: a vendor binary, an inherited legacy deployment, a container whose owning team dissolved two reorgs ago. The routes are undocumented, the dependencies are unknown, and when a request takes 130 milliseconds nobody can say whether that time is application work or a wait across a network boundary.

If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.

A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.

You shipped a skill. It worked. You closed the tab. That’s the whole problem. Model choice is a decision you make once, at the moment you’re least equipped to make it: before the skill is even authored. Then you never revisit it, because the skill stopped being interesting the day you got it working. So go back and check. Here’s how.