Operations | Monitoring | ITSM | DevOps | Cloud

Power networks (DSOs, TSOs and generation) are under pressure from every direction. They need to improve reliability and sustainability, deliver real-time customer insight, and meet increasingly stringent regulations. In response, power generation has evolved from a simple centralized model, through to a decentralized model with generation from a mix of diverse sources such as centralized generation from carbon-based, nuclear and renewable generation plants, through DERs even located at people premises.

Our On-call product has a lot of great features: configuring escalation paths, viewing rotas and schedules, requesting cover, etc. However, when framing its reliability, we reduce it down to two critical pieces of functionality: It’s not that we’re happy if only these parts are working, but they are the most important parts. In this post, I'll go into more detail on how we think about their reliability.

AI coding tools made code generation faster. Measuring what actually ships is the hard part. Over the last eighteen months, tools like Cursor, Claude Code, Copilot, and Windsurf have fundamentally changed how software gets built. AI-generated pull requests are increasing, developers are producing more code than ever before, and workflows that once took hours now happen in minutes. But most organizations struggle to clearly explain what that investment is actually producing.

Gartner expects worldwide AI software spending to hit $2.59 trillion in 2026, 47% more than organizations spent last year. The dollars are real and growing fast. But most organizations still can't measure the ROI of that spend. The problem has two sides: developers and infrastructure. On the developer side, engineers are using AI to write nearly every line of new code, and leaders have no way to tell whether that spend is producing software that ships.

Companies are shipping AI features at a pace cloud teams have rarely seen. New agents, new copilots, new flows powered by language models, all moving from prototype to production in weeks. The spend that comes with it is real and accelerating, and most teams are seeing it on the invoice before they see it anywhere else. The question is no longer how much you're spending on AI. It's whether each dollar is producing a real outcome, and whether you can govern that spend before the next invoice arrives.

If your infrastructure is configured outside version control through dashboards, scripts, or manual steps, environment drift is the expected outcome. Most teams have lived this scenario. A feature works in staging but breaks in production. Two hours later, someone finds a configuration setting that was changed in staging three weeks ago and never documented.