Operations | Monitoring | ITSM | DevOps | Cloud

When we think about saving the planet, we usually imagine solar panels, electric cars, or governments making sweeping climate policies. Rarely do we picture rows of blinking servers in a data center or IT admins patching endpoints. Maybe we should. In today's world, the intersection between technology and sustainability is becoming impossible to ignore, and the IT industry is right at the center of it. The truth is, IT is both part of the problem and part of the solution.

Currently, you’ll hear a lot about Copilot, Microsoft 365 security, and modern workplace innovation. But here’s the question: Who’s making sure it all actually works, every day, without interruption?

AI adoption is progressing at a rapid pace. What started as a trickle of generative tools is now a flood of autonomous agents, custom copilots, and AI-powered SaaS, most of it entering the workplace faster than IT can keep track of.

Artificial intelligence (AI) is now embedded in everyday professional workflows — so much so that 46% of employees say they would continue using AI tools even if their organization banned them. The productivity gains are undeniable, but this widespread, unmonitored use of AI also introduces growing risks around data security, compliance, and governance.

Originally published on Medium, this piece by Winston Hearn dives into a philosophical discussion on why the "data is oil" metaphor is no longer serving the tech industry. Hearn argues that by reframing our thinking to "data is power," we can better understand and manage today's complex data systems. ‍ For more than a decade, we in the tech industry have referenced a common metaphor: data is the new oil. It’s a concept that’s easy to grasp.

Serverless platforms like Azure Functions and Azure Container Apps make it easier to scale your applications without managing infrastructure. But successful serverless apps require thoughtful planning. They must be designed to account for cold starts, unpredictable scaling behavior, and ephemeral compute lifecycles, all while ensuring secure data handling and end-to-end observability across highly distributed components.