Operations | Monitoring | ITSM | DevOps | Cloud

(Almost) everyone is using generative AI, and just as many aren’t seeing any benefits. Research firm Gartner calls it the “gen AI paradox” — nearly 80% of companies say they’ve invested in generative solutions, and the same number report no benefits to their bottom line. What’s more, 90% of projects are stuck in pilot mode; ready to take off, but just can’t get up to speed.

FinOps thrives on clarity, and clarity is built on metrics. Metrics give engineering and finance a shared language to understand costs, evaluate trade-offs, and guide innovation. The most impactful metrics go beyond “how much are we spending?” and help us answer: When we measure these things, we stretch beyond tracking progress to fueling it.

Modern engineering teams are shipping more services, managing more complex infrastructure, and moving faster than ever. But this velocity often comes at a cost to the developer experience. Engineers are frequently bogged down by infrastructure complexity, inconsistent tooling, and a lack of clear standards, which leads to cognitive overload and slower cycle times.

This month’s DataStream update brings meaningful improvements across pipeline management, MSSP workflows, and endpoint visibility. We’ve focused on giving security teams more control over how data moves through their environment, expanding coverage for both Windows and Linux, and strengthening governance for multi-tenant deployments. Let’s walk through what’s new.

Every year, KubeCon offers a candid look at where the cloud-native community stands — the tools gaining traction, the pain points teams share, and the big gaps still holding organizations back. After a week of deep conversations, session hopping, and talking to dozens of platform teams, one theme became impossible to ignore: Platform engineering still isn’t doing enough testing. And even more surprising: many teams don’t think testing is their responsibility.

Since the publication of the inaugural AI Sector Study in 2022, the UK’s AI ecosystem has grown to include more than 5,800 companies – an 85% increase over the past two years. AI revenue is now £23.9 billion, and the sector employs more than 86,000 people. To put that in context, it’s bigger than the UK gambling sector – on both counts. Digital infrastructure is the foundation of this new economy.

Read a few articles on artificial intelligence and financial operations, and you’re bound to run across a sentence like this: AI enables FinOps teams to reduce TCO and boost ROI. Or one like this: The future of FinOps uses agentic AI-powered systems to detect and remediate cost issues automatically. Keep reading and you’ll find piece after piece that say a lot about AI and FinOps … without really saying anything.

Information architecture used to be about structure. We organized menus and pages into trees, built hierarchies, and created pathways for people to follow. For years, that worked. Navigation was the interface. But that world is changing. People aren’t clicking their way through information anymore. They’re asking for it. They’re refining questions, expecting context, and assuming that systems will not only understand what they mean, but act on it.

Large enterprises today manage more machine data than ever before. From legacy applications to modern, ERP and supply chain systems to cloud infrastructure, cybersecurity, and customer-facing applications, much of this valuable data remains trapped in silos, limiting its potential to drive faster decisions, strengthen resilience, and meet the demand for optimum service availability.