Operations | Monitoring | ITSM | DevOps | Cloud

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.

But migration is painful. Moving from Datadog means manually rebuilding dashboards, rewriting every query, and reconfiguring panels one by one. What took months to build takes weeks to migrate. Engineering teams get pulled away from actual product work to rebuild monitoring infrastructure they already had working. Critical monitoring setups and the context around why dashboards were built a certain way often get lost. We kept hearing about this from teams evaluating SigNoz, so we built a solution.

Teams deploying LLM applications face a critical blind spot: They can measure speed and cost, but not whether their AI is actually giving good answers. To build user trust in these applications, teams also need to measure response quality, including factual accuracy, safety, and tone. Operational metrics show how a system behaves, but not whether its responses are correct or on brand.

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.

Service-centric operations demand more than observability, they demand understanding. The Juneau release of ScienceLogic Skylar One brings that understanding into sharp focus with greater clarity, intelligence, and ease-of-use for the IT and service operations teams who keep modern digital businesses running. Engineering enhancements in this release of Skylar One (formerly SL1) make it even more accurate, more intuitive, and more aligned with the way operations teams actually work.

The holiday shopping season is a familiar ritual for many. We spend hours researching the best deals, comparing features, and reading reviews to make sure we’re investing in the right things. As we all come to grips with the fact that 2026 is right around the corner, engineering leaders are doing the same thing, but largely in response to the explosion of AI developer tools.