Operations | Monitoring | ITSM | DevOps | Cloud

You hear that? It’s the unmistakable, terrifying flood of tickets rolling in! Password resets, VPN issues, access requests, and performance alerts. The numbers climb faster than the team can respond. You’ve added automation, new tools, even a chatbot or two, but the tide surges on. Here’s a plot twist: sometimes, your IT service desk solution isn’t solving the problem. Sometimes, it’s the thing keeping the problem alive.

As security data volumes grow, so do the costs of processing and storing them. Microsoft Sentinel and other SIEM platforms charge based on data ingestion, which makes every decision about normalization rules critical and every duplicate log a direct expense. Enterprise-scale security data pipelines face a persistent problem: data duplication across normalization tiers. As logs move through multiple transformation stages, it’s often impossible to know in advance which version will succeed.

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.

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.

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.

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.

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.

Every developer who’s worked with PostgreSQL knows the pain: testing against a real database slows everything down. You need the database running locally, loaded with the right data, and configured to match production as closely as possible. Every time you run a new test or build, you’re forced to repeat that setup migrate schemas, seed test data, and clean everything up again. It’s time-consuming, brittle, and hard to scale across a team.

FinOps continues to grow in importance as organizations balance cloud services with on-prem systems, legacy applications, and evolving business demands. Many teams want to manage their costs more effectively but are unsure how to apply a FinOps strategy for hybrid IT outside the cloud.

As the year winds down, my inbox is overflowing with Black Friday offers and festive greetings. It’s that time when Thanksgiving and the run-up to December holidays remind us to pause and appreciate what truly matters. Yet, in my recent conversations with IT leaders in financial services, I’ve noticed something: the time and calm need to do this feels elusive.