Operations | Monitoring | ITSM | DevOps | Cloud

Security teams love the power of Google SecOps (formerly Chronicle)—fast detection, scalable infrastructure, and native integration across Google Cloud. But there’s a challenge most organizations still struggle with: the cost, complexity, and chaos of raw telemetry data. Security data doubles every 2-3 years. Without control and context, even the best SIEM platforms can be overwhelmed with noise. That’s where Observo AI comes in.

AI is changing how we build software — but debugging code still comes down to having context. One minute the model’s performance is cruising. The next, you’re hit with a KeyError from a tool you forgot existed, triggered by a model that silently timed out, and a retrieval call that returns... nothing, or 11 “Let me try this a different way" messages before failure. You’re stitching together LLM calls, agents, vector stores, and custom logic. Then hoping it holds up in prod.

On a recent episode of The Product Manager podcast, Cortex CEO Anish Dhar joined host Hannah Clark to challenge a growing narrative: that software engineering is obsolete in the age of AI. His take? Engineering isn’t disappearing, it’s maturing. At Cortex, we work with some of the most forward-thinking engineering organizations at companies like Canva and Fanatics.

Most IT leaders already understand that AI is the future of operations, but too many are still treating it like it’s so far off. The irony? Waiting is exactly what’s costing them the most. While businesses obsess over budget cuts, resource constraints, and service quality, one truth remains: delays in adopting AI for IT operations are compounding operational inefficiencies, inflating labor costs, and stalling digital progress. AI isn’t just about innovation; it’s about scale.