Operations | Monitoring | ITSM | DevOps | Cloud

The quick download: AI reduces MTTR by helping teams detect issues sooner, pinpoint root causes faster, and resolve incidents with less manual effort. IT downtime costs organizations an average of $9,000 per minute. AI-powered observability can cut incident resolution time by up to 70%. Here’s what it takes to get there. Every minute an incident goes unresolved, the meter is running.

November 24th, the Opus 4.5 release turned around the entire tech industry. This was the moment when agents became capable. Capable enough to write solid staff-level code. Capable enough to reason about alerts, investigate root causes much faster than most engineers, and set up the reliability layer faster. For me, this feels like an iPhone moment on steroids; the adoption of AI is accelerating much faster than any adoption curve I’ve seen over the past few decades.

Back in 2016, Jonathan Ross founded Groq, the AI chip startup, which went on to enter a non-exclusive licensing agreement with NVIDIA for Groq’s inference technology (as part of a $20 billion deal). The name ‘Groq’ is commonly confused with X (formerly Twitter)’s Grok, which was launched in 2023 as a Gen AI chatbot. As demand for real-time AI continues to grow, inference has become one of the most important and expensive parts of the machine learning lifecycle.

Since the advent of Artificial Intelligence (AI), it has become the buzzword for modern day businesses. It has tremendous benefits which has lured enterprises invest hefty money with a view of getting ahead of their competitors. Yet, many CIOs are still figuring out ways to get the best ROI of AI that resonates with their businesses. While there are many initial programs and proof of concepts that show promise, in the long run they fail to deliver their promise.