Operations | Monitoring | ITSM | DevOps | Cloud

Only 14% of CFOs can prove AI ROI. OpenAI’s gross margin fell from 40% to 33% in 2025, well below its 46% target. Even the AI providers cannot reliably predict what AI will cost. Companies are scaling AI faster than they can measure it: more tokens, more agents, more model calls, more spend moving through systems finance cannot yet see. Every board is asking the same question: What is this AI investment returning? Most companies cannot answer it. The ones that can will compound their advantage.

Key Takeaway: Harness AI Test Automation now runs existing Playwright suites without code changes, adds AI-powered failure triage, and integrates test results directly into build and deployment pipelines. ‍

In The Five Pillars of AI Agent Accountability: A Diagnostic Framework for Engineering Leaders, we walked through each pillar of AI agent accountability (traceability, authorization provenance, identity and ownership, policy at scale, and human oversight) and argued that most enterprises today sit at Level 0 or Level 1 of the Accountability Maturity Model. The most common reaction we get when we share that framework is some version of: “We’re already covered. We have network policies.

Is your monitoring stack really giving you clarity, or just more alerts? Your monitoring stack is probably working exactly as designed. That is the problem. As systems grow, most IT and platform teams start to see the same patterns: At this point, traditional monitoring starts to feel limited. This is where teams begin exploring AI in observability. In this guide, we will explain what AI-powered observability actually means, how it works, and when it is useful.