Operations | Monitoring | ITSM | DevOps | Cloud

DORA's data shows that as AI adoption goes up, individual effectiveness rises, and so does software delivery instability: more rollbacks and more unplanned rework. Nathen Harvey of Google's DORA team explains why AI acts as an amplifier of whatever system you already have. He walks through the seven capabilities that separate teams getting real gains from teams drowning in downstream chaos. He also argues that the risks stopping you from shipping AI-built work should become your platform roadmap.

AI can write 1,100 lines of code for a loading spinner in five minutes. Your change board still meets once a week. Karthik Jayaraman, VP of Information Technology at Fiserv, explains what happens when code generation speeds up and everything downstream stays the same. He covers why "human in the loop" doesn't scale, why handing all review to AI backfires, and why an agent told not to touch production needs a boundary it can't cross, not just an instruction.

Turns out telling coding agents what to do over and over again doesn't actually make them better at fixing CI failures.

Onboarding a new client shouldn't mean rebuilding the same dashboards by hand. Live demos show how our MCP server (in preview) hands that work to AI, built for MSPs, but relevant to any team automating dashboards and reporting.