For years, observability helped engineering teams understand whether their systems were healthy. If an API failed, logs showed it. If latency increased, dashboards reflected it. If CPU, memory, or disk usage crossed a limit, alerts helped teams respond quickly. Traditional systems usually failed loudly. AI systems are different. They may not crash. They may not throw obvious errors. The API may respond on time, infrastructure may look stable, and dashboards may stay green. But the output can still be wrong. That is why AI observability needs to move beyond system monitoring. It needs to help teams understand decision quality. This idea was discussed in an AI ThoughtMakers episode on observability in AI from systems to decisions , where the conversation focused on how AI systems introduce new challenges around output correctness, cost, latency, governance, and decision drift. Traditional observability worked because failures were visible Traditional applications are mostly deterministic.…