In my opinion, AIOps can be quite reliable for performing root cause analysis, especially in large-scale environments where the volume of logs, metrics, and events makes manual investigation slow and error-prone, as it can quickly correlate signals, detect patterns, and narrow down the most likely source of an issue instead of just surfacing multiple symptoms. It significantly improves response time and helps reduce alert noise, making operations more efficient and focused. However, its reliability depends heavily on the quality, completeness, and consistency of the data being collected, because inaccurate or missing data can lead to misleading conclusions. Teams may also face challenges such as complex initial setup, the need for proper model tuning, integration with existing tools, and a shortage of skills that combine operations knowledge with data science. Additionally, relying too much on automation without human validation can sometimes miss important context, so the most effective approach is to use AIOps as a support system alongside human expertise rather than a complete replacement.