Take a closer look at the role of AI in observability today, based on the results from the 2026 Observability Survey, the largest community-driven survey on the state of the industry.

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Survey data from over 1,300 observability practitioners reveals strong enthusiasm for AI in observability workflows, with 91-92% seeing value in anomaly detection, trend forecasting, root cause analysis, and dashboard generation. However, autonomous AI actions remain the most polarizing use case, with only 49% rating it as very valuable or critical and 15% expressing skepticism. The biggest blocker to AI adoption isn't fear but friction — specifically too much manual context input (cited by 26%). Accuracy and transparency are paramount, with 95% saying AI must explain its reasoning. Observability of LLM-based applications is growing fast, jumping from 5% production usage in 2025 to 14% in 2026. Notably, AI capabilities rank below cost, ease of use, and interoperability as tool selection criteria.

AI in observability in 2026: Huge potential, lingering concerns