5 challenges to achieving observability at scale

As AI spreads through operations, observability must become a strategic control plane, because more telemetry rarely means better decisions. The paper explores five challenges:

Data overload, although 57% of technology leaders already use AI for data management
Siloed teams and fragmented data, which erode trust in AI insights
Proving business impact, since only 28% use AI to align observability data with KPIs
Opacity in AI-driven systems, where 69% of agentic AI decisions are still verified by humans
Automation that acts without context, so failures cascade

Its conclusion: a unified data layer, real-time context graph and autonomous operations system turn complexity into advantage.

8681-5 challenges to achieving observability at scale

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