Observability Emerges as the Control Plane for Enterprise AI

AI adoption has reached near-universal levels across enterprises, but the way organizations operationalize AI varies sharply. As AI systems move from experimentation to business-critical deployment, observability is increasingly becoming the foundation that determines whether AI delivers value, remains compliant, and earns trust.

According to the report, 70% of organizations increased their observability budgets this year, underscoring a shift in priorities. This is not merely about monitoring infrastructure uptime anymore; it reflects the need for real-time visibility into complex, distributed AI systems that span data pipelines, models, applications, and users. Observability is evolving into a strategic capability rather than a technical afterthought.

One of the strongest signals of this shift is security and governance. Ninety-eight percent of organizations now use AI to support security and compliance, highlighting how AI-driven monitoring, anomaly detection, and policy enforcement are becoming essential in an era of expanding regulatory scrutiny and threat complexity. However, trust in automation remains calibrated rather than absolute—humans still verify 69% of AI decisions, especially in high-risk or regulated environments. This points to a hybrid operating model where AI accelerates insight, while humans retain accountability.

Despite heavy investment, a key gap remains. Fewer than 30% of organizations currently use AI to correlate observability data with business KPIs. This disconnect means many enterprises can see what their systems are doing, but not fully understand how AI performance translates into revenue impact, customer experience, or operational efficiency. Closing this gap represents the next frontier of AI maturity.

Notably, AI has become the number-one driver for selecting observability platforms, surpassing traditional factors like cost or basic monitoring features. Enterprises now expect observability tools to be AI-aware by design—capable of tracking model behavior, detecting drift, explaining outcomes, and aligning technical signals with business objectives.

In this context, observability is no longer just a toolset—it is the control plane for AI transformation, enabling scale, trust, and measurable business outcomes.