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Hybrid infrastructure and AI adoption lead to escalating operational complexity
Traditional observability models are no longer sufficient for operating AI-enabl…
AI observability emerges as an extension of traditional observability
AI monitoring lags AI adoption in depth and coverage
AI-specific capabilities drive vendor selection and switching
Enterprises plan to rapidly expand observability
Conclusion
Research methodology
Respondent demographics
Research Report: Unlocking Observability in the Era of AI and Application Modernization
Research Report
Mar 24, 2026
by
Torsten Volk, Emily Marsh
As enterprises increasingly adopt generative and agentic AI, hybrid multi-cloud ecosystems, and distributed application architectures, organizations’ needs for observability platforms capable of proactively detecting anomalies, managing AI model drift, ensuring data compliance, and streamlining operational complexity are quickly ramping up. IT teams now face significant challenges ensuring consistent performance, security, and reliability across legacy, modernized, and AI-driven applications deployed on premises, in multi-cloud environments, and at the edge. Monitoring AI models and agentic AI applications is now a priority to unlock the value within AI initiatives.
To gain further insights into these trends, Omdia surveyed 400 IT professionals and application developers involved with purchase and deployment decisions for observability tools and platforms at their organization.
Page Count: 23
Table of Contents
Executive summary
Report conclusions
Introduction
Research objectives
Research Findings
Hybrid infrastructure and AI adoption lead to escalating operational complexity
Traditional observability models are no longer sufficient for operating AI-enabled systems
AI observability emerges as an extension of traditional observability
AI monitoring lags AI adoption in depth and coverage
AI-specific capabilities drive vendor selection and switching
