State of Log Management in 2026

Modernizing logs to drive agentic AI innovation

We surveyed 450 global IT leaders to understand how AI workloads are reshaping log management—and why traditional approaches break under the weight of scale, cost, and complexity. The findings reveal why logs must evolve from raw records into a contextual foundation for trusted, AI‑driven operations. 

• AI has broken traditional log management. AI drove a 93% spike in telemetry volume in the past year, forcing organizations to shift.
• Sacrifice doesn’t scale. Even after filtering and aggregating to reduce volume, traditional logging tools force teams into compromises: 50% of organizations exclude 86% of logs on average to manage costs.
• Blind spots slow AI progress. 71% struggle to collect and correlate AI health metrics across sources, slowing decisions and delaying projects from moving to production.    
• AI demands more from logs. 84% say AI trust depends on log analytics that can predict and prevent problems, inspiring a shift to a unified, observability platform approach. 

Download the full report for benchmarks, trends, and guidance on modernizing log management for AI transformation.