New research released by Dynatrace suggests enterprise log management practices are struggling to keep pace with the growth in AI-related workloads, with organisations reporting sharp increases in log volume, tool sprawl and rising costs.
The study, titled The State of Log Management 2026, is based on a survey of 450 senior leaders responsible for log management at enterprises with annual revenues of US$750 million or more. The research was conducted by Coleman Parkes on behalf of Dynatrace in January and February 2026.
According to the report, respondents estimate they spend nearly US$2.5 million annually on logging solutions, including ingestion, management, storage, indexing, rehydration and querying. Despite that spending, organisations said they exclude an average of 86% of log data from ingestion, storage or analysis to manage costs and system limitations.
The report also found AI workloads have driven a 93% increase in log volume over the past 12 months, while organisations use an average of seven tools to manage logs and telemetry. Dynatrace said the combination of higher data volumes and fragmented tooling is increasing reliance on manual correlation across systems, slowing the ability to detect issues, secure AI systems and extract timely insights.
In the survey, 80% of respondents said turning telemetry into actionable insights is negatively affecting customer experience and delaying AI initiatives. Nearly three-quarters said AI workloads require a platform-based approach to log management, while 81% said log ingestion and processing must be open and automated to support real-time analysis.
“AI is accelerating enterprise innovation, but most logging systems were never built for the scale, speed, or complexity of AI‑driven environments,” said Mala Pillutla, Vice President of Log Management at Dynatrace. “As AI agents operate probabilistically, treating logs, metrics, traces, and events as separate signals is no longer viable. To make AI systems reliable and trustworthy, organisations need a unified, intelligent approach that brings all telemetry together in real time, enriched with deep context to drive confident decisions.”
The report argues that as AI initiatives move from pilots into production, limitations in traditional and fragmented logging approaches can become a barrier to reliability and operational scale. It also says about a third of organisations are paying for redundant or underused observability features, while more than a quarter are spending engineering effort maintaining multiple tools across environments.
