AI-driven telemetry surges 93% as enterprises grapple with fragmented tools, rising costs, and growing observability challenges
As enterprises accelerate AI adoption, traditional log management systems are increasingly failing to keep up with the volume and complexity of AI-generated telemetry, according to a new global study released by Dynatrace.
The company’s latest State of Log Management 2026 report reveals that AI workloads have driven a 93% increase in log and telemetry volumes over the past year, creating significant operational and financial challenges for organizations attempting to scale AI initiatives.
Based on a survey of 450 senior technology leaders worldwide, the research highlights a growing observability crisis as businesses struggle to extract meaningful insights from massive data volumes while controlling costs and maintaining system reliability.
According to the study, organizations now rely on an average of seven different tools to manage logs and telemetry, forcing IT and security teams to manually correlate information across multiple platforms. This fragmented approach is slowing incident detection, complicating AI governance, and delaying production deployments.
The findings reveal that 80% of organizations believe difficulties in converting telemetry into actionable insights are negatively impacting customer experience and hindering AI projects. At the same time, enterprises spend nearly $2.5 million annually on logging solutions, including data ingestion, storage, indexing, and querying.
To manage escalating costs, many organizations are making difficult compromises. Nearly half of respondents reported discarding or not collecting portions of their telemetry data, with businesses excluding an average of 86% of log data from analysis and storage due to cost and system limitations.
“AI is accelerating innovation, but legacy log management systems are struggling to keep pace with the scale and complexity of modern AI environments.” – Mala Pillutla, Vice President of Log Management, Dynatrace
“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. “To make AI systems reliable and trustworthy, organizations need a unified, intelligent approach that brings all telemetry together in real time, enriched with deep context to drive confident decisions.”
The report indicates that enterprises are increasingly recognizing the need for a platform-based observability strategy. Nearly three-quarters of respondents said AI workloads now require a unified approach to log management, while 81% believe log ingestion and processing must become more open and automated to enable real-time analysis without the limitations of traditional architectures.
Dynatrace argues that unified observability platforms, which combine logs, metrics, traces, and events into a single intelligent framework, are becoming essential for organizations seeking to operationalize AI at scale.
As enterprises move beyond AI experimentation toward production deployments, the study suggests that observability modernization will become a critical success factor in ensuring AI reliability, governance, performance, and business value.
