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MENAFN- Mid-East Info) AI workloads triggered a 93% surge in log and telemetry volume, while teams rely on an average of seven different tools, forcing manual correlation that doesn't scale
Dubai, UAE., June, 2026 – Dynatrace NYSE: DT, the leading AI-powered observability platform, today released findings from its new research, The State of Log Management 2026 report, revealing that the rapid growth of AI workloads is pushing traditional log management approaches to their limits. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools, making it harder for teams to keep AI systems explainable, trustworthy, and production ready. As a result, enterprises must rethink how they manage and analyze telemetry data to maintain visibility, control costs, and support AI at scale.
Key findings from the report include:
AI workloads have driven a 93% increase in log volume over the last 12 months.
Organizations use an average of seven different tools to manage logs and telemetry.
80% say turning telemetry into actionable insights is negatively impacting customer experience and delaying AI initiatives.
Organizations exclude an average of 86% of log data to manage costs and system limitations.
Teams spend nearly $2.5 million annually on logging solutions.
Nearly three-quarters say AI workloads require a platform-based approach to log management.
81% believe log ingestion and processing must be open and automated for real-time analysis.
According to a global study of 450 senior technology leaders, this surge in data, combined with fragmented tools, is making it increasingly difficult for teams to detect issues, secure AI systems, and extract timely insights. Organizations are forced into manual, time-consuming workflows as they compare insights across systems, slowing time to insight and limiting their ability to move AI initiatives from pilot to production.
AI growth pushes traditional log management to breaking point:
Respondents estimate they spend an average of nearly $2.5 million annually on logging solutions, including log ingestion, management, storage, indexing, rehydration, and querying. At the same time, logs are a key component for understanding and securing AI systems. To manage rising costs and system limitations using traditional methods, many organizations are forced to limit the amount of telemetry they ingest or retain.
Nearly half of organizations report discarding or not collecting logs, excluding an average of 86% of log data from ingestion, storage, or analysis to manage cost and system limitations. These challenges are most pronounced in environments that rely on fragmented or log-centric approaches, rather than a unified observability platform designed to handle AI-scale telemetry.
“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, organizations need a unified, intelligent approach that brings all telemetry together in real time, enriched with deep context to drive confident decisions.”
As AI initiatives move from experimentation to production, fragmented log management from too many tools is emerging as a key barrier to reliability, trust, and operational scale.
Unified observability becomes essential to scaling AI workloads:
The report underscores the need for a fundamentally new approach to log management, where logs serve as the high-fidelity foundation, unified with distributed tracing and other telemetry data to deliver real-time, context-rich insights at a massive scale.
Nearly three‐quarters of respondents say AI workloads now demand a platform‐based approach to log management, while 81% believe log ingestion and processing must be open and automated to enable real‐time analysis without rigid schemas, indexing overhead, or rehydration delays.
The real cost of observability fragmentation isn't just the infrastructure bill - it's the opportunity cost of AI initiatives that stall between pilot and production because teams can't trust their telemetry. The research shows that roughly a third of organizations are paying for redundant or underutilized observability features, and more than a quarter are burning engineering cycles just keeping multiple tools running across environments. That's capacity that should be going toward making AI workloads production-ready, not toward stitching together dashboards across numerous different tools.
Download The State of Log Management 2026 report here to explore benchmark data on how AI workloads are exploding log volume and costs, and why unified observability is now essential for reliable, trustworthy AI operations.
Methodology:
The State of Log Management 2026 report is based on a global survey of 450 senior leaders and decision makers responsible for log management in enterprises with annual revenues of $750 million or more. The research was conducted by Coleman Parkes on behalf of Dynatrace in January and February of 2026.
Resources:
The future of log management: New research reveals that AI workloads demand more from logs
About Dynatrace:
Dynatrace is advancing observability for today's digital businesses, helping to transform the complexity of modern digital ecosystems into powerful business assets. By leveraging AI-powered insights, Dynatrace enables organizations to analyze, automate, and innovate faster to drive their business forward.
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This press release includes certain“forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, including statements regarding Dynatrace's capabilities and platform and the expected current and future benefits to organizations from using the Dynatrace platform. These forward-looking statements include all statements that are not historical facts and statements identified by words such as“will,”“expects,”“anticipates,”“intends,”“plans,”“believes,”“seeks,”“estimates,” and words of similar meaning. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies, and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies, and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations, or strategies will be attained or achieved. Actual results may differ materially from those described in the forward-looking statements and will be affected by a variety of risks and factors that are beyond our control, including the risks set forth under the caption“Risk Factors” in our Annual Report on Form 10-K, subsequent Quarterly Reports on Form 10-Q, and our other SEC filings. We assume no obligation to update any forward-looking statements contained in this document because of new information, future events, or otherwise.
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