AI is Breaking Enterprise Log Management

Learn more about Dynatrace

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace.

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 the 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, VP 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.

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.

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AI is Breaking Enterprise Log Management

Learn more about Dynatrace

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace.

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 the 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, VP 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.

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.

Related Links

Hot Topics

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Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...