As companies race to deploy AI, a less visible challenge is emerging: the systems meant to monitor artificial intelligence are struggling to keep up

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Modern organizations invested heavily in logging systems, monitoring tools, security platforms, and observability software designed to track every application, server, transaction, and digital interaction. The assumption was simple: if something broke, the data would reveal why.

Then artificial intelligence arrived.

Not as another application layered onto existing infrastructure, but as an entirely different computing paradigm. AI systems generate vast streams of telemetry data, operate probabilistically rather than deterministically, and often require organizations to understand not just what happened, but why a machine reached a particular conclusion.

Suddenly, the digital breadcrumbs enterprises relied upon became a flood.

New research from Dynatrace suggests many organizations are now confronting an unexpected consequence of AI adoption. The challenge is no longer simply building AI models or deploying AI agents. It is managing the enormous volume of data required to understand, secure, govern, and trust them.

The result is a growing observability crisis that could determine which organizations successfully scale AI and which remain trapped in perpetual experimentation.

The hidden infrastructure behind trustworthy AI

Much of the public conversation around artificial intelligence focuses on models, algorithms, and business outcomes.

Less attention is given to the infrastructure required to make those systems dependable once they enter production environments.

Every interaction with an AI system generates information. User prompts, model outputs, performance metrics, security events, application traces, infrastructure signals, and operational logs all contribute to a constantly expanding record of activity.

For technology teams, these records are not optional.

They are essential for understanding whether AI systems are functioning correctly, complying with regulations, avoiding security vulnerabilities, and producing reliable results.

Without visibility into these signals, organizations lose the ability to explain how AI-driven decisions were made or identify when systems begin to drift from expected behavior.

According to Dynatrace's State of Log Management 2026 report, AI workloads have increased enterprise log volumes by 93 percent over the past year. At the same time, organizations report using an average of seven separate tools to manage logs and telemetry.

Those two trends are colliding in ways that many enterprises did not anticipate.

As data volumes surge, the tools designed to process that information are becoming fragmented, expensive, and increasingly difficult to manage.

When more data creates less clarity

Conventional wisdom suggests that more information should lead to better decisions.

In practice, the opposite is often true.

The Dynatrace research found that 80 percent of organizations believe the process of converting telemetry data into actionable insights is negatively affecting customer experience and delaying AI initiatives.

The problem is not a lack of data.

It is an inability to make sense of it quickly enough.

Many enterprises now find themselves navigating multiple dashboards, disconnected analytics systems, and siloed monitoring environments. Engineering teams often spend significant amounts of time correlating information across platforms before they can identify the root cause of an issue.

That complexity becomes even more problematic when AI systems are involved.

Unlike traditional software, AI applications frequently produce outcomes that cannot be traced through a simple sequence of predetermined rules. Understanding their behaviour requires richer context and broader visibility across entire technology ecosystems.

As organizations expand AI deployments, operational teams are increasingly discovering that legacy approaches to log management were designed for a different era.

The rising cost of visibility

The financial implications are becoming difficult to ignore.

Dynatrace's research estimates that organizations spend nearly US$2.5 million annually on logging solutions, covering ingestion, storage, management, indexing, querying, and related operational requirements.

Yet despite those investments, many organizations cannot afford to retain all the data they generate.

Nearly half of surveyed enterprises report discarding or failing to collect significant portions of their log data. On average, organizations exclude 86 percent of logs from ingestion, storage, or analysis due to cost pressures and system limitations.

That statistic highlights one of the most significant tensions emerging in enterprise AI.

Businesses need more visibility than ever before to validate AI-driven outcomes. Yet the volume of telemetry being produced is forcing many teams to deliberately reduce the amount of information they retain.

In effect, organizations are paying more while seeing less.

The consequence is not simply higher infrastructure spending.

It is the possibility of missing the very signals needed to identify operational risks, performance issues, security incidents, or compliance concerns within AI environments.

Why observability is becoming an AI strategy

Historically, observability was viewed as a technical discipline.

Today, it is increasingly becoming a business issue.

Across industries, executives are under pressure to demonstrate measurable returns on AI investments. Pilot projects are no longer enough. Boards, shareholders, and customers want to see production-scale deployments delivering tangible value.

That transition from experimentation to operational reality is where many organizations encounter friction.

"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 report notes.

The findings suggest that roughly a third of organizations are paying for redundant or underutilized observability capabilities, while more than a quarter are dedicating engineering resources to maintaining multiple monitoring environments.

For enterprises attempting to scale AI, these inefficiencies create a significant obstacle.

Engineering talent that could be improving models, refining customer experiences, or developing new AI applications is instead focused on managing fragmented technology stacks.

A shift toward unified visibility

The research points toward a broader transformation occurring within enterprise technology.

Rather than treating logs, metrics, traces, and events as separate categories of information, organizations are increasingly looking to combine them into unified observability platforms capable of delivering contextual insights in real time.

Nearly three-quarters of survey respondents believe AI workloads now require a platform-based approach to log management. More than eight in ten say log ingestion and processing should be open, automated, and capable of supporting real-time analysis.

The goal is not simply to collect more information.

It is to create an environment where information becomes immediately actionable.

That shift reflects the realities of AI operations. Modern AI systems generate interconnected signals that often lose meaning when viewed in isolation. Bringing telemetry together provides a more complete picture of system behaviour, performance, and risk.

As enterprises continue to integrate AI across business functions, that holistic visibility is becoming increasingly valuable.

The next phase of enterprise AI

The first phase of the AI revolution was defined by access.

Organizations rushed to experiment with large language models, copilots, agents, and automation tools.

The next phase may be defined by control.

As AI becomes embedded in critical business processes, success will depend not only on model performance but also on an organization's ability to understand, monitor, govern, and trust these systems at scale.

That challenge is forcing a reassessment of technologies that have long operated behind the scenes.

"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."

For many enterprises, the future of AI may ultimately depend on a less glamorous question than which model they choose.

It may depend on whether they can see clearly enough to trust what those models are doing.

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