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2025 State of Observability: Business Leaders Are Seeking New Ways to Drive AI Value and Trust

While AI adoption is accelerating, concerns about reliability and trust make it challenging to transition initiatives from concept to production, according to the 2025 State of Observability Report from Dynatrace.

To address this, business leaders are prioritizing observability solutions to scale their AI projects, with more than two-thirds (70%) saying observability budgets have increased in the past year.

Key findings from the report include:

AI adoption

  • 100% of business leaders surveyed are using AI as part of their operations today. Top AI use cases include data management (57%), AI governance (50%), and security operations (46%).
  • AI use cases such as sustainability (27%) and log management (29%) present exciting opportunities for organizations to expand adoption and unlock greater efficiency and ROI.
  • The two major categories where business leaders anticipate AI-powered automation delivering significant value are real-time detection of and response to security risks (37%) and anomaly detection (41%).

AI governance, trust and security

  • One in four business leaders believes improving AI governance and trust should be their highest priority.
  • For leaders in charge of data governance, their top two areas of concern with AI reliability are related to data quality and predictability (50%) and data privacy (45%).
  • More than two-thirds (69%) of AI-powered decisions still include human-in-the-loop processes to verify accuracy.
  • Nearly all (98%) business leaders reported using AI to manage security compliance in some capacity, with a combined 69% seeing increased budgets for AI-powered threat detection in the past year and expecting budgets to increase next year.

“Enterprise IT software and applications must evolve from simply adding AI to existing systems toward building truly AI-native experiences,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “This shift introduces new challenges for observability, as organizations must ensure their AI-driven systems are transparent, reliable, and scalable. Observability becomes the critical foundation, providing the shared intelligence needed to navigate these challenges, make smarter decisions, and drive safe, efficient automation at scale.”

Additional Report Findings

  • More than 50% of business leaders see automated real-time observability solutions to enhance customer experience within the next year.
  • 46% of business leaders anticipate the greatest ROI of AI-powered observability will come from optimizing AI model configurations.
  • By 2030, 50% of business leaders expect to have adopted AI-powered data encryption, risk assessment, and threat detection capabilities.
  • 70% of those surveyed say observability budgets have increased in the past year, and three-quarters (75%) expect budgets to increase in the next fiscal year.

“Observability is shifting from reporting telemetry about application health to informing the decisions that run the business,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “As more of those decisions are supported by AI, observability becomes the key to unlocking the full potential of AI‑driven decision support, providing the trustworthy context, guardrails, and feedback loops leaders need to act with confidence at scale.”

Methodology: This report is based on a global survey conducted by Qualtrics and commissioned by Dynatrace of 842 CIOs, CTOs, and other senior technology leaders involved in IT operations and DevOps management in large enterprises with an annual company revenue greater than or equal to $100M USD. The sample included 206 respondents in the US, 125 in Germany, 129 in France, 130 in Spain, 128 in Italy, and 124 in Japan.

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AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

2025 State of Observability: Business Leaders Are Seeking New Ways to Drive AI Value and Trust

While AI adoption is accelerating, concerns about reliability and trust make it challenging to transition initiatives from concept to production, according to the 2025 State of Observability Report from Dynatrace.

To address this, business leaders are prioritizing observability solutions to scale their AI projects, with more than two-thirds (70%) saying observability budgets have increased in the past year.

Key findings from the report include:

AI adoption

  • 100% of business leaders surveyed are using AI as part of their operations today. Top AI use cases include data management (57%), AI governance (50%), and security operations (46%).
  • AI use cases such as sustainability (27%) and log management (29%) present exciting opportunities for organizations to expand adoption and unlock greater efficiency and ROI.
  • The two major categories where business leaders anticipate AI-powered automation delivering significant value are real-time detection of and response to security risks (37%) and anomaly detection (41%).

AI governance, trust and security

  • One in four business leaders believes improving AI governance and trust should be their highest priority.
  • For leaders in charge of data governance, their top two areas of concern with AI reliability are related to data quality and predictability (50%) and data privacy (45%).
  • More than two-thirds (69%) of AI-powered decisions still include human-in-the-loop processes to verify accuracy.
  • Nearly all (98%) business leaders reported using AI to manage security compliance in some capacity, with a combined 69% seeing increased budgets for AI-powered threat detection in the past year and expecting budgets to increase next year.

“Enterprise IT software and applications must evolve from simply adding AI to existing systems toward building truly AI-native experiences,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “This shift introduces new challenges for observability, as organizations must ensure their AI-driven systems are transparent, reliable, and scalable. Observability becomes the critical foundation, providing the shared intelligence needed to navigate these challenges, make smarter decisions, and drive safe, efficient automation at scale.”

Additional Report Findings

  • More than 50% of business leaders see automated real-time observability solutions to enhance customer experience within the next year.
  • 46% of business leaders anticipate the greatest ROI of AI-powered observability will come from optimizing AI model configurations.
  • By 2030, 50% of business leaders expect to have adopted AI-powered data encryption, risk assessment, and threat detection capabilities.
  • 70% of those surveyed say observability budgets have increased in the past year, and three-quarters (75%) expect budgets to increase in the next fiscal year.

“Observability is shifting from reporting telemetry about application health to informing the decisions that run the business,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “As more of those decisions are supported by AI, observability becomes the key to unlocking the full potential of AI‑driven decision support, providing the trustworthy context, guardrails, and feedback loops leaders need to act with confidence at scale.”

Methodology: This report is based on a global survey conducted by Qualtrics and commissioned by Dynatrace of 842 CIOs, CTOs, and other senior technology leaders involved in IT operations and DevOps management in large enterprises with an annual company revenue greater than or equal to $100M USD. The sample included 206 respondents in the US, 125 in Germany, 129 in France, 130 in Spain, 128 in Italy, and 124 in Japan.

Hot Topics

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...