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Majority of Organizations Cannot Realize Full Potential of Observability

Although 78% of organizations surveyed have an observability practice in place, 91% said they face challenges that prevent them from realizing the full potential of the systems they have already deployed, according to Observability and Demystifying AIOps, a report from Chronosphere and the Enterprise Strategy Group (ESG).

Scalability and reliability of the observability tools were cited as the top concerns.

Additional survey findings include:

Observability tool sprawl is expanding

A majority of organizations reported at least 6 different tools in use, with more than half (52%) using 11-20 different tools. The report also shows that 72% of organizations agree that the number of tools they use adds complexity.

Explosive observability data growth

Additionally, 69% of survey respondents reported that the most costly line item for most observability solutions, data storage, is growing. Respondents stated that "the amount of observability data is growing at a concerning rate" and one in five respondents reported that this explosive data growth was their top concern.

To address data growth, organizations are taking a mix of steps to rein in costs, including:

■ increasing storage spend (52%)

■ limiting the number of observed applications in their environment (44%)

■ limiting the number of observed metrics per application (43%).

Cloud is greatest Observability challenge

Many respondents also noted that applications deployed in the cloud were harder for them to monitor and few of them felt that their observability solution was helping them meet their availability goals.

When asked about their biggest observability challenges, 60% agreed that "Lack of visibility into our cloud applications makes achieving SLAs a challenge," and only 20% chose "Improved SLA Performance" as one of their monitoring/observability strategy's most impactful benefits.

Methodology: TechTarget’s Enterprise Strategy Group (ESG) surveyed 374 IT (58%) and DevOps/AppDev (42%) professionals responsible for evaluating, purchasing, managing, and using observability at large midmarket (500 to 999 employees) (11%) and enterprise (1,000+ employees) (89%) organizations in North America (US and Canada).

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Majority of Organizations Cannot Realize Full Potential of Observability

Although 78% of organizations surveyed have an observability practice in place, 91% said they face challenges that prevent them from realizing the full potential of the systems they have already deployed, according to Observability and Demystifying AIOps, a report from Chronosphere and the Enterprise Strategy Group (ESG).

Scalability and reliability of the observability tools were cited as the top concerns.

Additional survey findings include:

Observability tool sprawl is expanding

A majority of organizations reported at least 6 different tools in use, with more than half (52%) using 11-20 different tools. The report also shows that 72% of organizations agree that the number of tools they use adds complexity.

Explosive observability data growth

Additionally, 69% of survey respondents reported that the most costly line item for most observability solutions, data storage, is growing. Respondents stated that "the amount of observability data is growing at a concerning rate" and one in five respondents reported that this explosive data growth was their top concern.

To address data growth, organizations are taking a mix of steps to rein in costs, including:

■ increasing storage spend (52%)

■ limiting the number of observed applications in their environment (44%)

■ limiting the number of observed metrics per application (43%).

Cloud is greatest Observability challenge

Many respondents also noted that applications deployed in the cloud were harder for them to monitor and few of them felt that their observability solution was helping them meet their availability goals.

When asked about their biggest observability challenges, 60% agreed that "Lack of visibility into our cloud applications makes achieving SLAs a challenge," and only 20% chose "Improved SLA Performance" as one of their monitoring/observability strategy's most impactful benefits.

Methodology: TechTarget’s Enterprise Strategy Group (ESG) surveyed 374 IT (58%) and DevOps/AppDev (42%) professionals responsible for evaluating, purchasing, managing, and using observability at large midmarket (500 to 999 employees) (11%) and enterprise (1,000+ employees) (89%) organizations in North America (US and Canada).

Hot Topics

The Latest

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...