The Invisible Crisis: 77% of IT Teams Lack Full Hybrid Cloud Visibility

AUSTIN, TX – March 11, 2026 – A staggering 77% of information technology teams are operating with significant blind spots, lacking full visibility across their on-premise and cloud environments. This critical gap, revealed in a new industry report, exposes businesses to prolonged outages, security risks, and operational inefficiencies as they navigate increasingly fragmented digital infrastructures.

The findings are part of the 2026 SolarWinds State of Monitoring & Observability Report, which surveyed over 750 IT professionals globally. The report paints a picture of organizations struggling to keep pace with their own technological evolution, where the tools meant to provide clarity are often contributing to the chaos. As businesses balance legacy systems with modern cloud-native architectures, the lack of a unified view has become a foundational threat to performance and resilience.

"As IT environments grow more distributed and business-critical, visibility is no longer optional; it’s foundational,” said Cullen Childress, Chief Product Officer at SolarWinds, in the report's release. “Unified observability shifts teams from reactive firefighting to proactive resilience, enabling them to optimize performance, reduce risk, and keep the business running without disruption.”

A Tangled Web of Tools and Teams

The report highlights a trio of systemic issues crippling IT operations: visibility gaps, tool sprawl, and poor cross-team collaboration. While hybrid IT—a mix of private data centers and public cloud services—offers flexibility, it has created a complex, often unmanageable, tangle.

According to the SolarWinds study, 75% of IT professionals state that a lack of coordination between specialized teams—such as network, infrastructure, applications, and database administrators—hinders effective observability. This siloed approach means that when a problem arises, teams often point fingers or lack the shared data needed to quickly diagnose the root cause. This finding is echoed across the industry, with other research, such as IDC's 2025 Observability Survey, noting that 43% of organizations identify poor collaboration as a key obstacle to identifying performance issues.

Compounding the problem is "tool sprawl." The report found that 55% of IT professionals are using too many disparate monitoring and observability tools. Each tool provides a narrow slice of the picture, forcing engineers to manually stitch together data from multiple dashboards, a process that is slow, error-prone, and unsustainable during a crisis. The result is a reactive cycle of "firefighting" where teams scramble to fix problems after they have already impacted customers, rather than preventing them in the first place.

AI: The Double-Edged Sword of Observability

Amidst these challenges, Artificial Intelligence (AI) has emerged as a powerful, if complex, solution. An overwhelming 90% of respondents in the SolarWinds report expressed confidence in AI's ability to improve monitoring and observability. The technology promises to cut through the noise, automate tedious tasks, and provide the predictive insights needed to manage modern IT stacks.

IT teams are already leveraging AI to:
* Automate the prioritization of incoming incidents (47%)
* Accelerate root cause analysis (45%)
* Predict future capacity and performance issues (45%)
* Reduce the deluge of alerts that lead to team burnout (45%)

However, the path to AI-driven observability is fraught with its own obstacles. The report identifies significant barriers preventing full-scale adoption, with security concerns topping the list at 47%. Experts warn that implementing AI introduces new attack surfaces, including the potential for data poisoning, where malicious data is fed into training models, or adversarial attacks that trick AI into making incorrect decisions.

Beyond security, 42% of organizations point to a growing skills gap, as the demand for professionals who can build, manage, and secure AI systems far outstrips the available talent. The complexity of the technology itself (41%) and employee resistance (37%) also present considerable hurdles.

"AI should do more than reduce alerts — it should sharpen prioritization, streamline workflows, and give teams the space to focus on strategy instead of scrambling through dashboards,” noted Abigail Norman, Sr. Director of Product Marketing at SolarWinds. This sentiment underscores the need for organizations to develop comprehensive strategies for upskilling their workforce and implementing robust governance to use AI responsibly and effectively.

An Industry-Wide Race for Intelligent Insight

SolarWinds is not alone in positioning AI as the answer to observability's biggest challenges. The entire IT management industry is in a race to embed advanced AI into their platforms, reflecting a market-wide consensus on the technology's transformative potential.

Competitors like Dynatrace have centered their platforms around proprietary engines like "Davis AI," which combines predictive and generative AI to automate root cause analysis and simplify dashboard creation. Similarly, Datadog is expanding its AIOps capabilities to automate incident response and provide cross-domain insights, while Splunk has rolled out generative AI assistants to help engineers investigate issues using natural language.

This competitive landscape signals a fundamental shift in the market. The primary criterion for selecting an observability platform is no longer just its ability to handle cloud workloads but its embedded AI capabilities. The goal for these platforms is to create a unified "single pane of glass" that not only displays data from across the hybrid environment but also interprets it, offers predictive insights, and automates remediation.

From Reactive Fixes to Proactive Strategy

The ultimate goal of this evolution is to transform IT from a cost center focused on maintenance to a strategic driver of business innovation. Industry data shows that organizations leading in observability see tangible benefits, including significantly faster issue resolution times, reduced unplanned downtime, and higher software deployment rates. One study by New Relic found that users of AI-powered observability features resolved issues 25% faster on average.

By automating routine monitoring and analysis, IT professionals are freed up to focus on higher-value activities. According to a 2025 Splunk report, 78% of leaders say AI in observability enables them to spend more time on innovation. This shift is critical, as downtime and performance degradation have direct financial consequences, with 74% of executives in the same report admitting that downtime has delayed their time-to-market.

As organizations continue their digital transformation journeys, the complexity of their IT environments will only increase. The ability to see, understand, and proactively manage this complexity is no longer a technical luxury but a business imperative. Overcoming the challenges of tool sprawl, team silos, and AI integration is the critical next step for enterprises seeking to build a resilient and innovative future.