IT teams struggle for hybrid visibility, turn to AI

More than three-quarters of IT professionals say they lack full visibility across on-premises and cloud systems, according to a new SolarWinds study that points to infrastructure complexity, siloed teams and too many monitoring tools.

In a survey of more than 750 IT professionals, 77% reported limited visibility across on-prem and cloud environments. The results suggest a widening gap between modern hybrid architectures and the monitoring and observability practices organisations use to gauge performance and reliability.

Beyond the headline figure, 75% said poor coordination between teams-such as network, infrastructure, applications and database groups-hinders effective observability. Another 55% said they use too many monitoring and observability tools.

Hybrid complexity

The research also points to the persistence of traditional infrastructure: more than half of organisations remain primarily or entirely on-premises. This mix of legacy systems and cloud services increases the number of places faults can occur and complicates how teams collect and interpret operational data.

Tooling is another source of friction. Multiple products often result from separate procurement decisions across teams, leaving organisations with overlapping alerting systems, inconsistent dashboards and gaps in coverage when ownership is unclear.

Team structure adds further complexity. Separate network, database, application and infrastructure teams may each see part of the environment but struggle to build a shared picture during incidents. More than a third of respondents described cross-team coordination as a major challenge.

SolarWinds said the findings indicate IT operations practices are under strain as environments become more distributed. "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. "Unified observability shifts teams from reactive firefighting to proactive resilience, enabling them to optimise performance, reduce risk, and keep the business running without disruption."

AI expectations

The survey also found growing interest in using AI in monitoring workflows. Sixty-four per cent of respondents rated unified observability across all layers of the IT stack as very important to their team's success.

Confidence in AI was high: 90% said they believe AI can improve monitoring and observability operations. Respondents linked AI to operational and cost benefits, including faster mean time to resolve and less unnecessary work caused by noisy alerts.

Reported uses of AI in observability included automating incident prioritisation, accelerating root-cause analysis, predicting capacity and performance issues, and reducing alert noise and fatigue. About 45% cited each use case, including 47% who said they use AI to automate incident prioritisation.

Organisations also reported barriers that could slow adoption. Security concerns topped the list at 47%, followed by skills gaps (42%) and technology complexity (41%). Employee reluctance or resistance was reported by 37%, and 33% pointed to budget constraints.

Operational trade-offs

The results highlight a tension between rapidly changing infrastructure and slower operational standardisation. Many organisations run a combination of on-premises systems, cloud platforms and distributed applications, increasing both the volume of telemetry and the variety of data sources teams must pull together during outages.

When coordination is weak, resolution can turn into incidents being handed from team to team rather than managed from a shared set of signals. Tool sprawl can amplify the problem, raising questions about which data is authoritative and which alerts should trigger escalation.

The report also outlines steps for integrating AI into observability, including identifying quick-win tasks, setting access protocols for AI tools, and investing in training and skills development.

Abigail Norman, senior director of product marketing at SolarWinds, linked AI adoption to changes in how teams manage information overload from monitoring systems.

"Every organisation's path to full visibility looks different," Norman said. "Our platform cuts through the noise by unifying observability across the stack. AI should do more than reduce alerts - it should sharpen prioritisation, streamline workflows, and give teams the space to focus on strategy instead of scrambling through dashboards."

The study was conducted with UserEvidence and included respondents from public- and private-sector organisations across North America, Europe, Latin America, Asia-Pacific, and the Middle East and Africa. Roles covered application and database management, network operations and infrastructure across on-premises and cloud environments.

SolarWinds expects organisations to continue prioritising platforms that unify operational data and automate insights as monitoring shifts toward more automated, AI-assisted operations.