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Cloud Barriers Impact the Bottom Line

While most companies are now deploying cloud-based technologies, the 2024 Secure Cloud Networking Field Report from Aviatrix found that there is a silent struggle to maximize value from those investments. Many of the challenges organizations have faced over the past several years have evolved, but continue today.

Among the key findings:

Cost and visibility are top barriers

Cost and visibility are the top barriers to cloud. Respondents report that "cost controls" and "visibility and troubleshooting" (both 45%) are the biggest hurdles to their organization taking full advantage of cloud (i.e. where they are burning the most man-hours ).

Human error is top cause of cloud outages

Legacy approaches and lagging skill sets are causes for cloud security concern. More cloud network outages were caused by firewalls (31.2%) than cyberattacks (15.3%) in the past year. In addition, human error caused 47.1% of outages.

AI is impacting cloud budgets

Unrealistic cloud budgets are resulting in cost overruns and concern about the implementation of generative AI (GenAI) initiatives. 30.2% report their organization's AI initiatives have increased planned investment in cloud. But another 38.4% report AI initiatives have not impacted cloud budgets (25.8%) or even decreased them (12.6%).

Cloud skills gap remains strong

62.6% report their company has struggled to hire the necessary candidates to support cloud initiatives within their organization. 65.7% of respondents reported that they have struggled to find educational resources for learning about high-demand skills such as multicloud network architecture and design.

"Cloud networking and network security have been thought of as utilities – essentials like electricity that enterprises rely on to run their business and just expect to work," said Chris McHenry, Vice President of Product Management at Aviatrix. "But as we've seen from the practitioners behind the infrastructure, there's still a of complexity in the cloud that is ultimately impacting businesses' bottom lines."

Methodology: The survey included more than 400 global respondents spanning security, cloud, networking roles, with several reporting they hold more than one of these roles at their organization. More than 50% of the respondents were from enterprise organizations with more than 2,000 employees.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Cloud Barriers Impact the Bottom Line

While most companies are now deploying cloud-based technologies, the 2024 Secure Cloud Networking Field Report from Aviatrix found that there is a silent struggle to maximize value from those investments. Many of the challenges organizations have faced over the past several years have evolved, but continue today.

Among the key findings:

Cost and visibility are top barriers

Cost and visibility are the top barriers to cloud. Respondents report that "cost controls" and "visibility and troubleshooting" (both 45%) are the biggest hurdles to their organization taking full advantage of cloud (i.e. where they are burning the most man-hours ).

Human error is top cause of cloud outages

Legacy approaches and lagging skill sets are causes for cloud security concern. More cloud network outages were caused by firewalls (31.2%) than cyberattacks (15.3%) in the past year. In addition, human error caused 47.1% of outages.

AI is impacting cloud budgets

Unrealistic cloud budgets are resulting in cost overruns and concern about the implementation of generative AI (GenAI) initiatives. 30.2% report their organization's AI initiatives have increased planned investment in cloud. But another 38.4% report AI initiatives have not impacted cloud budgets (25.8%) or even decreased them (12.6%).

Cloud skills gap remains strong

62.6% report their company has struggled to hire the necessary candidates to support cloud initiatives within their organization. 65.7% of respondents reported that they have struggled to find educational resources for learning about high-demand skills such as multicloud network architecture and design.

"Cloud networking and network security have been thought of as utilities – essentials like electricity that enterprises rely on to run their business and just expect to work," said Chris McHenry, Vice President of Product Management at Aviatrix. "But as we've seen from the practitioners behind the infrastructure, there's still a of complexity in the cloud that is ultimately impacting businesses' bottom lines."

Methodology: The survey included more than 400 global respondents spanning security, cloud, networking roles, with several reporting they hold more than one of these roles at their organization. More than 50% of the respondents were from enterprise organizations with more than 2,000 employees.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...