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Rising IT Complexity Threatens Modernization - Survey Shows SysAdmins Under Pressure

Martin Hirschvogel
Checkmk

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk.

Complexity Undermines Control and Cybersecurity

As cloud adoption, containerization, and serverless computing scale up, IT teams are struggling to maintain control, manage workloads, and mitigate risks. Without a coordinated response, the very goals of digital transformation could be at stake.

The survey highlights a dramatic rise in operational strain. Four out of five IT professionals say their tasks are becoming more complex, while 83% feel intense pressure to keep up with the rapid pace of innovation. This complexity isn't just technical — it's operational. Fragmented toolchains, distributed systems, and increased interdependencies are making environments more difficult to manage and leaving them more vulnerable to cyber threats.

Many teams find themselves forced into short-term solutions. In fact, 59% of respondents admit that quick fixes — often implemented under pressure — end up causing new problems. The lack of coherence in IT tool strategies further escalates costs and maintenance overhead.

Staff Shortages and Skills Gaps Add Fuel to the Fire

The strain on human resources is just as critical. Half of those surveyed report heavier workloads due to staffing shortages. At the same time, 49% now identify the IT skills gap as the greatest barrier to modernization — a 10 percentage point increase in just two years.

Upskilling is non-negotiable: A striking 94% of IT professionals say they'll need to learn new technologies in the next 12 months to stay effective. Skill areas in high demand include automation, configuration management, and IT monitoring — competencies that are increasingly tied to system resilience and performance. DevOps and programming expertise are also gaining traction as deployment cycles accelerate.

AI Expectations Remain Modest

Despite the buzz around artificial intelligence, the survey reveals skepticism around its real-world value. Only 40% of survey respondents expect AI to significantly reduce their daily workload. AI-driven monitoring ranks among the lowest-priority tools today, with most teams focusing instead on foundational capabilities that offer direct, tangible insights.

Monitoring: A Critical Line of Defense

Monitoring is widely regarded as essential for keeping operations on track. An overwhelming 94% of IT professionals consider IT infrastructure monitoring crucial for reducing Mean Time to Resolution (MTTR) and maintaining service level objectives (SLOs). Log management (72%), application performance management (64%), and full-stack observability (60%) are also seen as key areas, as IT teams increasingly rely on tools and methods that provide deeper insights into system components and dependencies, aiming for end-to-end visibility.

However, even effective monitoring is being challenged. A lack of knowledge is the second biggest barrier to improving MTTR — right behind infrastructure complexity itself. Without adequate support and training, even the best platforms can fall short of delivering value.

Lowering the Barrier to Innovation

The  report paints a clear picture of an industry under pressure: complexity is rising, skills are in short supply, and workloads are becoming unsustainable. To help, technology providers must lower adoption barriers and lighten the load on IT teams — with simple setup, intuitive workflows, strong automation, and flexible SaaS models. These platforms must be built not just for modern systems, but for the real-world challenges sysadmins face every day.

As IT demands grow, one thing is clear: innovation won't scale unless those managing it can keep up. Investing in better tools, training, and support for sysadmins isn't optional — it's essential to digital transformation.

Methodology: In fall 2024, Checkmk surveyed 192 IT professionals in 27 countries, primarily in IT operations, management, and consulting. Most respondents were based in Europe and North America.

Martin Hirschvogel is Chief Product Officer at Checkmk

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 ...

Rising IT Complexity Threatens Modernization - Survey Shows SysAdmins Under Pressure

Martin Hirschvogel
Checkmk

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk.

Complexity Undermines Control and Cybersecurity

As cloud adoption, containerization, and serverless computing scale up, IT teams are struggling to maintain control, manage workloads, and mitigate risks. Without a coordinated response, the very goals of digital transformation could be at stake.

The survey highlights a dramatic rise in operational strain. Four out of five IT professionals say their tasks are becoming more complex, while 83% feel intense pressure to keep up with the rapid pace of innovation. This complexity isn't just technical — it's operational. Fragmented toolchains, distributed systems, and increased interdependencies are making environments more difficult to manage and leaving them more vulnerable to cyber threats.

Many teams find themselves forced into short-term solutions. In fact, 59% of respondents admit that quick fixes — often implemented under pressure — end up causing new problems. The lack of coherence in IT tool strategies further escalates costs and maintenance overhead.

Staff Shortages and Skills Gaps Add Fuel to the Fire

The strain on human resources is just as critical. Half of those surveyed report heavier workloads due to staffing shortages. At the same time, 49% now identify the IT skills gap as the greatest barrier to modernization — a 10 percentage point increase in just two years.

Upskilling is non-negotiable: A striking 94% of IT professionals say they'll need to learn new technologies in the next 12 months to stay effective. Skill areas in high demand include automation, configuration management, and IT monitoring — competencies that are increasingly tied to system resilience and performance. DevOps and programming expertise are also gaining traction as deployment cycles accelerate.

AI Expectations Remain Modest

Despite the buzz around artificial intelligence, the survey reveals skepticism around its real-world value. Only 40% of survey respondents expect AI to significantly reduce their daily workload. AI-driven monitoring ranks among the lowest-priority tools today, with most teams focusing instead on foundational capabilities that offer direct, tangible insights.

Monitoring: A Critical Line of Defense

Monitoring is widely regarded as essential for keeping operations on track. An overwhelming 94% of IT professionals consider IT infrastructure monitoring crucial for reducing Mean Time to Resolution (MTTR) and maintaining service level objectives (SLOs). Log management (72%), application performance management (64%), and full-stack observability (60%) are also seen as key areas, as IT teams increasingly rely on tools and methods that provide deeper insights into system components and dependencies, aiming for end-to-end visibility.

However, even effective monitoring is being challenged. A lack of knowledge is the second biggest barrier to improving MTTR — right behind infrastructure complexity itself. Without adequate support and training, even the best platforms can fall short of delivering value.

Lowering the Barrier to Innovation

The  report paints a clear picture of an industry under pressure: complexity is rising, skills are in short supply, and workloads are becoming unsustainable. To help, technology providers must lower adoption barriers and lighten the load on IT teams — with simple setup, intuitive workflows, strong automation, and flexible SaaS models. These platforms must be built not just for modern systems, but for the real-world challenges sysadmins face every day.

As IT demands grow, one thing is clear: innovation won't scale unless those managing it can keep up. Investing in better tools, training, and support for sysadmins isn't optional — it's essential to digital transformation.

Methodology: In fall 2024, Checkmk surveyed 192 IT professionals in 27 countries, primarily in IT operations, management, and consulting. Most respondents were based in Europe and North America.

Martin Hirschvogel is Chief Product Officer at Checkmk

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 ...