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Cloud Computing and Hybrid IT: Evolving Norms - Part 2

Leon Adato

For IT professionals to succeed as the hybrid IT era continues to evolve, they must arm themselves with a new set of skills, tools, and resources. Consider the following recommendations:

Start with Cloud Computing and Hybrid IT: Evolving Norms - Part 1

1. Ensure central visibility across on-premises and cloud environments

In the face of enterprise technology’s exponential rate of change, a management and monitoring toolset that surfaces a single point of truth across those platforms is essential. The ability to consolidate and correlate data to deliver more breadth, depth, and visibility across the data center will allow IT professionals to proactively identify and remediate problem areas and reduce the mean time to resolution.

2. Consider more than just cost efficiency

The findings of this year’s report indicate that cloud’s ability to increase ROI is less important to today’s IT professionals, with security, compliance, and performance now top of mind. With end-user expectations for availability, durability, and an acceptable response time no matter where an application is hosted or from where it’s delivered, IT professionals need to factor in the security and performance requirements of each application prior to migration to cloud services to ensure that Quality of Service is still met throughout the distributed stack.

3. Cloud-proof your job

Over the past 12 months, IT professionals ranked hybrid monitoring/management tools and metrics, application migration, automation, and data analytics as the most important skills and knowledge needed to successfully manage hybrid IT environments. In addition to leveraging their peer communities to better understand technology adaptations and abstractions, IT professionals need to establish monitoring as a foundational IT skill, also known as monitoring as a discipline, to drive a more proactive, efficient, and effective IT management strategy.

4. Forecast future migration, but remain flexible

As illustrated by this year’s report findings, every organization's hybrid IT environment is unique and the velocity, variety, and volume of new technology services are giving ample opportunity to realize innovation. To that end, IT professionals must be open to and agile in adopting the best-of-breed elements of cloud computing and hybrid IT. The best thing for any IT department to do in the year ahead is to build a roadmap for future integration and delivery that will help illustrate ROI and business advantages, or the lack thereof, for business management.

5. Build trust with cloud service providers through IT competency

“Trust but verify” should be the IT professional’s mantra in the year ahead, as organizations work to identify how best to maintain an element of control and visibility into workloads and applications that are hosted in the cloud. It will be critical to leverage comprehensive hybrid IT monitoring, beyond what is typically offered by cloud service providers, to ensure they have enough data and visibility to truly understand how workloads are performing in the cloud and the reasons for that performance. Similar to traditional on-premises strategies, application availability and durability are key trust tenets in hybrid IT.

Cloud computing’s mounting importance and the shift to hybrid IT are evolving norms. With the report’s findings in mind, it’s crucial that IT professionals continue to learn new skills and adapt to the ever-changing hybrid IT environment.

Hot Topics

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

Cloud Computing and Hybrid IT: Evolving Norms - Part 2

Leon Adato

For IT professionals to succeed as the hybrid IT era continues to evolve, they must arm themselves with a new set of skills, tools, and resources. Consider the following recommendations:

Start with Cloud Computing and Hybrid IT: Evolving Norms - Part 1

1. Ensure central visibility across on-premises and cloud environments

In the face of enterprise technology’s exponential rate of change, a management and monitoring toolset that surfaces a single point of truth across those platforms is essential. The ability to consolidate and correlate data to deliver more breadth, depth, and visibility across the data center will allow IT professionals to proactively identify and remediate problem areas and reduce the mean time to resolution.

2. Consider more than just cost efficiency

The findings of this year’s report indicate that cloud’s ability to increase ROI is less important to today’s IT professionals, with security, compliance, and performance now top of mind. With end-user expectations for availability, durability, and an acceptable response time no matter where an application is hosted or from where it’s delivered, IT professionals need to factor in the security and performance requirements of each application prior to migration to cloud services to ensure that Quality of Service is still met throughout the distributed stack.

3. Cloud-proof your job

Over the past 12 months, IT professionals ranked hybrid monitoring/management tools and metrics, application migration, automation, and data analytics as the most important skills and knowledge needed to successfully manage hybrid IT environments. In addition to leveraging their peer communities to better understand technology adaptations and abstractions, IT professionals need to establish monitoring as a foundational IT skill, also known as monitoring as a discipline, to drive a more proactive, efficient, and effective IT management strategy.

4. Forecast future migration, but remain flexible

As illustrated by this year’s report findings, every organization's hybrid IT environment is unique and the velocity, variety, and volume of new technology services are giving ample opportunity to realize innovation. To that end, IT professionals must be open to and agile in adopting the best-of-breed elements of cloud computing and hybrid IT. The best thing for any IT department to do in the year ahead is to build a roadmap for future integration and delivery that will help illustrate ROI and business advantages, or the lack thereof, for business management.

5. Build trust with cloud service providers through IT competency

“Trust but verify” should be the IT professional’s mantra in the year ahead, as organizations work to identify how best to maintain an element of control and visibility into workloads and applications that are hosted in the cloud. It will be critical to leverage comprehensive hybrid IT monitoring, beyond what is typically offered by cloud service providers, to ensure they have enough data and visibility to truly understand how workloads are performing in the cloud and the reasons for that performance. Similar to traditional on-premises strategies, application availability and durability are key trust tenets in hybrid IT.

Cloud computing’s mounting importance and the shift to hybrid IT are evolving norms. With the report’s findings in mind, it’s crucial that IT professionals continue to learn new skills and adapt to the ever-changing hybrid IT environment.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.