Skip to main content

Top Six Skills Needed to Manage Hybrid IT Environments

Kong Yang

The market is evolving from traditional on-premises IT to a hybrid strategy where one set of critical services is maintained on-premises, but it’s connected with another set in the cloud. Whether it’s cost savings or improvement in agility organizations are after, one thing is for sure — this migration cannot be done without skilled IT professionals at the helm.

Although the new role of IT professionals in this hybrid world will vary based on individual business needs, all will need to become polymaths in order to be successful. In fact, SolarWinds recently conducted a survey among nearly 100 global IT professionals in its thwack community that revealed the top six skills they say are needed to manage hybrid IT environments.

1. Service-oriented architectures (SOAs)

As more companies move to a hybrid IT model, they will need to be more agile, lean and cost-effective. To meet these needs, the barriers to consumption will need to decrease. So, IT professionals will need to leverage templates and services from the marketplace and understand application architectures, distributed systems, APIs and IT operations.

2. Automation

In a hybrid world, where infrastructure resources are only one part of the equation, businesses will need more than scripts to enable automation as they reduce the friction to consume services. Automation in a hybrid IT environment must abstract away the operations layer and be integrated with machine learning algorithms that will automatically scale, move and remediate services. IT professionals will need to integrate their automation and orchestration workflows with provider APIs.

3. Hybrid IT monitoring

For hybrid IT environments, a complete view of the on-premises data center and the cloud is even more critical. IT professionals must build a tool to aggregate, consolidate and visualize key performance and events metrics, and glean the key points from the data to discern the most valuable pieces of information from their application stacks. Alternatively, they can leverage a monitoring vendor that has an end to end solution that can provide the single point of truth for their IT needs from their premises to their clouds.

4. Vendor management

Vendor management is two-fold, as IT professionals will need to manage the technology aspect of cloud environments, as well as the business side of service provider T&Cs and variable pricing. Most IT professionals are not currently involved in business dealings that include legalese and pricing, but as contracts become more nuanced, IT professionals must improve upon the following trifecta: business savvy for contract negotiation, technical expertise to use cloud services and project management.

5. Application migration

Application migration to the cloud can be time-consuming, as it typically takes weeks for a single application. However, some service providers are making it much easier. But IT professionals must remember that application migration is just step one — the management required following initial migration is arguably more important. They should apply the core competencies they would employ in a traditional IT environment, while having a firm understanding of the application’s key events and performance metrics. Troubleshooting and remediation are also key, because things will change and fail. Therefore, having backup and disaster recovery plans can ensure business continuity.

6. Distributed architectures

Working with distributed architectures will require working across multiple service providers and geographies. It’s important to remember that these architectures abstract the underlying resources, so IT professionals will need to translate speeds and feeds into acceptable quality-of-service for their users. In hybrid IT, they will need to become accustomed to multiple providers handling remediation in case of outages or other performance issues. The control and responsibility for maintaining the distributed architecture will shift beyond IT’s purview, but in their place will be choice, scale, agility and availability of services to build distributed architectures.

Adding and mastering these skills will go a long way to ensuring not only business success in this new hybrid world, but IT career longevity.

Kong Yang is a Head Geek at SolarWinds.

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.

Top Six Skills Needed to Manage Hybrid IT Environments

Kong Yang

The market is evolving from traditional on-premises IT to a hybrid strategy where one set of critical services is maintained on-premises, but it’s connected with another set in the cloud. Whether it’s cost savings or improvement in agility organizations are after, one thing is for sure — this migration cannot be done without skilled IT professionals at the helm.

Although the new role of IT professionals in this hybrid world will vary based on individual business needs, all will need to become polymaths in order to be successful. In fact, SolarWinds recently conducted a survey among nearly 100 global IT professionals in its thwack community that revealed the top six skills they say are needed to manage hybrid IT environments.

1. Service-oriented architectures (SOAs)

As more companies move to a hybrid IT model, they will need to be more agile, lean and cost-effective. To meet these needs, the barriers to consumption will need to decrease. So, IT professionals will need to leverage templates and services from the marketplace and understand application architectures, distributed systems, APIs and IT operations.

2. Automation

In a hybrid world, where infrastructure resources are only one part of the equation, businesses will need more than scripts to enable automation as they reduce the friction to consume services. Automation in a hybrid IT environment must abstract away the operations layer and be integrated with machine learning algorithms that will automatically scale, move and remediate services. IT professionals will need to integrate their automation and orchestration workflows with provider APIs.

3. Hybrid IT monitoring

For hybrid IT environments, a complete view of the on-premises data center and the cloud is even more critical. IT professionals must build a tool to aggregate, consolidate and visualize key performance and events metrics, and glean the key points from the data to discern the most valuable pieces of information from their application stacks. Alternatively, they can leverage a monitoring vendor that has an end to end solution that can provide the single point of truth for their IT needs from their premises to their clouds.

4. Vendor management

Vendor management is two-fold, as IT professionals will need to manage the technology aspect of cloud environments, as well as the business side of service provider T&Cs and variable pricing. Most IT professionals are not currently involved in business dealings that include legalese and pricing, but as contracts become more nuanced, IT professionals must improve upon the following trifecta: business savvy for contract negotiation, technical expertise to use cloud services and project management.

5. Application migration

Application migration to the cloud can be time-consuming, as it typically takes weeks for a single application. However, some service providers are making it much easier. But IT professionals must remember that application migration is just step one — the management required following initial migration is arguably more important. They should apply the core competencies they would employ in a traditional IT environment, while having a firm understanding of the application’s key events and performance metrics. Troubleshooting and remediation are also key, because things will change and fail. Therefore, having backup and disaster recovery plans can ensure business continuity.

6. Distributed architectures

Working with distributed architectures will require working across multiple service providers and geographies. It’s important to remember that these architectures abstract the underlying resources, so IT professionals will need to translate speeds and feeds into acceptable quality-of-service for their users. In hybrid IT, they will need to become accustomed to multiple providers handling remediation in case of outages or other performance issues. The control and responsibility for maintaining the distributed architecture will shift beyond IT’s purview, but in their place will be choice, scale, agility and availability of services to build distributed architectures.

Adding and mastering these skills will go a long way to ensuring not only business success in this new hybrid world, but IT career longevity.

Kong Yang is a Head Geek at SolarWinds.

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.