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Gartner Says "Cloud Shift" Will Affect More Than $1 Trillion in IT Spending

Pete Goldin
APMdigest

More than $1 trillion in IT spending will be directly or indirectly affected by the shift to cloud during the next five years, according to Gartner, Inc. This will make cloud computing one of the most disruptive forces of IT spending since the early days of the digital age.

"Cloud-first strategies are the foundation for staying relevant in a fast-paced world," said Ed Anderson, Research VP at Gartner. "The market for cloud services has grown to such an extent that it is now a notable percentage of total IT spending, helping to create a new generation of start-ups and "born in the cloud" providers."

IT spending is steadily shifting from traditional IT offerings to cloud services (cloud shift). The aggregate amount of cloud shift in 2016 is estimated to reach $111 billion, increasing to $216 billion in 2020. Cloud shift rates are determined by comparing IT spending on cloud services with traditional noncloud services in the same market categories.

In addition to the direct effects of cloud shift, many markets will be affected indirectly. Identifying indirect effects can help IT asset and purchasing managers ensure they are getting the best value out of new expenditure and are protected against risk, as well as assisting them to exploit the new opportunities caused by cloud shift.

For example, instead of buying operating systems (OSs) for each user in the traditional way, many will be provided as OS images — particularly with the use of containers for next-generation applications. Another example is that enterprise storage needs could be met with a lower up front cost and far more scalability by switching to cloud solutions instead of buying dedicated hardware.

"Cloud shift is not just about cloud. As organizations pursue a new IT architecture and operating philosophy, they become prepared for new opportunities in digital business, including next-generation IT solutions such as the Internet of Things," said Anderson. "Furthermore, organizations embracing dynamic, cloud-based operating models position themselves better for cost optimization and increased competitiveness."

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

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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 ...

Gartner Says "Cloud Shift" Will Affect More Than $1 Trillion in IT Spending

Pete Goldin
APMdigest

More than $1 trillion in IT spending will be directly or indirectly affected by the shift to cloud during the next five years, according to Gartner, Inc. This will make cloud computing one of the most disruptive forces of IT spending since the early days of the digital age.

"Cloud-first strategies are the foundation for staying relevant in a fast-paced world," said Ed Anderson, Research VP at Gartner. "The market for cloud services has grown to such an extent that it is now a notable percentage of total IT spending, helping to create a new generation of start-ups and "born in the cloud" providers."

IT spending is steadily shifting from traditional IT offerings to cloud services (cloud shift). The aggregate amount of cloud shift in 2016 is estimated to reach $111 billion, increasing to $216 billion in 2020. Cloud shift rates are determined by comparing IT spending on cloud services with traditional noncloud services in the same market categories.

In addition to the direct effects of cloud shift, many markets will be affected indirectly. Identifying indirect effects can help IT asset and purchasing managers ensure they are getting the best value out of new expenditure and are protected against risk, as well as assisting them to exploit the new opportunities caused by cloud shift.

For example, instead of buying operating systems (OSs) for each user in the traditional way, many will be provided as OS images — particularly with the use of containers for next-generation applications. Another example is that enterprise storage needs could be met with a lower up front cost and far more scalability by switching to cloud solutions instead of buying dedicated hardware.

"Cloud shift is not just about cloud. As organizations pursue a new IT architecture and operating philosophy, they become prepared for new opportunities in digital business, including next-generation IT solutions such as the Internet of Things," said Anderson. "Furthermore, organizations embracing dynamic, cloud-based operating models position themselves better for cost optimization and increased competitiveness."

Pete Goldin is Editor and Publisher of APMdigest

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 ...