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IDC FutureScape: Top 10 Predictions for the Future of Digital Infrastructure

International Data Corporation's (IDC) top 10 predictions for the Future of Digital Infrastructure point to a digital infrastructure strategy that addresses resiliency and trust; data-driven operational complexity; and business outcomes-driven sourcing and autonomous operations.

Organizations must invest in and foster a digital-first culture that leverages trusted industry ecosystems, generates profitable revenue growth, provides empathetic customer experiences, and demonstrates an ability to adapt operating models to complex customer requirements.

In the coming years, organizations will deploy, operate, and scale digital infrastructure to ensure consistent security, performance, and compliance across all resources, regardless of where and how they are deployed. These organizations will invest in more intelligent, autonomous operations and take advantage of flexible consumption and strategic vendor partnerships to promote agility and ensure that the business, and its digital infrastructure, can continue to perform in the face of a wide range of unexpected scenarios – social, geopolitical, economic, climate, or business related.

"Digital infrastructure spans compute, storage, network, and infrastructure software, including virtualization and containers, and the automation, AI/ML analytics, and security software and cloud services needed to maintain and optimize both legacy and modern applications and data," explained Mary Johnston Turner, Research VP, Future of Digital Infrastructure. "IDC's 2022 predictions for the future of digital infrastructure identify critical shifts in governance, operations, architecture, and sourcing that need to be factored into enterprise digital transformation strategies going forward."

The top 10 predictions from the Worldwide Future of Digital Infrastructure 2022 report are:

Prediction 1

By 2023, G2000 leaders will prioritize business objectives over infrastructure choice, deploying 50% of new strategic workloads using vendor-specific APIs that add value but reduce workload portability.

Prediction 2

In 2023, over 80% of the G2000 will cite business resiliency to drive verifiable infrastructure supply chain integrity as a mandatory and non-negotiable vendor evaluation criterion.

Prediction 3

By 2023, most C-suite leaders will implement business critical KPIs tied to data availability, recovery, and stewardship as rising levels of cyber-attacks expose the scale of data at risk.

Prediction 4

By 2024, 75% of G2000 digital infrastructure RFPs will require vendors to prove progress on ESG/Sustainability initiatives with data, as CIOs rely on infrastructure vendors to help meet ESG goals.

Prediction 5

By 2024, due to an explosion of edge data, 65% of the G2000 will embed edge-first data stewardship, security, and network practices into data protection plans to integrate edge data into relevant processes.

Prediction 6

By 2025, a 6x explosion in high dependency workloads leads to 65% of G2000 firms using consistent architectural governance frameworks to ensure compliance reporting and audit of their infrastructure.

Prediction 7

By 2025, 60% of enterprises will fund LOB and IT projects through OPEX budgets, matching how vendors provide their services with a focus on outcomes that are determined by SLAs and KPIs.

Prediction 8

By 2025, 70% of companies will invest in alternative computing technologies to drive business differentiation by compressing time to value of insights from complex data sets.

Prediction 9

By 2026, 90% of G2000 CIOs will use AIOps solutions to drive automated remediation and workload placement decisions that include cost and performance metrics, improving resiliency and agility.

Prediction 10

By 2026, mid-market companies will shift 65% of infrastructure spending from traditional channels towards more app-centric trusted advisors.

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 ...

IDC FutureScape: Top 10 Predictions for the Future of Digital Infrastructure

International Data Corporation's (IDC) top 10 predictions for the Future of Digital Infrastructure point to a digital infrastructure strategy that addresses resiliency and trust; data-driven operational complexity; and business outcomes-driven sourcing and autonomous operations.

Organizations must invest in and foster a digital-first culture that leverages trusted industry ecosystems, generates profitable revenue growth, provides empathetic customer experiences, and demonstrates an ability to adapt operating models to complex customer requirements.

In the coming years, organizations will deploy, operate, and scale digital infrastructure to ensure consistent security, performance, and compliance across all resources, regardless of where and how they are deployed. These organizations will invest in more intelligent, autonomous operations and take advantage of flexible consumption and strategic vendor partnerships to promote agility and ensure that the business, and its digital infrastructure, can continue to perform in the face of a wide range of unexpected scenarios – social, geopolitical, economic, climate, or business related.

"Digital infrastructure spans compute, storage, network, and infrastructure software, including virtualization and containers, and the automation, AI/ML analytics, and security software and cloud services needed to maintain and optimize both legacy and modern applications and data," explained Mary Johnston Turner, Research VP, Future of Digital Infrastructure. "IDC's 2022 predictions for the future of digital infrastructure identify critical shifts in governance, operations, architecture, and sourcing that need to be factored into enterprise digital transformation strategies going forward."

The top 10 predictions from the Worldwide Future of Digital Infrastructure 2022 report are:

Prediction 1

By 2023, G2000 leaders will prioritize business objectives over infrastructure choice, deploying 50% of new strategic workloads using vendor-specific APIs that add value but reduce workload portability.

Prediction 2

In 2023, over 80% of the G2000 will cite business resiliency to drive verifiable infrastructure supply chain integrity as a mandatory and non-negotiable vendor evaluation criterion.

Prediction 3

By 2023, most C-suite leaders will implement business critical KPIs tied to data availability, recovery, and stewardship as rising levels of cyber-attacks expose the scale of data at risk.

Prediction 4

By 2024, 75% of G2000 digital infrastructure RFPs will require vendors to prove progress on ESG/Sustainability initiatives with data, as CIOs rely on infrastructure vendors to help meet ESG goals.

Prediction 5

By 2024, due to an explosion of edge data, 65% of the G2000 will embed edge-first data stewardship, security, and network practices into data protection plans to integrate edge data into relevant processes.

Prediction 6

By 2025, a 6x explosion in high dependency workloads leads to 65% of G2000 firms using consistent architectural governance frameworks to ensure compliance reporting and audit of their infrastructure.

Prediction 7

By 2025, 60% of enterprises will fund LOB and IT projects through OPEX budgets, matching how vendors provide their services with a focus on outcomes that are determined by SLAs and KPIs.

Prediction 8

By 2025, 70% of companies will invest in alternative computing technologies to drive business differentiation by compressing time to value of insights from complex data sets.

Prediction 9

By 2026, 90% of G2000 CIOs will use AIOps solutions to drive automated remediation and workload placement decisions that include cost and performance metrics, improving resiliency and agility.

Prediction 10

By 2026, mid-market companies will shift 65% of infrastructure spending from traditional channels towards more app-centric trusted advisors.

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 ...