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AI Creates "Disrupt or Die" Era

"The rise of AI is ushering in a new disrupt-or-die era," said Gabie Boko, Chief Marketing Officer at NetApp. "Data-ready enterprises that connect and unify broad structured and unstructured data sets into an intelligent data infrastructure are best positioned to win in the age of AI."

The 2024 Cloud Complexity Report from Netapp found a clear divide between AI leaders and AI laggards across several areas including:

Regions: 60% of AI-leading countries (India, Singapore, UK, USA) have AI projects up and running or in pilot, in stark contrast to 36% in AI-lagging countries (Spain, Australia/New Zealand, Germany, Japan).

Industries: Technology leads with 70% of AI projects up and running or in pilot, while Banking & Financial Services and Manufacturing follow with 55% and 50%, respectively. However, Healthcare (38%) and Media & Entertainment (25%) are trailing.

Company size: Larger companies (with more than 250 employees) are more likely to have AI projects in motion, with 62% reporting projects up and running or in pilot, versus 36% of smaller companies (with fewer than 250 employees).

Both AI leaders and AI laggards show a difference in their approach to AI:

■ Globally, 67% of companies in AI-leading countries report having hybrid IT environments, with India leading (70%) and Japan lagging (24%).

■ AI leaders are also more likely to report benefits from AI, including a 50% increase in production rates, 46% in the automation of routine activities, and a 45% improvement in customer experience.

"AI is only as good as the data that fuels it," said Pravjit Tiwana, GM and SVP of Cloud Storage at NetApp. "Both the AI leaders and AI laggards show us that in the prevailing hybrid IT environment, the more unified and reliable your data, the more likely your AI initiatives are to be successful."

AI Laggards Must Swiftly Innovate to Stay Competitive

Despite the divide, there is notable progress among AI laggards in preparing their IT environments for AI, but the window to catch up is closing rapidly.

A significant number of companies in AI-lagging countries (42%) have optimized their IT environments for AI, including Germany (67%) and Spain (59%)

Companies in some AI-lagging countries already report seeing the benefits of a unified data infrastructure in place, such as:

Easier data sharing: Spain (45%), Australia/New Zealand (43%), Germany (44%)

Increased visibility: Spain (54%) and Germany (46%)

IT Costs and Data Security Emerge as Top Challenges but Won't Impede AI Progress

Rising IT costs and ensuring data security are the two of the biggest challenges in the AI era, but they will not block AI progress. Instead, AI leaders will scale back, cut other IT operations, or reallocate costs from other parts of the business to fund AI initiatives.

■ AI leaders will also increase their cloud operations (CloudOps), data security and AI investments throughout 2024, with 40% of large companies saying AI projects have already increased IT costs.

■ Year over year, "increased cybersecurity risk" jumped 16% as a top concern from 45% to 61%, while all other concerns decreased.

■ To manage AI project costs, 31% of companies globally are reallocating funds from other business areas, with India (48%), UK (40%), and US (35%) leading this trend.

Security, AI and CloudOps Drive 2024 Cloud Investments

As global companies, whether AI leaders or AI laggards, increase investments, they are relying on the cloud to support their goals.

■ Companies reported that they expect to increase AI-driven cloud deployments by 19% from 2024 to 2030.

■ 85% of AI leaders plan to enhance their CloudOps automation over the next year.

■ Increasing data security investments is a global priority, jumping 25% from 33% in 2023 to 58% in 2024.

Methodology: In March 2024, NetApp partnered with Savanta to conduct a quantitative research study of 1,300+ tech and data executives at businesses in 10 markets: US, EMEA (UK, France, Germany, Spain), and APAC (Australia, New Zealand, India, Singapore, Japan).

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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 ...

AI Creates "Disrupt or Die" Era

"The rise of AI is ushering in a new disrupt-or-die era," said Gabie Boko, Chief Marketing Officer at NetApp. "Data-ready enterprises that connect and unify broad structured and unstructured data sets into an intelligent data infrastructure are best positioned to win in the age of AI."

The 2024 Cloud Complexity Report from Netapp found a clear divide between AI leaders and AI laggards across several areas including:

Regions: 60% of AI-leading countries (India, Singapore, UK, USA) have AI projects up and running or in pilot, in stark contrast to 36% in AI-lagging countries (Spain, Australia/New Zealand, Germany, Japan).

Industries: Technology leads with 70% of AI projects up and running or in pilot, while Banking & Financial Services and Manufacturing follow with 55% and 50%, respectively. However, Healthcare (38%) and Media & Entertainment (25%) are trailing.

Company size: Larger companies (with more than 250 employees) are more likely to have AI projects in motion, with 62% reporting projects up and running or in pilot, versus 36% of smaller companies (with fewer than 250 employees).

Both AI leaders and AI laggards show a difference in their approach to AI:

■ Globally, 67% of companies in AI-leading countries report having hybrid IT environments, with India leading (70%) and Japan lagging (24%).

■ AI leaders are also more likely to report benefits from AI, including a 50% increase in production rates, 46% in the automation of routine activities, and a 45% improvement in customer experience.

"AI is only as good as the data that fuels it," said Pravjit Tiwana, GM and SVP of Cloud Storage at NetApp. "Both the AI leaders and AI laggards show us that in the prevailing hybrid IT environment, the more unified and reliable your data, the more likely your AI initiatives are to be successful."

AI Laggards Must Swiftly Innovate to Stay Competitive

Despite the divide, there is notable progress among AI laggards in preparing their IT environments for AI, but the window to catch up is closing rapidly.

A significant number of companies in AI-lagging countries (42%) have optimized their IT environments for AI, including Germany (67%) and Spain (59%)

Companies in some AI-lagging countries already report seeing the benefits of a unified data infrastructure in place, such as:

Easier data sharing: Spain (45%), Australia/New Zealand (43%), Germany (44%)

Increased visibility: Spain (54%) and Germany (46%)

IT Costs and Data Security Emerge as Top Challenges but Won't Impede AI Progress

Rising IT costs and ensuring data security are the two of the biggest challenges in the AI era, but they will not block AI progress. Instead, AI leaders will scale back, cut other IT operations, or reallocate costs from other parts of the business to fund AI initiatives.

■ AI leaders will also increase their cloud operations (CloudOps), data security and AI investments throughout 2024, with 40% of large companies saying AI projects have already increased IT costs.

■ Year over year, "increased cybersecurity risk" jumped 16% as a top concern from 45% to 61%, while all other concerns decreased.

■ To manage AI project costs, 31% of companies globally are reallocating funds from other business areas, with India (48%), UK (40%), and US (35%) leading this trend.

Security, AI and CloudOps Drive 2024 Cloud Investments

As global companies, whether AI leaders or AI laggards, increase investments, they are relying on the cloud to support their goals.

■ Companies reported that they expect to increase AI-driven cloud deployments by 19% from 2024 to 2030.

■ 85% of AI leaders plan to enhance their CloudOps automation over the next year.

■ Increasing data security investments is a global priority, jumping 25% from 33% in 2023 to 58% in 2024.

Methodology: In March 2024, NetApp partnered with Savanta to conduct a quantitative research study of 1,300+ tech and data executives at businesses in 10 markets: US, EMEA (UK, France, Germany, Spain), and APAC (Australia, New Zealand, India, Singapore, Japan).

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 ...