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Can IT Keep Up with Digital Transformation?

IT leaders have growing concerns about their ability to keep up with digital transformation, according to a new report from Dynatrace, How to transform the way teams work to improve collaboration and drive better business outcomes.

The report shows traditional IT operating models with siloed teams and multiple monitoring and management solutions are proving ineffective at keeping up with cloud-native architectures. As a result, teams waste time manually combining data from disparate solutions in a reactive effort to solve challenges instead of focusing on driving innovation.


The survey also reveals:

■ 89% of CIOs say digital transformation has already accelerated, and 58% predict it will continue to speed up.

■ 93% of CIOs say IT's ability to maximize value for the business is hindered by challenges, including IT and business teams working in silos.

■ 74% of CIOs say they are fed up with the need to piece together data from multiple tools to assess the impact of IT investments on the business.

■ 40% of CIOs say limited collaboration across BizDevOps teams disrupts IT's ability to respond quickly to sudden changes in business needs.

■ 16% of an IT team's time is spent in meetings with the business to identify the causes of and solutions to problems. This issue alone costs organizations an average of $1.7 million annually due to lost productivity.

"As the pace of digital transformation accelerates, and modern, dynamic clouds introduce increasing complexity, the pressure on teams to make data-driven business decisions, and automate operations to deliver business value faster, has never been greater," said Mike Maciag, CMO at Dynatrace. "However, a lack of cross-team collaboration and access to a single source of truth across the organization is hindering BizDevOps teams' ability to achieve this. By using disparate data from multiple monitoring and analytics solutions and adhering to a 'my-part-works-fine' view, they are wasting hundreds of hours and millions of dollars every year, rather than pursuing shared business goals backed by precise, holistic insights."

Additional findings from the report include:

■ 49% of CIOs say they have limited data and visibility into users' perspectives on how digital services are performing.

■ Only 14% of organizations have a single platform that enables cross-team collaboration and a true understanding of IT's business impact.

■ 49% of CIOs say IT and business teams work in silos.

■ 40% of CIOs say limited cross-team collaboration makes it more difficult to identify the severity of an issue and minimize its overall business impact.

■ To ease the burden on IT and avoid stretching limited resources beyond their limits, organizations are adopting new practices that rely on breaking down silos:
- 53% are adopting BizDevOps
- 50% are adopting Autonomous Cloud Operations
- 47% are adopting NoOps

"Without breaking down the silos between IT, development, and the business, organizations simply can't keep up with the accelerated pace of digital transformation," added Maciag. "Empowering teams with a single analytics and monitoring platform, rooted in a common data model and delivering precise and real-time insights, drives shared goals and improved business outcomes."

Methodology: The report is based on a global survey of 700 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace in 2020. The sample included 200 respondents in the US, 100 in the UK, France, and Germany, and 50 in Australia, Singapore, Brazil and Mexico, respectively.

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 ...

Can IT Keep Up with Digital Transformation?

IT leaders have growing concerns about their ability to keep up with digital transformation, according to a new report from Dynatrace, How to transform the way teams work to improve collaboration and drive better business outcomes.

The report shows traditional IT operating models with siloed teams and multiple monitoring and management solutions are proving ineffective at keeping up with cloud-native architectures. As a result, teams waste time manually combining data from disparate solutions in a reactive effort to solve challenges instead of focusing on driving innovation.


The survey also reveals:

■ 89% of CIOs say digital transformation has already accelerated, and 58% predict it will continue to speed up.

■ 93% of CIOs say IT's ability to maximize value for the business is hindered by challenges, including IT and business teams working in silos.

■ 74% of CIOs say they are fed up with the need to piece together data from multiple tools to assess the impact of IT investments on the business.

■ 40% of CIOs say limited collaboration across BizDevOps teams disrupts IT's ability to respond quickly to sudden changes in business needs.

■ 16% of an IT team's time is spent in meetings with the business to identify the causes of and solutions to problems. This issue alone costs organizations an average of $1.7 million annually due to lost productivity.

"As the pace of digital transformation accelerates, and modern, dynamic clouds introduce increasing complexity, the pressure on teams to make data-driven business decisions, and automate operations to deliver business value faster, has never been greater," said Mike Maciag, CMO at Dynatrace. "However, a lack of cross-team collaboration and access to a single source of truth across the organization is hindering BizDevOps teams' ability to achieve this. By using disparate data from multiple monitoring and analytics solutions and adhering to a 'my-part-works-fine' view, they are wasting hundreds of hours and millions of dollars every year, rather than pursuing shared business goals backed by precise, holistic insights."

Additional findings from the report include:

■ 49% of CIOs say they have limited data and visibility into users' perspectives on how digital services are performing.

■ Only 14% of organizations have a single platform that enables cross-team collaboration and a true understanding of IT's business impact.

■ 49% of CIOs say IT and business teams work in silos.

■ 40% of CIOs say limited cross-team collaboration makes it more difficult to identify the severity of an issue and minimize its overall business impact.

■ To ease the burden on IT and avoid stretching limited resources beyond their limits, organizations are adopting new practices that rely on breaking down silos:
- 53% are adopting BizDevOps
- 50% are adopting Autonomous Cloud Operations
- 47% are adopting NoOps

"Without breaking down the silos between IT, development, and the business, organizations simply can't keep up with the accelerated pace of digital transformation," added Maciag. "Empowering teams with a single analytics and monitoring platform, rooted in a common data model and delivering precise and real-time insights, drives shared goals and improved business outcomes."

Methodology: The report is based on a global survey of 700 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace in 2020. The sample included 200 respondents in the US, 100 in the UK, France, and Germany, and 50 in Australia, Singapore, Brazil and Mexico, respectively.

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 ...