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CIOs Say: The Need for Rapid Innovation Puts Customer Experience at Risk

John Van Siclen

CIOs of 73% of organizations say the need for speed in digital innovation is putting customer experience at risk, according to an independent global survey of 800 CIOs commissioned by Dynatrace.

The study found that on average, organizations release new software updates three times per working hour, as they push to keep up with competitive pressures and soaring consumer expectation.


Looking ahead, 89% of CIOs said they will need to release updates even faster in the future. However, the speed of releases can come at a cost. Nearly two-thirds (64%) of CIOs admitted they are forced to compromise between faster innovation and the need to ensure customers have a great software experience.

“Every organization on the planet is a software company these days. Market leaders like Amazon are releasing multiple software updates every second. Consequently, the modern approach to delivering software is about agile, fast development cycles and releasing into dynamic, hybrid multi-cloud environments,” said Andreas Grabner, DevOps Activist, Dynatrace. “Yet end users also expect the steady flow of new features and updates to work perfectly, without compromise. The challenge for IT is to deliver fast, while moving to a cloud native architecture and maintaining user experience.”

The new CIO report looks at the pains organizations face as they strive for new heights of agility and speed. Key findings include:

Cloud enables agility but CIOs struggle with:

■ Ensuring software performance isn’t negatively impacted (67%)

■ Identifying if moving an application to the cloud has delivered the desired benefits (57%)

■ Understanding if an application is well-suited to the cloud (55%)

■ Re-architecting legacy applications for the cloud (51%)

■ Ensuring the user-experience isn’t impacted during the migration process (48%)

Lack of collaboration and visibility leads to innovation delays:

■ 78% of CIOs said their organization has experienced IT project delays that could have been prevented if development and operations teams were able to easily collaborate

■ CIOs said digital transformation initiatives were most frequently derailed by:

- IT outages caused by external issues (55%)

- IT outages caused by internal changes (50%)

- Rectifying bad code that has been pushed through the pipeline (45%)

Organizations face challenges as they turn to DevOps to improve collaboration:

■ 68% of organizations have implemented or are exploring the possibilities of a DevOps culture to improve collaboration and drive faster innovation

■ 74% of CIOs said that DevOps efforts are often being undermined by the absence of shared data and tools, which makes it difficult for IT teams to obtain a single view of "the truth"

■ 56% of CIOs identified differences in priorities between departmental siloes as an additional barrier to DevOps adoption

“The challenge for all organizations is to get a holistic view of the DevOps pipeline – from idea to code to experience. As DevOps has matured, enterprises are looking to automate and integrate their software development to release faster, with higher quality, and with less manual effort. It’s exciting to see AI play an even greater role in reducing manual tasks, so we can do what we love – write better software, deploy with speed, and deliver perfect software experiences,” adds Grabner.

About the Survey: This report, commissioned by Dynatrace, is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany and China, and 50 in Australia, Singapore, Brazil and Mexico respectively.

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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 ...

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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 ...

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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.

CIOs Say: The Need for Rapid Innovation Puts Customer Experience at Risk

John Van Siclen

CIOs of 73% of organizations say the need for speed in digital innovation is putting customer experience at risk, according to an independent global survey of 800 CIOs commissioned by Dynatrace.

The study found that on average, organizations release new software updates three times per working hour, as they push to keep up with competitive pressures and soaring consumer expectation.


Looking ahead, 89% of CIOs said they will need to release updates even faster in the future. However, the speed of releases can come at a cost. Nearly two-thirds (64%) of CIOs admitted they are forced to compromise between faster innovation and the need to ensure customers have a great software experience.

“Every organization on the planet is a software company these days. Market leaders like Amazon are releasing multiple software updates every second. Consequently, the modern approach to delivering software is about agile, fast development cycles and releasing into dynamic, hybrid multi-cloud environments,” said Andreas Grabner, DevOps Activist, Dynatrace. “Yet end users also expect the steady flow of new features and updates to work perfectly, without compromise. The challenge for IT is to deliver fast, while moving to a cloud native architecture and maintaining user experience.”

The new CIO report looks at the pains organizations face as they strive for new heights of agility and speed. Key findings include:

Cloud enables agility but CIOs struggle with:

■ Ensuring software performance isn’t negatively impacted (67%)

■ Identifying if moving an application to the cloud has delivered the desired benefits (57%)

■ Understanding if an application is well-suited to the cloud (55%)

■ Re-architecting legacy applications for the cloud (51%)

■ Ensuring the user-experience isn’t impacted during the migration process (48%)

Lack of collaboration and visibility leads to innovation delays:

■ 78% of CIOs said their organization has experienced IT project delays that could have been prevented if development and operations teams were able to easily collaborate

■ CIOs said digital transformation initiatives were most frequently derailed by:

- IT outages caused by external issues (55%)

- IT outages caused by internal changes (50%)

- Rectifying bad code that has been pushed through the pipeline (45%)

Organizations face challenges as they turn to DevOps to improve collaboration:

■ 68% of organizations have implemented or are exploring the possibilities of a DevOps culture to improve collaboration and drive faster innovation

■ 74% of CIOs said that DevOps efforts are often being undermined by the absence of shared data and tools, which makes it difficult for IT teams to obtain a single view of "the truth"

■ 56% of CIOs identified differences in priorities between departmental siloes as an additional barrier to DevOps adoption

“The challenge for all organizations is to get a holistic view of the DevOps pipeline – from idea to code to experience. As DevOps has matured, enterprises are looking to automate and integrate their software development to release faster, with higher quality, and with less manual effort. It’s exciting to see AI play an even greater role in reducing manual tasks, so we can do what we love – write better software, deploy with speed, and deliver perfect software experiences,” adds Grabner.

About the Survey: This report, commissioned by Dynatrace, is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany and China, and 50 in Australia, Singapore, Brazil and Mexico respectively.

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.