Skip to main content

CIOs Say It Could Become Impossible to Manage Digital Performance as IT Complexity Grows

John Van Siclen

Three out of four (76%) of organizations think IT complexity could soon make it impossible to manage digital performance efficiently, according to the Top Challenges Facing CIOs in a Cloud-Native World report from Dynatrace.


The study further highlights that IT complexity is growing exponentially; a single web or mobile transaction now crosses an average of 35 different technology systems or components, compared to 22 just five years ago.

This growth has been driven by the rapid adoption of new technologies in recent years. However, the upward trend is set to accelerate, with 53% of CIOs planning to deploy even more technologies in the next 12 months. The research revealed the key technologies that CIOs will have adopted within the next 12 months include multi-cloud (95%), microservices (88%) and containers (86%).

As a result of this mounting complexity, IT teams now spend an average of 29% of their time dealing with digital performance problems; costing their employers $2.5 million annually.

As they search for a solution to these challenges, four in five (81%) CIOs said they think Artificial Intelligence (AI) will be critical to IT's ability to master increasing IT complexity; with 83% either already, or planning to deploy AI in the next 12 months.

81% of CIOs think AI will be critical to master increasing IT complexity

“Today’s organizations are under huge pressure to keep-up with the always-on, always connected digital economy and its demand for constant innovation,” said Matthias Scharer, VP of Business Operations, Dynatrace. “As a consequence, IT ecosystems are undergoing a constant transformation. The transition to virtualized infrastructure was followed by the migration to the cloud, which has since been supplanted by the trend towards multi-cloud. CIOs have now realized their legacy apps weren’t built for today’s digital ecosystems and are rebuilding them in a cloud-native architecture. These rapid changes have given rise to hyper-scale, hyper-dynamic and hyper-complex IT ecosystems, which makes it extremely difficult to monitor performance and, find and fix problems fast.”

The research further identified the challenges that organizations find most difficult to overcome as they transition to multi-cloud ecosystems and cloud native architecture.

Key findings include:

84% of CIOs say the dynamic nature of containers makes it difficult to understand their impact on application performance

■ 76% of CIOs say multi-cloud makes it especially difficult and time-consuming to monitor and understand the impact that cloud services have on the user-experience

■ 72% are frustrated that IT has to spend so much time setting-up monitoring for different cloud environments when deploying new services

■ 72% say monitoring the performance of microservices in real-time is almost impossible

■ 84% of CIOs say the dynamic nature of containers makes it difficult to understand their impact on application performance

■ Maintaining and configuring performance monitoring (56%) and identifying service dependencies and interactions (54%) are the top challenges CIOs identify with managing microservices and containers

“For cloud to deliver on expected benefits, organizations must have end-to-end visibility across every single transaction,” continued Scharer. “However, this has become very difficult because organizations are building multi-cloud ecosystems on a variety of services from AWS, Azure, Cloud Foundry and SAP amongst others. Added to that, the shift to cloud native architectures fragments the application transaction path even further.

“Today, one environment can have billions of dependencies, so, while modern ecosystems are critical to fast innovation, the legacy approach to monitoring and managing performance falls short. You can’t rely on humans to synthesize and analyze data anymore, nor a bag of independent tools. You need to be able to auto detect and instrument these environments in real time, and most importantly use AI to pinpoint problems with precision and set your environment on a path of auto-remediation to ensure optimal performance and experience from an end users’ perspective.”

Further to the challenges of managing a hyper-complex IT ecosystem, the research also found that IT departments are struggling to keep pace with internal demands from the business:

74% of CIOs said IT is under too much pressure to keep up with unrealistic demands from the business and end users

■ 74% of CIOs said IT is under too much pressure to keep up with unrealistic demands from the business and end users

■ 78% of CIOs highlighted that it is getting harder to find time and resources to answer the range of questions the business asks and still deliver everything else that is expected of IT

■ 80% of CIOs said it is difficult to map the technical metrics of digital performance to the impact they have on the business.

Methodology: This report, commissioned by Dynatrace, is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted in August 2017 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.

CIOs Say It Could Become Impossible to Manage Digital Performance as IT Complexity Grows

John Van Siclen

Three out of four (76%) of organizations think IT complexity could soon make it impossible to manage digital performance efficiently, according to the Top Challenges Facing CIOs in a Cloud-Native World report from Dynatrace.


The study further highlights that IT complexity is growing exponentially; a single web or mobile transaction now crosses an average of 35 different technology systems or components, compared to 22 just five years ago.

This growth has been driven by the rapid adoption of new technologies in recent years. However, the upward trend is set to accelerate, with 53% of CIOs planning to deploy even more technologies in the next 12 months. The research revealed the key technologies that CIOs will have adopted within the next 12 months include multi-cloud (95%), microservices (88%) and containers (86%).

As a result of this mounting complexity, IT teams now spend an average of 29% of their time dealing with digital performance problems; costing their employers $2.5 million annually.

As they search for a solution to these challenges, four in five (81%) CIOs said they think Artificial Intelligence (AI) will be critical to IT's ability to master increasing IT complexity; with 83% either already, or planning to deploy AI in the next 12 months.

81% of CIOs think AI will be critical to master increasing IT complexity

“Today’s organizations are under huge pressure to keep-up with the always-on, always connected digital economy and its demand for constant innovation,” said Matthias Scharer, VP of Business Operations, Dynatrace. “As a consequence, IT ecosystems are undergoing a constant transformation. The transition to virtualized infrastructure was followed by the migration to the cloud, which has since been supplanted by the trend towards multi-cloud. CIOs have now realized their legacy apps weren’t built for today’s digital ecosystems and are rebuilding them in a cloud-native architecture. These rapid changes have given rise to hyper-scale, hyper-dynamic and hyper-complex IT ecosystems, which makes it extremely difficult to monitor performance and, find and fix problems fast.”

The research further identified the challenges that organizations find most difficult to overcome as they transition to multi-cloud ecosystems and cloud native architecture.

Key findings include:

84% of CIOs say the dynamic nature of containers makes it difficult to understand their impact on application performance

■ 76% of CIOs say multi-cloud makes it especially difficult and time-consuming to monitor and understand the impact that cloud services have on the user-experience

■ 72% are frustrated that IT has to spend so much time setting-up monitoring for different cloud environments when deploying new services

■ 72% say monitoring the performance of microservices in real-time is almost impossible

■ 84% of CIOs say the dynamic nature of containers makes it difficult to understand their impact on application performance

■ Maintaining and configuring performance monitoring (56%) and identifying service dependencies and interactions (54%) are the top challenges CIOs identify with managing microservices and containers

“For cloud to deliver on expected benefits, organizations must have end-to-end visibility across every single transaction,” continued Scharer. “However, this has become very difficult because organizations are building multi-cloud ecosystems on a variety of services from AWS, Azure, Cloud Foundry and SAP amongst others. Added to that, the shift to cloud native architectures fragments the application transaction path even further.

“Today, one environment can have billions of dependencies, so, while modern ecosystems are critical to fast innovation, the legacy approach to monitoring and managing performance falls short. You can’t rely on humans to synthesize and analyze data anymore, nor a bag of independent tools. You need to be able to auto detect and instrument these environments in real time, and most importantly use AI to pinpoint problems with precision and set your environment on a path of auto-remediation to ensure optimal performance and experience from an end users’ perspective.”

Further to the challenges of managing a hyper-complex IT ecosystem, the research also found that IT departments are struggling to keep pace with internal demands from the business:

74% of CIOs said IT is under too much pressure to keep up with unrealistic demands from the business and end users

■ 74% of CIOs said IT is under too much pressure to keep up with unrealistic demands from the business and end users

■ 78% of CIOs highlighted that it is getting harder to find time and resources to answer the range of questions the business asks and still deliver everything else that is expected of IT

■ 80% of CIOs said it is difficult to map the technical metrics of digital performance to the impact they have on the business.

Methodology: This report, commissioned by Dynatrace, is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted in August 2017 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.