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

John Van Siclen
Learn more about Dynatrace

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.

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

John Van Siclen
Learn more about Dynatrace

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.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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