Advanced AI, analytics, and automation are vital to tackle tech stack complexity

97% of technology leaders find traditional AIOps models are unable to tackle the data overload, according to Dynatrace.

AIOps value

Organizations are drowning in data

The research reveals that organizations are continuing to embrace multi-cloud environments and cloud-native architectures to enable rapid transformation and deliver secure innovation. However, despite the speed, scale, and agility enabled by these modern cloud ecosystems, organizations are struggling to manage the explosion of data they create.

These research findings underscore the need for a mature AI, analytics, and automation strategy that moves beyond traditional AIOps models to drive lasting business value.

88% of organizations say the complexity of their technology stack has increased in the past 12 months, and 51% say it will continue to increase.

The average multi-cloud environment spans 12 different platforms and services. 87% of technology leaders say multi-cloud complexity makes it more difficult to deliver outstanding customer experiences, and 84% say it makes applications more difficult to protect.

86% of technology leaders say cloud-native technology stacks produce an explosion of data that is beyond humans’ ability to manage.

On average, organizations use ten different monitoring and observability tools to manage applications, infrastructure, and user experience.

85% of technology leaders say the number of tools, platforms, dashboards, and applications they rely on adds to the complexity of managing a multi-cloud environment.

“Cloud-native architectures have become mandatory for modern organizations, bringing the speed, scale, and agility they need to deliver innovation,” said Bernd Greifeneder, CTO at Dynatrace. “These architectures reflect a growing array of cloud platforms and services to support even the simplest digital transaction. The huge amount of data they produce makes it increasingly difficult to monitor and secure applications. As a result, critical business outcomes like customer experience are suffering, and it is becoming more difficult to protect against advanced cyber threats.”

AIOps probabilistic methods limit value

The continued reliance on fragmented cloud monitoring tools and manual analytics strategies creates a further challenge for IT and security teams. With no single source of truth or real-time insight, these teams increasingly struggle to access the answers needed to accelerate innovation and optimize digital services effectively.

The effort involved in managing all these tools, platforms, and dashboards means that teams often monitor only their mission-critical applications. This creates myriad blind spots across the technology stack, where teams are unable to access insights, making it easier for problems to arise.

  • 81% of technology leaders say manual approaches to log management and analytics cannot keep up with the rate of change in their technology stack and the volumes of data it produces.
  • 81% of technology leaders say the time their teams spend maintaining monitoring tools and preparing data for analysis steals time from innovation.
  • 72% of organizations have adopted AIOps to reduce the complexity of managing their multicloud environment.
  • 97% of technology leaders say probabilistic machine learning approaches have limited the value AIOps delivers due to the manual effort needed to gain reliable insights.

“Without the ability to transform the high volumes of diverse data from cloud-native architectures into real-time, contextually relevant insights, IT, development, security, and business teams struggle to understand what is happening in their environment and lack the answers needed to solve issues quickly and decisively,” continued Greifeneder.

“While many organizations turn to AIOps, they often encounter limited value due to reliance on probabilistic methods, which can be imprecise and time-consuming to implement. To overcome the complexity of modern technology stacks, organizations require advanced AI, analytics, and automation capabilities. By unifying diverse data, retaining its context, and powering analytics and automation with a hypermodal AI that combines multiple techniques, including causal, predictive, and generative AI, teams can unlock a wealth of insights from their data to drive smarter decision-making, intelligent automation, and more efficient ways of working,” Greifeneder concluded.


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