As organizations increasingly adopt multi-cloud environments to enhance speed, scale and agility for digital transformation, their technology stacks grow in complexity, and CIOs are struggling to master a rapidly expanding domain, a Dynatrace survey of 1,300 IT and business leaders found.

The report revealed the average multi-cloud environment spans 12 different platforms and services—this multitude of cloud-native services and platforms, from various providers supporting digital transactions, is complicating monitoring and management for IT and security teams.

This complexity challenges efforts to optimize user experience and enhance application resilience.

The survey underscores the rising complexity in managing multi-cloud environments and cloud-native architectures, driven by a surge in data and the proliferation of monitoring tools.

Nearly nine in 10 (88%) of organizations said they are seeing increased complexity of their technology stack in the past 12 months, with 51% expecting this to continue.

In addition, 84% of technology leaders surveyed said multi-cloud complexity makes it more difficult to protect applications from security vulnerabilities and attacks, highlighting the security issue IT teams are facing.

With the explosion of data from cloud-native environments rapidly approaching a point beyond humans’ ability to manage and to address this, 72% of organizations said they have adopted artificial intelligence for IT operations (AIOps).

This approach leverages AI and machine learning (ML) techniques to enhance and automate IT operations processes, employing advanced analytics and algorithmic capabilities to analyze vast amounts of data generated by IT systems, infrastructure and applications in real time.

The primary goal of AIOps is to improve the efficiency, performance and reliability of IT operations by identifying patterns, predicting potential issues and automating remediation tasks.

This proactive approach helps IT teams to detect and resolve issues more quickly, reduce downtime, optimize resource utilization and ultimately enhance the overall user experience.

However, AIOps relies on probabilistic methods that can be imprecise and time-consuming to implement.

“By adopting an advanced AI, analytics and automation strategy, organizations can easily access precise answers and have full visibility and control over their cloud ecosystems,” says Jay Livens, senior director of product marketing at Dynatrace.

The research also highlights the struggle organizations face in managing the explosion of data produced by cloud-native technology stacks.

This will require IT leaders to ensure that their teams are equipped to handle vast amounts of data—they must also be able to derive meaningful insights to drive business value and innovation.

“IT leaders need to move beyond traditional AIOps models to empower their teams to overcome the complexity of modern technology stacks,” Livens says.

By leveraging a hypermodal AI that combines multiple techniques, including causal, predictive and generative AI to power analytics and automation, organizations are better equipped to unlock a wealth of insights from their data to drive smarter decision-making and more efficient ways of working.

A significant portion of technology leaders express concerns about the limitations of manual approaches to log management and analytics, as well as the time-consuming nature of maintaining monitoring tools.

By embracing AI-powered analytics and automation, organizations can streamline log management and analytics processes and automate repetitive tasks associated with maintaining monitoring tools.

“In doing so and by reducing the need for manual efforts, teams can shift their focus from routine operations to strategic innovation, ultimately enhancing business value and gaining a competitive edge,” Livens explains.

The report also found nearly all (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.

Livens says organizations can optimize their cloud-native architectures and overcome the hurdles associated with traditional AIOps approaches by embracing mature AI, analytics and automation capabilities.

“This will enable them to access real-time, contextually relevant insights, empowering their teams to innovate while minimizing routine maintenance efforts,” he says.