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Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Hybrid Cloud Management Platforms Help Control Data and Costs

More enterprises are implementing hybrid cloud management platforms as they diversify their IT environments to overcome the limits of relying solely on public clouds, according to a new research report published by Information Services Group (ISG).

The 2024 ISG Provider Lens™ global Private/Hybrid Cloud — Data Center Solutions report finds that organizations want the flexibility, scalability and agility of cloud computing while addressing their unique operational, regulatory and security challenges. In many cases, intelligently planned hybrid cloud platforms help them control expenses, data residency and compliance.

"Companies that are worried about the economy want to get more out of their IT investments," said Anay Nawathe, ISG cloud delivery lead. "With strong management, private and hybrid cloud infrastructures can maximize operational efficiency and financial resilience."

Along with these benefits, hybrid clouds bring more complexity, especially with the need for resource coordination across platforms and smooth data flow between on-premises and cloud infrastructure, ISG says. This requires specialized tools and skills, so enterprises are implementing hybrid cloud management platforms that let them get the most out of each cloud environment and minimize performance bottlenecks.

Organizations are also under pressure to make IT infrastructure more resilient, increasing the demand for backup and disaster recovery platforms, the report says. These create copies of critical data and systems so operations can quickly resume after a cyberattack or natural disaster. Scalable, secure and cost-effective resiliency solutions are becoming as crucial as primary on-premises and public cloud infrastructure.

AI and ML play growing roles in both cloud management and resilience platforms, ISG says. Companies are embracing AI and ML cloud management tools that use data from various sources to predict downtime and initiate self-healing tools, enhancing reliability. Such technologies are also being used to automate backup and recovery platforms, some of which use algorithms to identify and respond to anomalies or threats in real time.

"Faster response and recovery to a disruption minimizes any loss of revenue and productivity, while at the same time improving customer satisfaction," said Jan Erik Aase, partner and global leader, ISG Provider Lens Research. "Vendors are helping enterprises achieve these gains through AI and automation."

Companies are also tightening control over data in both cloud management and resilience platforms using privacy-enhancing features, the report says. These include access controls and encryption key management that allow them to define and enforce granular access policies.

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...