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Enterprises Looking to AI for Smarter IT Management

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from global AI-centered technology research and advisory firm Information Services Group (ISG).

The ISG Buyers Guides™ for IT Management, produced by ISG Software Research, find AI plays a growing role in comprehensive software frameworks for IT observability, operations management and FinOps. The need for IT management software is growing, the research says, as enterprises transition to more agile and cloud-centric architectures. AI-powered software is also helping enterprises manage and optimize the delivery, performance and responsiveness of IT services.

"IT leaders need effective operations and service management more than ever for resilience and long-term success," said Jeff Orr, Research Director for IT, ISG Software Research. "Enterprises are adopting multiple tools and platforms to support ongoing IT innovation while controlling costs."

Economic pressures, heightened cybersecurity risks and the growing need to support hybrid and remote workers have intensified the need for software that helps manage and operate IT systems and services. CIOs and IT leaders often cite these trends when building a business case for new investments in this area, ISG says.

Enterprises are strategically integrating AIOps, which uses machine learning to automate IT processes, and holistic observability practices, which help companies understand the state of IT systems through their outputs, the reports say. Together, these approaches enable real-time monitoring of application performance and infrastructure health, and provide the ability to predict and mitigate potential issues, allowing companies deliver high-quality IT services with less manual intervention. Through 2026, ISG expects 40% of enterprises to fund AIOps strategies to streamline operations and optimize resources.

AI is enabling IT teams to generate insights from vast amounts of data, the reports say. By 2027, ISG expects software providers to release GenAI-driven tools for processes such as incident management, resource allocation and performance forecasting. GenAI is also changing IT service management, introducing features such as automatic command-line generation to help teams handle service requests.

In the future, agentic AI will enable intelligent workflows with semi-autonomous actions and decisions to manage incidents in real time, ISG says. Self-healing mechanisms driven by agentic AI may be able to resolve issues automatically, allowing IT teams to focus on strategic initiatives. However, the reports say, enterprises need to be aware of unique challenges involving governance, compliance, business risk and other aspects of these emerging technologies.

As companies move more data and workloads to the cloud, FinOps is becoming a critical tool for managing costs and finances. FinOps strategies foster collaboration among finance, IT and business teams to share responsibility for managing costs and resource consumption. ISG expects one in five enterprises to invest in coordinated FinOps efforts by IT and finance departments through 2026.

"CIO and IT leaders are looking to unify the management of their IT environments and technology services through software made more intelligent with AI," said Mark Smith, chief software analyst and partner, ISG Software Research. "For the first time, our portfolio of IT management software research introduces a unified framework for evaluating software providers and products operating in this space."

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 ...

Enterprises Looking to AI for Smarter IT Management

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from global AI-centered technology research and advisory firm Information Services Group (ISG).

The ISG Buyers Guides™ for IT Management, produced by ISG Software Research, find AI plays a growing role in comprehensive software frameworks for IT observability, operations management and FinOps. The need for IT management software is growing, the research says, as enterprises transition to more agile and cloud-centric architectures. AI-powered software is also helping enterprises manage and optimize the delivery, performance and responsiveness of IT services.

"IT leaders need effective operations and service management more than ever for resilience and long-term success," said Jeff Orr, Research Director for IT, ISG Software Research. "Enterprises are adopting multiple tools and platforms to support ongoing IT innovation while controlling costs."

Economic pressures, heightened cybersecurity risks and the growing need to support hybrid and remote workers have intensified the need for software that helps manage and operate IT systems and services. CIOs and IT leaders often cite these trends when building a business case for new investments in this area, ISG says.

Enterprises are strategically integrating AIOps, which uses machine learning to automate IT processes, and holistic observability practices, which help companies understand the state of IT systems through their outputs, the reports say. Together, these approaches enable real-time monitoring of application performance and infrastructure health, and provide the ability to predict and mitigate potential issues, allowing companies deliver high-quality IT services with less manual intervention. Through 2026, ISG expects 40% of enterprises to fund AIOps strategies to streamline operations and optimize resources.

AI is enabling IT teams to generate insights from vast amounts of data, the reports say. By 2027, ISG expects software providers to release GenAI-driven tools for processes such as incident management, resource allocation and performance forecasting. GenAI is also changing IT service management, introducing features such as automatic command-line generation to help teams handle service requests.

In the future, agentic AI will enable intelligent workflows with semi-autonomous actions and decisions to manage incidents in real time, ISG says. Self-healing mechanisms driven by agentic AI may be able to resolve issues automatically, allowing IT teams to focus on strategic initiatives. However, the reports say, enterprises need to be aware of unique challenges involving governance, compliance, business risk and other aspects of these emerging technologies.

As companies move more data and workloads to the cloud, FinOps is becoming a critical tool for managing costs and finances. FinOps strategies foster collaboration among finance, IT and business teams to share responsibility for managing costs and resource consumption. ISG expects one in five enterprises to invest in coordinated FinOps efforts by IT and finance departments through 2026.

"CIO and IT leaders are looking to unify the management of their IT environments and technology services through software made more intelligent with AI," said Mark Smith, chief software analyst and partner, ISG Software Research. "For the first time, our portfolio of IT management software research introduces a unified framework for evaluating software providers and products operating in this space."

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 ...