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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

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...