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The Hidden Costs of IT Tool Sprawl

Bharath Rangarajan
Omnissa

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done.

According to a 2024 Forrester survey, 77% of U.S. technology decision-makers report moderate to extensive levels of technology sprawl, while 63% of these decision makers said they planned to pursue consolidation strategies. As IT teams come under pressure to do more with less while delivering a modern digital employee experience while ensuring systems remain secure and compliant; these fragmented tech stacks are heading toward a breaking point.

We are observing several key trends signaling the beginning of a significant wave of IT consolidation. Here's a closer look at the factors driving this shift.

Why IT Consolidation Can't Wait

Employees expect to be able to work from anywhere, on any device — without friction — and IT teams are under pressure to meet heightened expectations while simultaneously managing increased complexity and security risks. This difficult balance is making it increasingly clear that IT teams cannot afford to have systems that don't talk to each other. These fragmented tools can create bottlenecks, forcing IT to toggle between multiple dashboards just to provision a laptop or troubleshoot a simple issue.

Then there's the security challenge that siloed point solutions introduce. As cyberattacks become more sophisticated by the day, IT teams must shift to a proactive security posture — but that's difficult to achieve when scattered tools create blind spots across the environment. When an organization's endpoint protection lives in one system, patch management in another and device compliance monitoring in yet another, IT teams lack visibility to detect threats early and respond decisively — a compromised device might go unnoticed for days. Proactive security requires real-time intelligence and the ability to act on it instantly.

Finally, there's the money. Running all these separate tools is costly. Between licensing fees, support contracts, and the sheer inefficiency of managing multiple vendors, costs add up fast.

Making Consolidation Work: A Step-by-Step Guide

Consolidation sounds great in theory, but how do you actually do it without causing chaos? You need a plan.

Start with an honest assessment of what you have. Map out all your current tools and what they do. You'll probably find significant redundancy — three different tools doing endpoint monitoring or patch management when one comprehensive platform can handle it all.

Get everyone involved. Don't let this be just an IT decision. Talk to security teams, business units, and anyone who uses these tools. A unified platform needs to work for everyone, not just check technical boxes.

And, perhaps most importantly, don't try to do everything at once. Roll it out in phases. Start with one department or one use case, see how it goes, make adjustments, and then expand. That way you're not overcompensating and risking a massive disruption.

Why Now Is the Right Time

The shift to unified platforms is about working smarter. Simplicity and capability can coexist. The payoff is lower costs, smoother operations, stronger security posture, and the agility to keep up as AI accelerates and transforms how employees work.

For IT teams, consolidation means spending less time wrestling with integrations and more time on work that actually moves the needle. Fewer tools to manage means more bandwidth for strategic initiatives that improve how people work.

And that's where the real value shows up: employees get faster support, more consistent experiences, and technology that just works. When IT isn't buried in tool management, they can focus on what matters: making sure every person in the organization has what they need to do their best work.

Bharath Rangarajan is Chief Product Officer at Omnissa

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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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 Hidden Costs of IT Tool Sprawl

Bharath Rangarajan
Omnissa

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done.

According to a 2024 Forrester survey, 77% of U.S. technology decision-makers report moderate to extensive levels of technology sprawl, while 63% of these decision makers said they planned to pursue consolidation strategies. As IT teams come under pressure to do more with less while delivering a modern digital employee experience while ensuring systems remain secure and compliant; these fragmented tech stacks are heading toward a breaking point.

We are observing several key trends signaling the beginning of a significant wave of IT consolidation. Here's a closer look at the factors driving this shift.

Why IT Consolidation Can't Wait

Employees expect to be able to work from anywhere, on any device — without friction — and IT teams are under pressure to meet heightened expectations while simultaneously managing increased complexity and security risks. This difficult balance is making it increasingly clear that IT teams cannot afford to have systems that don't talk to each other. These fragmented tools can create bottlenecks, forcing IT to toggle between multiple dashboards just to provision a laptop or troubleshoot a simple issue.

Then there's the security challenge that siloed point solutions introduce. As cyberattacks become more sophisticated by the day, IT teams must shift to a proactive security posture — but that's difficult to achieve when scattered tools create blind spots across the environment. When an organization's endpoint protection lives in one system, patch management in another and device compliance monitoring in yet another, IT teams lack visibility to detect threats early and respond decisively — a compromised device might go unnoticed for days. Proactive security requires real-time intelligence and the ability to act on it instantly.

Finally, there's the money. Running all these separate tools is costly. Between licensing fees, support contracts, and the sheer inefficiency of managing multiple vendors, costs add up fast.

Making Consolidation Work: A Step-by-Step Guide

Consolidation sounds great in theory, but how do you actually do it without causing chaos? You need a plan.

Start with an honest assessment of what you have. Map out all your current tools and what they do. You'll probably find significant redundancy — three different tools doing endpoint monitoring or patch management when one comprehensive platform can handle it all.

Get everyone involved. Don't let this be just an IT decision. Talk to security teams, business units, and anyone who uses these tools. A unified platform needs to work for everyone, not just check technical boxes.

And, perhaps most importantly, don't try to do everything at once. Roll it out in phases. Start with one department or one use case, see how it goes, make adjustments, and then expand. That way you're not overcompensating and risking a massive disruption.

Why Now Is the Right Time

The shift to unified platforms is about working smarter. Simplicity and capability can coexist. The payoff is lower costs, smoother operations, stronger security posture, and the agility to keep up as AI accelerates and transforms how employees work.

For IT teams, consolidation means spending less time wrestling with integrations and more time on work that actually moves the needle. Fewer tools to manage means more bandwidth for strategic initiatives that improve how people work.

And that's where the real value shows up: employees get faster support, more consistent experiences, and technology that just works. When IT isn't buried in tool management, they can focus on what matters: making sure every person in the organization has what they need to do their best work.

Bharath Rangarajan is Chief Product Officer at Omnissa

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.