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Need A Change? Newer Isn't Necessarily Better

Rebecca Dilthey
Rocket Software

We all love new, shiny objects. When the washing machine dies, most of us don't run to an appliance store that sells used models or parts to repair your machine. You drop the money on a new one with all the bells and whistles.

Same goes for enterprise technology. When a legacy system starts to fail, our eyes tend to widen as we evaluate all the fancy toys on the market. Absolutely everything in hardware and software is about novelty — yesterday's innovation is tomorrow's doorstop.

While it doesn't appear as glamorous as the newest, most disruptive technology, often your old systems can be updated to deliver the performance your organization needs, saving your business the money and time associated with "rip and replace" projects.

During the pandemic, IT scaled back on higher risk new investments and looked at how they could invest in the systems they already had. In a post-COVID world, this trend hasn't changed, even as IT spending returns to pre-pandemic levels. Even though there is increased investment in projects to better service hybrid work environments as well as the hot new trends like hybrid cloud, organizations are realizing they can save valuable time and money by modernizing existing technology rather than throwing capital at the next big technology trend.

It's only natural that IT leaders would consider replacing systems they believe are outdated, especially if there is a perception that the systems cannot natively support a need of the business. In fact, that's often the first order of business for new CIOs when they walk into an IT organization. What often is overlooked, however, is the value of the solutions they have in place — the ones their teams are comfortable with and don't cost hundreds of thousands or even millions of dollars in a rip and replace project. Often these systems are fully capable of meeting their needs and enabling innovation and experimentation, especially if they are kept up to date.

What was that about millions of dollars?

The numbers are not insignificant. When adopting a rip and replace strategy, there are so many costs that aren't realized when initial project scoping occurs. For example, Projects this size often need full time managers, often delegated to consulting firms. Then there is the opportunity cost of employees having to devote time and effort to the project instead of their day-to-day job. And what about the huge amount of risk inherent in a re-platforming project. There are companies that have made the huge investment to replatform, only to find out at the end of the project there are some applications that are so central to how the business operates, they are in essence the hub of all operations and therefore too risky to touch. Millions of dollars and years of effort essentially for nothing.

Fans of rip and replace often counter that change needs to be made for operational reasons — but the data doesn't support it. While the product lines of many large systems are several decades old, the hardware and operating systems are updated every year. For some reason, though, that gets lost when we think of these older systems.

If you drive a Ford Focus, you know it's evolved dramatically from Ford Model T — so why is there this perception that these machines are like their ancestors?

In fact, not only do these systems offer the lower total cost of ownership and the unique security and transaction management capabilities inherent in mainframe and midrange systems, today's developers and programmers can use their favorite open-source languages and tools, new technologies like AI and ML, and more.

Additionally, there are software tools that enable non-RPG and -COBOL developers to cost-effectively create an "innovation layer" that makes it easy and efficient to modernize and automate applications and workflows running on these systems.

Businesses are coming to the realization that it's more valuable to update their existing tech stack on the heritage system — and upgrade to the latest OS — rather than turning to a rip and replace approach. After all, at the end of the day, the two main factors that matter most to IT leaders are: does my infrastructure and the tools I deliver to the business support the business strategy and goals; and can I ensure that support of the business in a cost-effective way. If investing in systems instead of replatforming gives IT the best of both worlds, it would seem a quest for something brand new might not be the best option.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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.

Need A Change? Newer Isn't Necessarily Better

Rebecca Dilthey
Rocket Software

We all love new, shiny objects. When the washing machine dies, most of us don't run to an appliance store that sells used models or parts to repair your machine. You drop the money on a new one with all the bells and whistles.

Same goes for enterprise technology. When a legacy system starts to fail, our eyes tend to widen as we evaluate all the fancy toys on the market. Absolutely everything in hardware and software is about novelty — yesterday's innovation is tomorrow's doorstop.

While it doesn't appear as glamorous as the newest, most disruptive technology, often your old systems can be updated to deliver the performance your organization needs, saving your business the money and time associated with "rip and replace" projects.

During the pandemic, IT scaled back on higher risk new investments and looked at how they could invest in the systems they already had. In a post-COVID world, this trend hasn't changed, even as IT spending returns to pre-pandemic levels. Even though there is increased investment in projects to better service hybrid work environments as well as the hot new trends like hybrid cloud, organizations are realizing they can save valuable time and money by modernizing existing technology rather than throwing capital at the next big technology trend.

It's only natural that IT leaders would consider replacing systems they believe are outdated, especially if there is a perception that the systems cannot natively support a need of the business. In fact, that's often the first order of business for new CIOs when they walk into an IT organization. What often is overlooked, however, is the value of the solutions they have in place — the ones their teams are comfortable with and don't cost hundreds of thousands or even millions of dollars in a rip and replace project. Often these systems are fully capable of meeting their needs and enabling innovation and experimentation, especially if they are kept up to date.

What was that about millions of dollars?

The numbers are not insignificant. When adopting a rip and replace strategy, there are so many costs that aren't realized when initial project scoping occurs. For example, Projects this size often need full time managers, often delegated to consulting firms. Then there is the opportunity cost of employees having to devote time and effort to the project instead of their day-to-day job. And what about the huge amount of risk inherent in a re-platforming project. There are companies that have made the huge investment to replatform, only to find out at the end of the project there are some applications that are so central to how the business operates, they are in essence the hub of all operations and therefore too risky to touch. Millions of dollars and years of effort essentially for nothing.

Fans of rip and replace often counter that change needs to be made for operational reasons — but the data doesn't support it. While the product lines of many large systems are several decades old, the hardware and operating systems are updated every year. For some reason, though, that gets lost when we think of these older systems.

If you drive a Ford Focus, you know it's evolved dramatically from Ford Model T — so why is there this perception that these machines are like their ancestors?

In fact, not only do these systems offer the lower total cost of ownership and the unique security and transaction management capabilities inherent in mainframe and midrange systems, today's developers and programmers can use their favorite open-source languages and tools, new technologies like AI and ML, and more.

Additionally, there are software tools that enable non-RPG and -COBOL developers to cost-effectively create an "innovation layer" that makes it easy and efficient to modernize and automate applications and workflows running on these systems.

Businesses are coming to the realization that it's more valuable to update their existing tech stack on the heritage system — and upgrade to the latest OS — rather than turning to a rip and replace approach. After all, at the end of the day, the two main factors that matter most to IT leaders are: does my infrastructure and the tools I deliver to the business support the business strategy and goals; and can I ensure that support of the business in a cost-effective way. If investing in systems instead of replatforming gives IT the best of both worlds, it would seem a quest for something brand new might not be the best option.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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.