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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It's only natural that IT leaders would consider replacing systems they believe are outdated, especially if there is a perception that the systems cannot natively support a need of the business.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

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Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

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Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...