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Managing Technical Debt to Support Successful Adoption of Emerging Technologies

Rebecca Dilthey
Rocket Software

Technical debt is a pressing issue for many organizations, stifling innovation and leading to costly inefficiencies. According to recent statistics, 7 out of 10 organizations report that technical debt significantly hampers their ability to innovate. Despite these challenges, 90% of IT leaders are planning to boost their spending on emerging technologies like AI in 2025, as highlighted by Forrester. As budget season approaches, it's important for IT leaders to address technical debt to ensure that their 2025 budgets are allocated effectively and support successful technology adoption.

Technical debt refers to the accumulation of shortcuts, outdated technologies, and suboptimal coding practices that build up over time. These "debts" arise from quick fixes that, while solving immediate problems, create long-term inefficiencies and maintenance challenges. This accumulated debt can severely hinder innovation by complicating system updates, increasing maintenance costs, and introducing vulnerabilities. It often results in slower development cycles and higher costs when implementing new technologies.

Beyond these operational challenges, technical debt impacts customer experience with poor performance, data quality issues, and potential breaches, ultimately harming reputation and driving customers away. It also undermines agility, leading to missed business opportunities and inefficient management of outdated technologies. Unsustainable cost structures and insufficient engineering skills further exacerbate these problems, increasing the risk of security issues and diminishing overall ROI from technology investments.

While technical debt is a common and sometimes unavoidable aspect of technology management, it is not insurmountable. Proactively recognizing and managing technical debt can mitigate its impact and facilitate a smoother transition to new technologies.

The High Cost of "Rip and Replace"

The "rip and replace" strategy, which involves removing outdated systems entirely and installing new ones, might seem like a straightforward solution to technical debt, but it carries several significant drawbacks. This approach often leads to considerable downtime, demands extensive resources for migration and training, and can disrupt ongoing business operations. The high costs associated with this method can strain budgets and delay the realization of benefits from new technologies.

Modernizing shouldn't require abandoning systems that have supported critical operations. For example, mainframes, which have been essential for decades in processing retail transactions and managing bank accounts, still play a crucial role. Instead of opting for a complete overhaul, modernizing in place involves updating and enhancing existing systems to better meet current needs. This approach aims to improve the functionality and efficiency of legacy systems without the need for a full replacement. By adopting this strategy, organizations can leverage their current infrastructure while gradually integrating newer technologies.

Modernization techniques streamline the maintenance and update processes for existing systems. For instance, refactoring code can make it more efficient, reducing the time and resources needed for ongoing maintenance. Containerization allows for faster deployment and scaling of applications, enabling organizations to respond more swiftly to changing needs.

Enhancing existing infrastructure rather than replacing it can help organizations avoid the high expenses associated with a full system overhaul, while also minimizing the risks associated with major system changes. Modernization in place often involves incremental updates, which can be more budget-friendly and less disruptive. This approach allows organizations to extend the life of their current investments while gradually integrating new technologies.

Embracing Emerging Technologies: Key Considerations

As organizations plan for 2025 and beyond, integrating emerging technologies such as AI, machine learning, and advanced analytics will be crucial for maintaining a competitive edge. To fully leverage these innovations, a solid foundation and effective management of technical debt are essential.

AI and machine learning have the potential to transform business operations and enhance customer insights significantly. However, realizing these benefits requires that existing systems are well-maintained and updated. Addressing technical debt and modernizing infrastructure will ensure that the foundation is robust enough to support these advanced tools.

Cloud technologies provide excellent opportunities for scalability and cost efficiency. To optimize these benefits, it is important to align cloud strategies with ongoing modernization efforts. Addressing technical debt and refining cloud usage will improve system performance and streamline operations. The Internet of Things (IoT) offers real-time data collection and automation benefits, but effective integration with legacy systems requires careful management of technical debt. Ensuring that older systems can interact seamlessly with IoT solutions will lead to better data utilization and increased operational efficiency.

To manage cloud costs and meet AI demands, establishing FinOps capabilities is vital. This approach provides visibility into specific expenditures, such as costs associated with AI calls or document processing, facilitating more accurate budgeting and helping to avoid unexpected costs.

Additionally, designing AI applications to minimize unnecessary computing resources, through techniques like caching and processing only essential data, can reduce costs. Evaluating the value of various AI use cases ensures that investment is focused on areas that offer the greatest benefit relative to their cost, maximizing overall return on investment.

Conclusion

As organizations look toward the future, balancing the maintenance and modernization of existing systems with the adoption of emerging technologies like AI, cloud computing, and IoT will be of the utmost importance. Addressing technical debt proactively allows CIOs and tech leaders to optimize resources, mitigate risks, and build a more agile infrastructure that fosters innovation. Through prioritizing modernization over complete system overhauls, organizations can make the most of their current investments, minimize disruptions, and stay on a steady growth path. Aligning these efforts with long-term business goals ensures that technology investments yield sustainable value.

In an era of economic uncertainty, strategic planning and continuous improvement are essential for managing technical debt, driving innovation, and achieving long-term success while remaining competitive.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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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 ...

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Managing Technical Debt to Support Successful Adoption of Emerging Technologies

Rebecca Dilthey
Rocket Software

Technical debt is a pressing issue for many organizations, stifling innovation and leading to costly inefficiencies. According to recent statistics, 7 out of 10 organizations report that technical debt significantly hampers their ability to innovate. Despite these challenges, 90% of IT leaders are planning to boost their spending on emerging technologies like AI in 2025, as highlighted by Forrester. As budget season approaches, it's important for IT leaders to address technical debt to ensure that their 2025 budgets are allocated effectively and support successful technology adoption.

Technical debt refers to the accumulation of shortcuts, outdated technologies, and suboptimal coding practices that build up over time. These "debts" arise from quick fixes that, while solving immediate problems, create long-term inefficiencies and maintenance challenges. This accumulated debt can severely hinder innovation by complicating system updates, increasing maintenance costs, and introducing vulnerabilities. It often results in slower development cycles and higher costs when implementing new technologies.

Beyond these operational challenges, technical debt impacts customer experience with poor performance, data quality issues, and potential breaches, ultimately harming reputation and driving customers away. It also undermines agility, leading to missed business opportunities and inefficient management of outdated technologies. Unsustainable cost structures and insufficient engineering skills further exacerbate these problems, increasing the risk of security issues and diminishing overall ROI from technology investments.

While technical debt is a common and sometimes unavoidable aspect of technology management, it is not insurmountable. Proactively recognizing and managing technical debt can mitigate its impact and facilitate a smoother transition to new technologies.

The High Cost of "Rip and Replace"

The "rip and replace" strategy, which involves removing outdated systems entirely and installing new ones, might seem like a straightforward solution to technical debt, but it carries several significant drawbacks. This approach often leads to considerable downtime, demands extensive resources for migration and training, and can disrupt ongoing business operations. The high costs associated with this method can strain budgets and delay the realization of benefits from new technologies.

Modernizing shouldn't require abandoning systems that have supported critical operations. For example, mainframes, which have been essential for decades in processing retail transactions and managing bank accounts, still play a crucial role. Instead of opting for a complete overhaul, modernizing in place involves updating and enhancing existing systems to better meet current needs. This approach aims to improve the functionality and efficiency of legacy systems without the need for a full replacement. By adopting this strategy, organizations can leverage their current infrastructure while gradually integrating newer technologies.

Modernization techniques streamline the maintenance and update processes for existing systems. For instance, refactoring code can make it more efficient, reducing the time and resources needed for ongoing maintenance. Containerization allows for faster deployment and scaling of applications, enabling organizations to respond more swiftly to changing needs.

Enhancing existing infrastructure rather than replacing it can help organizations avoid the high expenses associated with a full system overhaul, while also minimizing the risks associated with major system changes. Modernization in place often involves incremental updates, which can be more budget-friendly and less disruptive. This approach allows organizations to extend the life of their current investments while gradually integrating new technologies.

Embracing Emerging Technologies: Key Considerations

As organizations plan for 2025 and beyond, integrating emerging technologies such as AI, machine learning, and advanced analytics will be crucial for maintaining a competitive edge. To fully leverage these innovations, a solid foundation and effective management of technical debt are essential.

AI and machine learning have the potential to transform business operations and enhance customer insights significantly. However, realizing these benefits requires that existing systems are well-maintained and updated. Addressing technical debt and modernizing infrastructure will ensure that the foundation is robust enough to support these advanced tools.

Cloud technologies provide excellent opportunities for scalability and cost efficiency. To optimize these benefits, it is important to align cloud strategies with ongoing modernization efforts. Addressing technical debt and refining cloud usage will improve system performance and streamline operations. The Internet of Things (IoT) offers real-time data collection and automation benefits, but effective integration with legacy systems requires careful management of technical debt. Ensuring that older systems can interact seamlessly with IoT solutions will lead to better data utilization and increased operational efficiency.

To manage cloud costs and meet AI demands, establishing FinOps capabilities is vital. This approach provides visibility into specific expenditures, such as costs associated with AI calls or document processing, facilitating more accurate budgeting and helping to avoid unexpected costs.

Additionally, designing AI applications to minimize unnecessary computing resources, through techniques like caching and processing only essential data, can reduce costs. Evaluating the value of various AI use cases ensures that investment is focused on areas that offer the greatest benefit relative to their cost, maximizing overall return on investment.

Conclusion

As organizations look toward the future, balancing the maintenance and modernization of existing systems with the adoption of emerging technologies like AI, cloud computing, and IoT will be of the utmost importance. Addressing technical debt proactively allows CIOs and tech leaders to optimize resources, mitigate risks, and build a more agile infrastructure that fosters innovation. Through prioritizing modernization over complete system overhauls, organizations can make the most of their current investments, minimize disruptions, and stay on a steady growth path. Aligning these efforts with long-term business goals ensures that technology investments yield sustainable value.

In an era of economic uncertainty, strategic planning and continuous improvement are essential for managing technical debt, driving innovation, and achieving long-term success while remaining competitive.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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 ...