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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Technical debt refers to the accumulation of shortcuts, outdated technologies, and suboptimal coding practices that build up over time.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 It also undermines agility, leading to missed business opportunities and inefficient management of outdated technologies.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The "rip and replace" strategy, which involves removing outdated systems entirely and installing new ones, might seem like a straightforward solution to technical debt, but it carries several significant drawbacks.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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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

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...