The Social Security Administration’s attempt to modernize 60 million lines of COBOL code has started a debate about legacy system transformation. These systems process payments for 66 million Americans monthly, highlighting a tension many CTOs face: 43% of banking systems are built on COBOL, with 80% of in-person transactions relying on this aging language.
Yet pressure mounts to modernize infrastructure refined over decades — making hasty replacement a recipe for disaster.
The scale is staggering as 220 billion lines of COBOL are in use today. COBOL has fallen out of favor among coders, creating a critical problem when systems need updates, and companies lack COBOL experts. But the real breakthrough is not in workforce numbers; it’s how generative AI is fundamentally changing what modernization means. For CTOs overseeing systems where five minutes of downtime could impact millions, the path forward demands precision, not promises.
Aging Infrastructure and a Retiring Talent Pool
In just over 10 years, most skilled US developers with knowledge to run these legacy systems will be at or past retirement age. Offshore outsourcing prospects also look dire.
Soon, COBOL’s essential programming and business application knowledge will be lost, making deferred modernization more difficult and costly. Additionally, maintaining legacy systems is increasingly challenging, and failure to modernize could mean operational inefficiencies, security vulnerabilities, and an inability to keep pace with innovation.
So why haven’t CTOs modernized their core systems? The answer lies in complexity and technical debt. Core system renewal presents architectural challenges, integration complexities and uncertain migration paths. Financial services CTOs know this reality intimately. Their COBOL systems process trillions in transactions daily without fail. The technical case for wholesale replacement often doesn’t add up when existing systems deliver proven performance and stability.
Enter GenAI: A Tool for Modernization
However, the emergence of GenAI is reshaping the conversation. GenAI’s ability to analyze, interpret and transform legacy code opens new possibilities, making modernization faster, more efficient, and more scalable. This shift draws attention from technology leaders who once viewed system modernization as architecturally prohibitive.
The key difference? GenAI enables selective modernization rather than risky rip-and-replace approaches. Development teams are increasingly turning to AI to accelerate their mainframe modernization initiatives. These tools can process millions of lines of undocumented COBOL code, extract business logic and generate modern language equivalents while preserving critical business rules.
While AI is a significant driver of change, modernizing risk-sensitive core systems is not a one-size-fits-all process. For CTOs in financial institutions where system reliability is paramount and architectural integrity must be maintained, the approach must be surgical rather than sweeping.
Strategies for Success
How can this transition be done successfully? The first step requires thorough legacy system analyses, technical debt assessment and architectural modernization planning. After determining their modernization strategy, CTOs should focus on establishing robust testing frameworks to guarantee system integrity and performance benchmarks during the entire migration process.
Selective and Continuous Rebuilding
While some CTOs feel it necessary to replace entire systems, the contrary is often more practical. Instead of replacing everything at once, development teams should modernize applications incrementally. This approach acknowledges a fundamental truth: not all COBOL code needs replacing. Transaction processing systems refined over decades often outperform modern alternatives in terms of throughput and reliability.
By choosing a phased approach, CTOs can ensure system stability, so performance benchmarks and architectural requirements are met while maintaining complete control over technical governance. Financial services CTOs can start with API-enabling legacy systems and modernizing integration layers while keeping proven core processing engines intact.
AI-Powered Code Transformation
When modernizing core systems, CTOs can leverage AI for automated code transformation. Large language models have unlocked the ability to analyze existing COBOL code, map system dependencies, and automatically generate modern code while maintaining algorithmic integrity, rapidly accelerating development cycles while reducing technical risk.
While CTOs should utilize AI for their modernization journey, engineering teams must prioritize code quality and system testing. AI can be a powerful tool for code conversion, but only when integrated with comprehensive testing pipelines and code review processes. In financial services, where system reliability is non-negotiable and performance requirements are stringent, AI serves best as a development accelerator rather than an autonomous solution.
Expert Guidance and Long-Term Support
Legacy system modernization is truly a journey, not a one-time project. CTOs must build skilled and experienced teams when approaching system transformation. This includes both mainframe specialists who understand existing architectures and cloud-native developers who can design modern microservices architectures.
Stay Competitive and Modernize Today
The time to modernize is now. COBOL applications running stably on mainframes do not need 100% conversion to other languages. Selective application modernization can be limited to specific components where technical benefits are clear: a pragmatic solution as COBOL’s monolithic architectures can be challenging to decompose.
CTOs who prioritize modernization with a focus on system resilience and architectural flexibility will position their organizations for long-term success. The winners will be those who use Gen AI to enhance their legacy systems’ performance while building modern capabilities, not those who attempt wholesale replacements that risk decades of proven system behavior.