AI drives faster modernisation of legacy COBOL systems
Incremental implementation and continuous validation let organisations translate COBOL code into modern languages while maintaining operational stability.
Critical to finance, airlines, and government, COBOL handles about 95% of US ATM transactions. Despite its ubiquity, the pool of developers able to read and maintain COBOL is shrinking as seasoned engineers retire and universities offer limited instruction.
Institutional knowledge is now embedded in decades-old code, and documentation often lags.
Modernising COBOL differs from typical software updates. It requires untangling intricate dependencies and reverse-engineering business logic that has evolved over decades.
Traditional modernisation efforts involved large teams of consultants over the years, resulting in high costs and lengthy timelines. AI tools are changing that paradigm by automating the most labour-intensive tasks.
AI-driven solutions like Claude Code map code dependencies, trace execution paths, document workflows, and identify risks. They provide teams with actionable insights for prioritisation, risk management, and refactoring, dramatically shortening modernisation timelines from years to months.
Human experts remain essential to reviewing AI recommendations, ensuring regulatory compliance, and making strategic decisions about which components to modernise first.
Implementation follows an incremental approach. AI translates COBOL logic into modern languages, creates integration scaffolding, and supports side-by-side operation with legacy components.
Continuous validation at each step reduces risk, allowing teams to build confidence as complex parts of the system are modernised. AI automation combined with expert oversight makes large-scale COBOL modernisation feasible.
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