The digital backbone of modern society often rests on a programming language created in 1959. COBOL, while ancient by tech standards, remains the invisible engine powering nearly 95% of American ATM transactions and essential government services. However, the expert workforce capable of maintaining these systems is rapidly retiring, leaving behind a "black box" of undocumented code that keeps public agencies trapped in expensive, outdated infrastructure.

Anthropic is now positioning its AI tools to bridge this generational divide. By utilizing Claude Code, organizations can automate the labor-intensive tasks of reverse-engineering business logic—the rules a computer follows to process data—and generating documentation for workflows that have existed for decades. Unlike typical updates, this involves extracting rules from systems built when original developers were first starting their careers, and where current documentation is often non-existent.

Shifting from manual refactoring to AI-assisted modernization could reduce project timelines from years to mere quarters. For local governments, this isn't just a technical upgrade; it's a financial necessity. As shown by Oregon's $106 million effort to replace a legacy system, the cost of technical debt is high. AI offers a scalable way to preserve critical logic while migrating to modern architectures.

The digital backbone of modern society often rests on a programming language created in 1959. COBOL, while ancient by tech standards, remains the invisible engine powering nearly 95% of American ATM transactions and essential government services. However, the expert workforce capable of maintaining these systems is rapidly retiring, leaving behind a "black box" of undocumented code that keeps public agencies trapped in expensive, outdated infrastructure.

Anthropic is now positioning its AI tools to bridge this generational divide. By utilizing Claude Code, organizations can automate the labor-intensive tasks of reverse-engineering business logic—the rules a computer follows to process data—and generating documentation for workflows that have existed for decades. Unlike typical updates, this involves extracting rules from systems built when original developers were first starting their careers, and where current documentation is often non-existent.

Shifting from manual refactoring to AI-assisted modernization could reduce project timelines from years to mere quarters. For local governments, this isn't just a technical upgrade; it's a financial necessity. As shown by Oregon's $106 million effort to replace a legacy system, the cost of technical debt is high. AI offers a scalable way to preserve critical logic while migrating to modern architectures.