'Modernizing a COBOL system once required armies of consultants spending years mapping workflows... AI changes this': Anthropic says AI could help keep COBOL running for a long time to come — but IBM won't be happy

• AI automates COBOL code exploration, maps dependencies, and analyzes structural risks quickly
• Engineers can prioritize modernization based on technical risk and business value efficiently
• Automated tests verify that migrated COBOL components produce identical outputs to legacy systems

Modernizing legacy COBOL systems has long been a costly and labor-intensive process that requires extensive human effort, as traditionally, teams of consultants spent months or even years mapping workflows, documenting dependencies, and untangling decades of accumulated business logic.

Hundreds of billions of lines of COBOL still run in production worldwide, powering critical systems in banking, government, and airlines, yet finding developers with the knowledge to interpret these systems has become increasingly difficult.

Now, however, Anthropic is looking to supplant this, with its Claude AI platform aiming to take much of the heavy lifting away from human workloads.

How AI aids code exploration and analysis

This scarcity of expertise has historically slowed modernization projects and increased costs - however, Anthropic now believes AI can automate much of the exploration phase that once consumed most human effort.

“Modernizing a COBOL system once required armies of consultants spending years mapping workflows… AI changes this,” the company said in a blog post.

Tools like Claude Code can map dependencies across thousands of lines of COBOL, trace data flows between modules, and document workflows that current staff no longer actively remember.

These automated processes identify risks, isolate tightly coupled components, and flag duplicated or potentially fragile code.

By analyzing these structural and functional relationships, AI can prioritize which components to modernize first based on technical risk, business value, and organizational priorities.

The best laptops for programming allow engineers to integrate AI outputs efficiently while maintaining oversight of the modernization plan, and once components are prioritized, AI can generate preliminary function tests to verify that migrated code produces identical outputs to the legacy system.

Human teams then decide whether these automated tests are sufficient, which scenarios require manual verification, and what performance benchmarks must be maintained.

Implementation proceeds incrementally, with each module tested and validated before additional changes are made.

AI tools can translate COBOL logic into modern languages, create API wrappers around legacy components, and build scaffolding that allows old and new code to operate side by side.

This reduces the risk of large-scale failures and enables organizations to move forward with complex modernization projects.

AI also provides detailed insights into potential technical debt, isolated modules, and high-risk areas, allowing teams to plan modernization strategically - as engineers can review these recommendations and sequence the work to align with regulatory requirements, business priorities, and operational constraints.

Automated documentation and analysis give teams comprehensive situational awareness, but final decisions still rely on human judgment.

While this is a major win for many engineering teams, IBM, a major vendor of COBOL-powered mainframes and enterprise systems, will not be pleased.

The company saw its stock fall sharply after Anthropic announced that Claude Code could automate much of the labor-intensive modernization process.

AI’s ability to replace work traditionally done by human consultants threatens parts of IBM’s business model.

This shows that even long-established enterprise software vendors may face disruption as AI continues to reshape legacy system modernization.

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