- AI automates COBOL code exploration, maps dependencies, and quickly analyzes structural risks
- Engineers can effectively prioritize modernization based on technical risks and business value.
- Automated testing verifies that migrated COBOL components produce output identical to existing systems
Modernizing existing COBOL systems has long been a costly and laborious process that requires considerable human effort, as traditionally consulting teams spent months or even years mapping workflows, documenting dependencies, and untangling decades of accumulated business logic.
Hundreds of billions of lines of COBOL are still in production around the world, powering critical systems in banks, governments and airlines, but it has become increasingly difficult to find developers with the knowledge to interpret these systems.
However, Anthropic is now looking to supplant that, with its Claude AI platform aiming to take much of the heavy lifting off human workloads.
How AI makes code exploration and analysis easier
This shortage of expertise has historically slowed modernization projects and increased costs. However, Anthropic now believes that 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 is changing that,” the company said in a blog post.
Tools like Claude Code can map dependencies across thousands of COBOL lines, 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 duplicate 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 efficiently integrate AI output while maintaining oversight of the modernization plan, and once components are prioritized, AI can generate preliminary functional tests to verify that the migrated code produces outputs identical to those of the existing system.
Human teams then decide whether these automated tests are sufficient, which scenarios require manual verification, and which performance criteria should be maintained.
Implementation occurs gradually, with each module tested and validated before additional changes are made.
AI tools can translate COBOL logic into modern languages, create API wrappers around existing components, and create scaffolding for old and new code to work side by side.
This reduces the risk of large-scale outages and allows organizations to move forward with complex modernization projects.
AI also provides detailed insights into potential technical debt, isolated modules, and high-risk areas, enabling teams to plan modernization strategically as engineers can review these recommendations and sequence work to align with regulatory requirements, business priorities, and operational constraints.
Automated documentation and analysis gives teams complete situational awareness, but final decisions still rely on human judgment.
While this is a major win for many engineering teams, IBM, a major supplier of COBOL-based mainframes and enterprise systems, won’t be happy.
The company saw its shares 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 companies can face disruption as AI continues to reshape the modernization of legacy systems.
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