How AI helps break the cost barrier to COBOL modernization

COBOL is everywhere. It handles an estimated 95% of ATM transactions in the US. Hundreds of billions of lines of COBOL run in production every day, powering critical systems in finance, airlines, and government.

Despite that, the number of people who understand it shrinks every year.

The developers who built these systems retired years ago, and the institutional knowledge they carried left with them. Production code has been modified repeatedly over decades, but the documentation hasn't kept up. Meanwhile, we aren't exactly minting replacements—COBOL is taught at only a handful of universities, and finding engineers who can read it gets harder every quarter.

Given these roadblocks, how can organizations modernize their systems without losing the reliability, availability, and data they’ve accumulated over decades? And without breaking anything?

Why modernizing COBOL is different

COBOL modernization differs fundamentally from typical legacy code refactoring. You aren’t just updating familiar code to use better patterns, you’re reverse engineering business logic from systems built when Nixon was president. You’re untangling dependencies that evolved over decades, and translating institutional knowledge that now exists only in the code itself. 

Modernizing a COBOL system once required armies of consultants spending years mapping workflows. This resulted in large timelines and high costs that few were willing to take on.

AI changes this.

Tools like Claude Code can automate the exploration and analysis phases that consume most of the effort in COBOL modernization. These tools can:

1. Map dependencies across thousands of lines of code
2. Document workflows that nobody remembers
3. Identify risks that would take human analysts months to surface 
4. Provide teams with the deep insights they need to make informed decisions

With AI, teams can modernize their COBOL codebase in quarters instead of years.

How AI changes COBOL modernization

AI excels at streamlining the tasks that once made COBOL modernization cost-prohibitive. With it, your team can focus on strategy, risk assessment, and business logic while AI automates the code analysis and implementation.

Automated exploration and discovery

AI starts by reading your entire COBOL codebase and mapping the structure. 

It identifies program entry points, traces execution paths through called subroutines, maps data flows between modules, and documents dependencies that span hundreds of files. 

This kind of mapping goes beyond simple call graphs. Shared data structures, file operations that create coupling between modules, initialization sequences that affect runtime behavior—these implicit dependencies don't show up in static analysis because they involve data shared through files, databases, or global state. They're also exactly what makes COBOL modernization risky, which is why automated discovery matters: it finds these hidden relationships before they cause problems during migration.

Workflow documentation also emerges out of this analysis. 

By tracing how data moves through a system from input to output, AI can produce diagrams and written descriptions of processing pipelines that nobody remembers building but everyone depends on.

Risk analysis and opportunity mapping

With the codebase mapped, AI can assess which components are safe to move and which need careful handling. Modules with high coupling can be more risky to modernize. Isolated components surface as candidates for early, independent modernization. Duplicated logic points to refactoring opportunities. Areas with accumulated technical debt get documented before they become migration surprises.

Strategic planning with expert oversight

This is where human judgment becomes essential. Your COBOL engineers bring the understanding of regulatory requirements, business priorities, operational constraints, and risk tolerance that AI cannot.

The planning phase develops a detailed roadmap that sequences modernization work strategically:

• AI suggests prioritization based on the risks, dependencies, and complexity it identified during analysis.
• Your team reviews these recommendations and decides which components to modernize first based on business value, technical risk, and organizational priorities.
• This is also when your team defines the target architecture, code standards, and integration requirements for modernized components.
  

Code testing and validation are also defined before any code changes:

• AI designs preliminary function tests that verify migrated code produces identical outputs to legacy COBOL. 
• Your team decides whether those tests are sufficient, which business scenarios need manual validation by subject-matter experts, and what performance benchmarks the modernized components need to meet.

Incremental implementation with continuous validation

Execution happens one component at a time, with validation at each step. AI translates COBOL logic into modern languages, creates API wrappers around legacy components that stay in place, and builds the scaffolding to run old and new code side by side during transition.

Each step either succeeds and gets validated, or fails and gets corrected while the scope is small. 

You never have massive changes in flight where failure means rolling back weeks of work. As your team sees modernized components passing tests, they gain confidence to tackle progressively more complex parts of the system.

Start your COBOL modernization

The approach outlined above works for COBOL systems of any size. 

Tools like Claude Code can automate much of the exploration and analysis work described, giving your team the comprehensive understanding they need to plan and execute migrations confidently.

Start with a single component or workflow that has clear boundaries and moderate complexity. Use AI to analyze and document it thoroughly, plan the modernization with your engineers, implement incrementally with testing at each step, and validate carefully.  This will build organizational confidence and surface adjustments needed for your systems.

The economics of COBOL modernization have shifted. AI makes the economics work by automating what used to require armies of consultants, freeing your engineers to make the migration decisions that require their domain expertise.

For a step-by-step guide, see the Code Modernization Playbook.

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