Revolutionizing Legacy Systems: AI's Role in Tackling Technical Debt

 

Insights from WeAreDevelopers World Congress 2025, Berlin

Yesterday at the WeAreDevelopers World Congress in Berlin (thanks Julia Kordick), I attended a fascinating session on AI-powered COBOL migration. As a management consultant working with public sector clients, this resonated deeply with our ongoing challenges in legacy system modernization.


The Technical Debt Crisis

Technical debt in public administrations and large organizations has reached critical levels. Like compound interest on a loan, postponed modernization efforts are making systems increasingly expensive and risky to maintain.


AI as the Game-Changer

Let me share two concrete examples of how AI is revolutionizing legacy system modernization:


Example 1: Tax Processing System

Traditional Migration:

20 developers × 18 months = 360 person-months

Cost: ~$5.4M (at $15K/month)

Quality: 85% code coverage

AI-Assisted Migration:

8 developers × 6 months = 48 person-months

Cost: ~$720K

Quality: 95% code coverage

Productivity Gain: 7.5x improvement ($5.4M/$720K)


Example 2: Healthcare Claims Processing

Traditional Approach:

Processing speed: 100 claims/hour

Error rate: 5%

Migration time: 24 months

AI-Modernized System:

Processing speed: 1000 claims/hour

Error rate: 0.5%

Migration time: 8 months

Productivity Gain: 10x improvement in processing speed, 10x reduction in errors


Here's a code snippet to create a visualization of technical debt accumulation over time:


Conclusion

The productivity gains from AI-assisted modernization are not just theoretical - they're being realized today. Organizations that embrace this transformation will gain significant competitive advantages, while those that delay risk falling further behind.

This technological shift represents one of the most significant opportunities for organizational transformation in the coming decade.


[This blog post was written based on observations at WeAreDevelopers World Congress 2025 in Berlin]









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