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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