24 Million Lines of COBOL. How Is AI Changing the Economics of Legacy Modernization? | Software Mind

Can AI safely modernize a system that has been running for 30 years, contains approximately 24 million lines of code, and handles millions of calls every day? How can organizations reduce the effort involved without increasing risk to business-critical processes? In the presentation “From Legacy COBOL to Modern Platforms: An AI-Powered Modernization Journey,” Damian Mazurek, Chief Innovation Officer at Software Mind, presents a real-world case study involving the modernization of a complex legacy environment at one of the largest mortgage organizations in the United States. The session demonstrates how artificial intelligence, a controlled migration methodology, and expert involvement changed the economics of a program covering 21 applications and approximately 24 million lines of code. Legacy systems remain the operational foundation of many financial institutions. Developed over several decades, before version control, unit testing, and modern engineering practices became standard, these systems often contain business logic that cannot be reliably reconstructed from outdated documentation. In many cases, the code itself is the most complete and accurate specification of how the business operates. The environment presented in the case study had been running in production for 30 years. The analyzed repository contained more than 100,000 files, approximately 24 million lines of code, and 21 legacy applications, while the shared services within scope handled approximately 2 million calls per day. The traditional modernization baseline assumed 90,000 person-days across a five-year program. The modernization process was supported by the SM Pulse platform. Its modules analyzed the full technology estate, created a dependency map, recovered business logic directly from the code, supported application migration to the target Java stack, and transferred schemas, data, and database logic. All activities took place within controlled workflows, with human experts involved at every quality gate. The approach consisted of five main stages: analyzing the legacy environment and its dependencies, dividing shared services into safe migration waves, recovering behavioral specifications from the code, building and testing the new components, and validating them against real traffic before any cutover decision. One of the primary risk-control mechanisms was a shadow run. The same production traffic was sent to both the legacy COBOL system and the new Java services. Responses, calculations, errors, and performance were compared automatically, while a human expert decided whether to accept the result or return it for correction. This approach replaced assumptions with evidence before switching production traffic. For one core service, approximately 90% of the migration was completed autonomously. Human experts stepped in after 4–6 hours of AI-driven work to review the result rather than write the solution manually. Across the program, the estimated effort decreased from 90,000 to 35,000 person-days, representing a 61% reduction through user acceptance testing. Analysis effort decreased by 48%, implementation effort by 64%, and testing effort by 70%. These figures describe the specific program presented in the session and should not be interpreted as a universal projection for every modernization project. The presentation demonstrates that AI does not eliminate experts from the transformation process. Instead, it changes their role. Rather than manually reconstructing the behavior of the legacy system, specialists verify AI-generated results at controlled quality gates. The organization is left not only with migrated code, but also with tests and living documentation of system behavior. This presentation offers valuable insights for banks, leasing companies, lenders, insurers, fintechs, and other organizations maintaining business-critical legacy systems. It shows how AI can support the analysis, migration, and testing of complex environments while preserving human control and reducing operational risk. This presentation was recorded during Ailleron Innovation Forum 2026, organized by Ailleron SA, on September 4, 2026. Speaker: Damian Mazurek Position: Chief Innovation Officer Company: Software Mind Presentation title: “From Legacy COBOL to Modern Platforms: An AI-Powered Modernization Journey” Event: Ailleron Innovation Forum 2026 Organizer: Ailleron SA Date: September 4, 2026 🔔 Subscribe to the Ailleron channel for insights into artificial intelligence, legacy modernization, banking, cloud technology, data, fintech, and digital transformation. #Ailleron #AIF2026 #SoftwareMind #AI #COBOL #LegacyModernization #LegacySystems #ArtificialIntelligence #SoftwareDevelopment #Banking #Fintech #FinancialServices #Cloud #DigitalTransformation #Java #AIModernization