There is a type of technical debt that does not show up on management dashboards but weighs on every engineering decision: legacy applications that have been running for decades, that nobody fully understands anymore, and that nonetheless support critical business operations. Migrating them is costly, but not migrating them is even more.

For many organizations, the dilemma is not whether to modernize, but how to do it without paralyzing operations, without depending on a handful of experts who know the code by heart, and without taking on a level of risk the business cannot tolerate.

The weight of software that cannot simply be switched off

Applications built in C/C++ for Win32 environments were, in their time, robust and reliable solutions. Decades later, that same code has become a burden. Not because it fails, but because maintaining it requires a developer profile that is increasingly scarce, because its documentation is fragmentary or simply nonexistent, and because every modification introduces a risk that modern teams are not equipped to manage.

The outcome is predictable: high maintenance overhead, long and risky migration cycles, compromised knowledge transfer, and a modernization capacity that moves far slower than the business needs. Over time, the dependency on that code and on the few people who truly understand it becomes a real operational risk.

This was the situation facing a large-scale technology organization managing an extensive portfolio of legacy C/C++ Win32 applications, some approaching 40 years old. Documentation was limited, reliance on legacy specialists was high, and traditional migration cycles were too long and costly to address the problem at the scale the organization required.

The Solution: Accelerated modernization with our Code Migration Toolkit

To address this challenge, we utilized its GenAI-powered Code Migration Toolkit to modernize a legacy Win32 STL Viewer application to C++ using a Qt framework. The project served as a proof of concept that validated a scalable, low-risk approach to large-scale legacy code modernization.

AI automated significant portions of the migration process, improved code structure and quality, and generated consistent, context-aware documentation with interactive visualizations to support long-term maintainability.

The key capabilities of the solution included:

Automated legacy-to-modern code conversion: AI handled the most mechanical and repetitive parts of the migration, moving the code from Win32 to modern C++ with Qt in an automated fashion, drastically reducing the manual effort required.

Improved maintainability and cross-platform support: The migration to Qt did not only modernize the code, it also enabled cross-platform support, expanding deployment possibilities and reducing dependency on a specific operating environment.

AI-generated, standardized code documentation: One of the most critical problems with legacy software is the lack of documentation. The toolkit generated consistent, well-structured technical documentation throughout the migration process, closing a gap that typically requires considerable manual effort.

Reduced dependency on specialized legacy developers: By automating the process and generating quality documentation, the solution reduced reliance on scarce profiles with deep knowledge of the original code, distributing knowledge in a more sustainable way.

The Results: Less Time, More Automation, Better Code

The proof of concept delivered concrete results that validate the approach as a scalable solution for larger modernization programs:

• 50% to 60% reduction in migration time, with timelines dropping from approximately 160 to 190 hours down to around 80 hours per application, significantly compressing modernization schedules.
• 60% to 70% automation potential for larger-scale migration programs, meaning the majority of conversion work can be completed without intensive manual intervention.
• High-quality, modernized code aligned with current C++ and Qt best practices, ready to be maintained by modern engineering teams without requiring specialized knowledge of the original system.
• Improved developer onboarding and long-term supportability guaranteed by the documentation generated throughout the process.

Beyond the numbers, the proof of concept demonstrated something equally valuable: that it is possible to approach the modernization of decades-old legacy software in a systematic, predictable way, and at a pace the business can sustain.

The Oldest Technical Debt Can Also Be Solved with AI

This success story challenges a common industry belief: that the oldest and most complex legacy software is, by definition, the most difficult and costly to modernize. Generative AI applied to code migration changes that equation, automating the most intensive parts of the process and generating the knowledge artifacts that legacy software never had.

We brought to this project both the technical capability of the toolkit and the experience to design a proof of concept that was representative of the real challenge and extrapolable to large-scale modernization programs. The result was not just a successfully migrated application, but a working model that the organization can replicate across the rest of its legacy portfolio.

If your organization is facing the challenge of modernizing critical legacy applications, let's talk about how AI can accelerate that process.