As enterprises modernize their digital infrastructure, one critical challenge looms large: converting legacy software applications—often written in outdated or specialized programming languages—into scalable, maintainable systems built on modern technology stacks.
Traditionally, this has been a slow, resource-intensive endeavor, requiring deep domain expertise in both the legacy language and the new architecture. But the emergence of generative AI is fundamentally shifting this paradigm. AI-augmented developers now have the tools to refactor, translate, and validate legacy systems in ways previously unimaginable.
This white paper explores how generative AI doesn’t just accelerate legacy migration—it expands organizational capability. Using real examples, including the transformation of legacy Python systems into C# backends and TypeScript frontends, we outline both the potential and the limitations of AI-assisted code transformation.
A key finding from recent transformation initiatives is that GenAI empowers developers to operate beyond their existing technical boundaries. Engineers who may not have prior expertise in TypeScript or C# can now leverage GenAI to generate, compare, and validate syntactically correct, idiomatic code for those target languages.
In a field experiment by BCG and OpenAI, GenAI-augmented participants with no prior coding experience achieved 84% of the expert benchmark on a Python data-cleaning task. Moderate-experience users performed 10–20% better across all technical tasks compared to non-augmented peers.
The impact? Entire systems can be translated or rearchitected in weeks rather than quarters, dramatically reducing technical debt and freeing engineering teams to focus on innovation instead of maintenance.
Migrating legacy applications—especially between dynamic and statically typed languages—poses more than a surface-level challenge:
Thus, conversion requires a system-wide understanding—not just translation.
One enterprise AI team undertook the conversion of a legacy Python-based platform that performed scheduling, reporting, and data visualization. The goals: modernize the backend in C# for .NET microservices and deliver a responsive frontend in TypeScript using React.
The GenAI system helped:
Over 70% of the boilerplate conversion was handled by GenAI, with human reviewers focusing on edge cases, architecture decisions, and logic testing. Time-to-migration dropped by 60%.
As GenAI reduces the barrier to entry for cross-language and cross-platform work, it redefines what software engineers can contribute. Developers become architects, QA testers become test designers, and legacy support engineers begin shaping cloud-native applications.
This capability expansion has several implications:
In one GenAI experiment, non-coders equipped with AI tools were able to complete data analysis and modeling tasks on par with experienced data scientists—demonstrating how quickly AI can upskill teams when embedded properly.
Just as spreadsheet software didn’t replace accountants but made them more strategic, GenAI is making developers more creative and impactful.
To realize these gains, leaders must:
Ultimately, GenAI doesn’t remove the need for expertise—it raises the ceiling on what each developer can achieve.
Legacy system migration is no longer a daunting, multi-year project. With AI, organizations can cut costs, boost maintainability, and prepare their codebase for a composable, API-driven future. More importantly, they can expand what their teams are capable of.
As AI continues to evolve, companies that blend automation with human judgment will not only modernize faster—they’ll lead the next generation of software innovation.