Every organization has them: the jQuery spaghetti that runs the internal dashboard, the Silverlight app that nobody can update, the Perl backend that only one person understood — and they left two years ago. Legacy systems that are too important to turn off and too expensive to rewrite. Until now.
AI-assisted coding is fundamentally changing the economics of legacy migration. The work that made rewrites prohibitively expensive — reverse-engineering undocumented business logic, mapping tangled dependencies, translating idioms between languages — is precisely the work that AI handles well. What used to be a multi-year, million-dollar programme can now be scoped, planned, and executed in a fraction of the time and cost.
This guide covers the migration paths we see most often, explains where AI delivers the biggest leverage, and makes the case that features you shelved years ago because they were too expensive are now within reach.
The opportunity
AI-assisted migration cuts reverse-engineering effort by 60% — turning the most expensive phase of any rewrite into the most automated.
Legacy migrations have been on the roadmap of every technology leader for years. They keep getting deferred because the cost-benefit math never quite worked: the rewrite was expensive, risky, and delivered the same functionality users already had. Why spend millions to get back to zero?
Three things have changed simultaneously:
AI can read legacy code
LLMs can parse jQuery, Perl, COBOL, VB6, and Silverlight XAML — understanding intent, not just syntax. The reverse-engineering bottleneck has been automated.
Legacy talent is disappearing
The developers who wrote these systems are retiring or moving on. Every year you wait, the institutional knowledge required to migrate safely gets thinner.
Maintenance costs are compounding
Security patches for dead frameworks, compatibility hacks, specialist contractors charging premium rates — the cost of not migrating is rising faster than the cost of migrating.
The most expensive phase of any legacy migration is not writing the new code. It is understanding the old code. Teams spend weeks — sometimes months — reading through undocumented modules, interviewing long-tenured staff, and building a mental model of business rules that were never written down. This is the phase where projects stall, budgets inflate, and leadership loses confidence.
AI collapses this phase. An LLM can ingest an entire legacy codebase, trace execution paths, explain what each module does in plain language, and generate documentation that would have taken a team of analysts weeks to produce. From there, it can propose equivalent implementations in modern frameworks — not perfect, but an 80% starting point that experienced engineers can review and harden.
Code comprehension
AI reads legacy languages fluently — jQuery, Perl, VB6, COBOL, XAML. It explains what code does, why it exists, and what will break if you change it.
Dependency mapping
Automatically traces which modules depend on which, identifies shared state, and flags circular dependencies that will complicate migration sequencing.
Translation at scale
Generates equivalent code in the target framework — React components from jQuery, Python services from Perl, C# from VB6 — maintaining the same business logic.
Test generation
Creates test suites from observed behaviour of the legacy system, giving you a safety net before you start replacing components.
Documentation recovery
Produces architecture diagrams, API contracts, and data-flow documentation from codebases that had none — capturing knowledge before legacy developers leave.
Incremental migration planning
Recommends a migration sequence that minimizes risk: which modules to migrate first, where to draw the strangler-fig boundary, and what to leave for last.
less time spent on reverse-engineering legacy code
of boilerplate translation handled by AI on first pass
times faster overall migration timeline compared to traditional rewrites
Frontend migrations are often the most visible and the most politically charged — users notice immediately. They are also where AI delivers the most dramatic acceleration, because frontend code is heavily pattern-based: components, event handlers, state management, and layout. AI excels at recognizing and translating these patterns.
jQuery / vanilla JS → React
This is the migration we see most often. The typical jQuery codebase has DOM manipulation scattered across dozens of files, no component model, global variables acting as state, and event handlers that trigger cascading side effects nobody fully understands.
AI reads the jQuery selectors and event bindings, identifies implicit components (the blocks of DOM that are always manipulated together), extracts state into React hooks, and generates a component tree. The result is not production-ready on the first pass — but it gives engineers a structured starting point instead of a blank editor.
The biggest win is usability. Moving from server-rendered pages with jQuery sprinkled on top to a proper React SPA with client-side routing, instant transitions, and a modern component library transforms how the application feels — and that transformation is now achievable in weeks, not quarters.
Silverlight → React
Silverlight applications are some of the hardest to migrate manually. The XAML-based UI has no direct web equivalent, data bindings are deeply nested, and business logic is often embedded in view models that were never designed to be portable.
AI can parse XAML layouts and map them to React component hierarchies, translate data-binding expressions into React state and effect patterns, and extract the C# view-model logic into service layers that the new frontend can consume via API. The “archaeology phase” — figuring out what the Silverlight app actually does — goes from months to days.
Blazor Server → React / Next.js
Blazor Server ties the frontend to a persistent SignalR connection, making the application difficult to scale, expensive to host, and impossible to deploy to a CDN. The Razor component syntax is similar enough to JSX that translation is relatively mechanical — but the state synchronisation model is fundamentally different.
AI translates Razor components to React, maps server-managed state to client-side state management (or API calls), and decouples the frontend into an independently deployable Next.js application. The result is a faster, more scalable, and more portable frontend — with a much larger talent pool available to maintain it.
Backend migrations carry higher risk and higher reward. The legacy backend is often the system of record — the source of truth for business data and business rules. Getting it wrong means data corruption, compliance violations, or silent calculation errors that take months to surface.
The challenge with backends written in Perl, VB6, Classic ASP, or COBOL is rarely the language itself. It is that the code is the documentation. Business rules — pricing logic, regulatory calculations, exception handling — are encoded in code that nobody wrote down anywhere else. When the developer who wrote it leaves, the knowledge leaves with them.
AI addresses this directly. It reads the legacy code, explains what each module does in plain language, generates equivalent implementations in standard modern languages, and — critically — creates the documentation and test suites that should have existed from the start. The migration does not just replace the technology; it recovers the knowledge.
jQuery / custom JS → React + TypeScript
The challenge
Spaghetti DOM manipulation, no component model, global state scattered across files, tightly coupled to server-rendered HTML.
How AI helps
AI reads jQuery selectors and event handlers, maps them to React components, extracts state into hooks, and generates TypeScript interfaces from runtime behaviour.
Impact
60–70% faster than manual rewrite
“We had 40,000 lines of Perl that one person understood. AI read it in an afternoon and produced a documented Python equivalent that our whole team could review. The migration went from ‘impossible’ to ‘done in two months.’”
Every organization has a wishlist — the features, redesigns, and integrations that users have been requesting for years but were always deprioritized because the legacy system made them too expensive or too risky to implement. Responsive mobile layouts. Real-time dashboards. Self-service workflows. API integrations with modern tools. Accessibility compliance.
A migration is not just a technology swap. It is a chance to deliver the wishlist. When you are already rebuilding the frontend in React and modernizing the backend, the marginal cost of adding features that would have been prohibitive in the legacy stack drops dramatically.
This is the conversation that changes how leadership perceives the migration. It is not “spend money to get back to where we are.” It is “spend money to get everything we have been asking for — plus a platform that can evolve.”
Modern, responsive UI
Real-time data
API-first architecture
Self-service workflows
Modern toolchain
Accessibility compliance
“We had a three-page wishlist that was older than half our engineering team. When we migrated the frontend to React with AI-assisted development, we delivered the top ten items as part of the migration — not as a separate project. The business saw the migration as an investment, not a cost.”
The playbook
- AI-assisted audit (1–2 weeks). Point AI at the legacy codebase to produce an architecture map, dependency graph, risk assessment, and plain-language documentation of business logic. This is your migration blueprint — and it replaces months of manual discovery.
- Pick a pilot module (2–4 weeks). Choose a bounded, low-risk module — a reporting screen, an admin panel, a standalone service. Migrate it end-to-end using AI-assisted translation. This proves the approach, builds team confidence, and produces a reusable migration pattern.
- Strangler-fig the rest (iterative). Migrate module by module behind a routing layer. New traffic hits the modern stack; legacy handles the rest. Each iteration shrinks the legacy surface area without a big-bang cutover.
- Deliver the wishlist along the way. As each module is migrated, implement the features that the legacy stack could not support. Real-time updates, mobile responsiveness, self-service workflows — deliver them incrementally so the business sees value throughout the migration, not just at the end.
- Modernize the toolchain. CI/CD pipelines, automated testing, preview environments, infrastructure-as-code — set these up early. They make every subsequent migration sprint faster and safer.
The biggest mistake organizations make is treating migration as a one-time project with a single go-live date. The most successful migrations are iterative, delivering value continuously, and using each completed module to build momentum for the next.
Legacy systems are not technical curiosities. They are business-critical assets running on borrowed time. The developers who built them are leaving. The frameworks they depend on are no longer maintained. The features your users need cannot be built on top of them.
AI has removed the last barrier: the sheer cost of understanding and translating legacy code. Migrations that would have taken years and millions now take months and a fraction of the budget. The wishlist that has been gathering dust for years? This is the moment to dust it off.
If not now, when?