An app modernization was once a resource-intensive process. Then, the rise of AI changed everything.
Today, integrating AI into legacy modernization workflows is transforming the industry, making AI-driven modernization the preferred approach for both enterprise and SaaS apps because of its speed and efficiency.
This impact is noticeable. At House of Angular, we are cutting refactoring time by 30–50% and reducing production bugs by over 40–50%!
Below, you’ll find a concise, step-by-step breakdown of how to achieve it.
Key Takeaways:
- Teams adopting AI-assisted legacy modernization are seeing significant reductions in both refactoring time and production bugs.
- A successful modernization starts with a thorough, AI-powered audit.
- Running a small PoC before committing to a full migration can save your team from costly surprises down the road.
- The most effective teams treat AI as a daily collaborator, not just a one-time tool.
- AI is only as effective as the team behind it—pair powerful tools with experienced developers.
Step 1: Discovery & Audit with AI
Start with a reasonably thorough audit of the current state, aided by AI (e.g., with Sourcegraph Cody).
Effect: a technical debt report highlighting modernization areas (even if just based on module age or bug counts).
Step 2: Proof of Concept with AI
Choose a small part of the legacy system and try modernizing it “with AI support” as a trial.
Use AI for refactoring. For example:
- Ask Claude Codeconvert to convert an old piece of code to a newer API
- Ask Claude Code to generate a new version of a module in Angular based on the old one in AngularJS.
It’s worth doing this under the close supervision of an experienced developer, since you’ll later model subsequent efforts on this module/fragment.
Let AI generate tests for both versions and compare results (AI can even help create a script to compare outputs).
Goal: assess how helpful AI is in your specific case and catch potential pitfalls. Such a PoC will show whether AI can understand your code and how many worthwhile suggestions it provides.
💻 See a real example of an AI-driven system modernization done for one of our U.S.-based clients. Read the case study.
Step 3: Iterative migration plan (with AI)
Based on audit and PoC findings, plan the entire roadmap. AI can help here, too.
For instance:
- Feed the model a list of modules and their dependencies (perhaps you generated it with AI earlier)
- Ask: “Propose an order for migrating these modules, minimizing risk and maximizing early business value.” The model might suggest priorities, which you then verify with stakeholders.
- Redesign: quick win. Use the Claude Code frontend-design skill to modernize your UI. MagicPath will help you create a new design system.
- Break the plan into stages (Milestone 1: API layer up, Milestone 2: new frontend for module X, etc.).
- Decide where you’ll use AI intensively (e.g., generating tests, converting data models) and where only as support.
- Define success metrics for each stage (e.g., reduce the number of bugs by X%, response time < Y, etc.).
Step 4: Iterative execution (AI-assisted)
During the execution of subsequent steps, use AI as an assistant in the team’s daily work:
Coding
Use Claude Code to generate Angular components, services, and modules based on your existing codebase or plain-English prompts.
Treat it as a daily collaborator, use it to refactor AngularJS code, scaffold new features, and speed up boilerplate while keeping developer review in the loop.
Testing
After writing a new service, use AI tools to generate a test suite that covers typical scenarios, including Playwright for end-to-end tests.
Documenting
Ask AI to create documentation for new APIs (e.g., endpoint descriptions), and also to fill in documentation for old modules (useful for new team members).
Code review
AI can assist in code reviews.
Tools that analyze PRs can catch suspicious changes or missing tests.
Refactoring
Finally, AI can help with refactors between stages. For example, if, after a few iterations, you see the need to reorganize code, models can suggest how to do it optimally.
📌 Need more hard proof? See how our team automated bug fixes in production using AI in another case study by our Team Leader, Mateusz.
Step 5: Stabilization and optimization (AI in ops)
After deploying each part, AI can be used in monitoring mode.
- We often introduce AI to analyze logs and alerts. It can diagnose problems faster (AI can piece together several signals and say “these symptoms together suggest a memory leak in X”).
- AI can forecast load and costs, e.g., by analyzing usage trends of new microservices and suggesting scale up/down, which helps avoid overpaying for infrastructure.
- Finally, AI helps in post-mortems of any incidents, e.g., it will analyze logs from before a failure and point out what was unusual.
Technical strategies for using AI in modernization
Now, let’s look at some technical strategies that you can apply during an AI-driven application modernization:
AI-assisted Development
It’s about using Claude Code directly in your daily development workflow.
It can generate Angular code based on prompts or existing files, helping you build components, services, or entire modules faster. In modernization, you can use it to create adapters for APIs, convert data formats, or rewrite legacy AngularJS code into modern Angular.
Effect: faster writing of repetitive code and fewer typos/errors.
AI-assisted Architecture
This involves using tools like Claude Code to analyze the entire repository and understand the system structure.
By feeding it code and dependency data, you can generate and interpret module relationships, identify bottlenecks, and get recommendations for better architecture. This is especially useful in modernization, where understanding dependencies is key to planning safe migrations.
Effect: better decisions on how to split the system into microservices, because AI can detect potential bounded contexts by looking at clusters of dependencies.
AI-assisted Testing
Generating tests and intelligently comparing the old vs the new system.
Example: you might have 1000 test scenarios (generated, e.g., from production logs), run them through the old and new system, and AI will compare the results and highlight differences. This is feasible because a model can quickly review two large data sets and spot outliers.
Effect: confidence that the new system responds exactly the same as the old one before we switch users over.
AI-assisted DevOps
AI in CI/CD pipelines.
There are already add-ons for, e.g., GitHub Actions that analyze build and test logs and suggest optimizations (e.g., “I noticed that test X often flakes on branch Y”).
AI can help automate rollbacks (recognize that the last deploy caused an unusual spike in errors and decide to revert).
Effect: faster and more reliable deployments; the pipeline reacts on its own and improves over time.
AI-assisted Monitoring
The mentioned use of AI in APM.
Models learn the system’s normal behavior and alert when something deviates.
This is important during modernization because the old and new systems may have slightly different characteristics. AI can catch subtle issues (e.g., a trend of increasing memory usage) before it turns into an outage.
Effect: proactive maintenance; you react before users report a problem.
Summary
AI-driven app modernization shortens timelines, reduces bugs, and keeps delivery moving through every phase, from initial audit and planning to development, testing, and production.
It’s an industry-changing switch, and right now, the fastest and most efficient legacy modernization option, especially for big enterprise applications.
But with all that being said, you need to remember: the true impact of AI depends on the team guiding its implementation.
AI will not replace your developers in this process. It’s by combining AI tools with the experience of skilled developers that you ensure the modernization process is effective and delivers real, working results.
5 min
