Moving to AI-ready, dynamic, and scalable architectures is a necessity, but the transition can seem risky, particularly when legacy systems power critical business operations.
The Strangler Fig approach, popularized by Martin Fowler, offers a proven path forward. In our experience, gradually modernizing systems ensures seamless operations while delivering quick wins. It's how we've earned Google's trust for Angular migrations and helped banks and brands like JetBlue, Kenvue, and Uniqlo modernize front-end architectures, optimize digital experiences, and build scalable, future-ready platforms that drive growth and enhance customer engagement.
How AI is changing front-end experiences
You can now build intelligent, intuitive interactions with AI that were neither feasible nor cost-effective just a year ago.
For example, OpenAI’s voice-to-voice model, released in July 2024, mirrors natural human conversation, responding nearly instantly, handling interruptions, and interpreting emotions across 25+ languages.
Plus, the cost of large language model inference has dropped to one-thousandth of what it was 3 years ago, from $60 to $0.06 per million tokens.
What does this mean for enterprises? Imagine an AI-powered customer support that retrieves data instantly, executes predefined workflows, and resolves issues autonomously, delivering faster, more personalized service, or a conversational shopping assistant that recommends products intelligently, based on a customer’s shopping history and preferences.
Large language models are also being used to automate parts of the design and development process, generating React components based on screenshots, text prompts, or Figma design files, significantly reducing the time from design to deployment.
With tools like Cursor Composer, Claude Artifacts, and OpenAI Canvas, developers can generate and edit code in-line. Not only are these tools boosting developer productivity, but they’re also enabling people to create web applications without coding at all.
What an AI-ready front-end tech stack looks like
A modern, modular, and maintenable front-end tech stack allows features and functionalities to be developed, deployed, and deleted independently, without disrupting the broader application.
It’s not a question of whether your front-end is AI-ready or not; AI readiness can be achieved incrementally, even in the most complex enterprise environments.
We recommend progressively phasing out parts of your legacy system to reduce risk, ensure business continuity, and enable continuous delivery and integration.
Start your modernization efforts on a specific feature or module that will yield meaningful and measurable results within a short timeline, ideally under a month.
Microservices architecture
Applications built as a collection of modular microservices are more adaptable to change, easier to maintain, and more cost-effective than deeply integrated monolithic applications.
Each business functionality can be added, updated, scaled, and replaced independently, without disrupting other parts of the system. The microservices are loosely coupled and operate independently, while sharing data and functionality, across frameworks and platforms.
Clear, well-documented communication between microservice APIs promotes interoperability, scalability, and flexibility across the system. An API-first approach allows new AI tools to be added to the tech stack easily without custom integration layers. Applications can switch between vendors and incorporate multiple best-of-breed tools via APIs.
For highly regulated industries, modularity also supports compliance. Isolating components that handle sensitive data such as personally identifiable information (PII), protected health information (PHI), or payment information ensures that updates to non-critical features do not trigger unnecessary audits. This approach is particularly valuable in applications like software as a medical device (SaMD).
Design systems
Adopting a design system early in development helps teams work more efficiently and consistently. Semantic tokens clarify purpose (e.g., primary-button-background), add structure, and help AI recognize patterns in codebases and streamline code changes.
A design system reduces rework and accelerates the development of AI-powered features, such as dynamic content delivery or personalized UX, with minimal disruption to existing workflows. Good design system governance bridges the gap between design and development teams, fostering better collaboration and alignment on business objectives.
Are you using Figma, shadcn/ui, and Tailwind CSS? Our latest, open-source Figma shadcn/ui kit and Figma plugin sync design tokens between Figma and code repositories. Designers can preview changes in Figma and merge approved changes into the repository; Tailwind CSS classes are generated automatically, ensuring seamless alignment between design and code.
Headless CMS
Enterprises can deploy AI agents quickly by using headless CMS platforms like Contentful or Sanity. These platforms provide structured APIs that control the information AI systems can access, minimizing the risk of inaccuracies or hallucinations.
By leveraging existing content, teams can launch AI-driven experiences in weeks, not months.
Open-source tools
Leveraging open-source tools untangles teams from restrictive proprietary software, providing flexibility and access to broader community support.
Large language models are trained extensively on open-source repositories, making them better suited for generating and interpreting code that adheres to open standards.
In contrast, proprietary or domain-specific tooling may require fine-tuning or additional context to work effectively with AI.
Monitoring
AI-driven systems require robust monitoring and governance. Simplifying the tech stack ensures that every component can be observed and controlled independently.
Vercel and the frontend cloud accelerates frontend with frictional deploys, environment management, observability, and code monitoring.
For AI, real-time monitoring tools such as Airbender, provide visibility into AI processes, enabling swift responses to anomalies or failures.
Big enough to matter, small enough to achieve
A well-scoped proof-of-concept is the best way to validate the feasibility and effectiveness of new initiatives, without disrupting existing operations or risking a large-scale failure.
Deliver quickly
A PoC should be completed within 3-4 weeks. Select a clear use case: identify a pressing pain point where you can add the most (and immediate) value and is manageable in scope.
Speed is critical for demonstrating value to stakeholders.
Avoid bottlenecks
In many organizations, integrating a new product or feature into existing systems or accessing real customer data often delays projects significantly.
We recommend developing the PoC independently of your legacy systems in a sandbox environment, as well as decoupling the development process from data dependencies to reduce risk, and accelerate time-to-production.
Use synthetic or publicly available data for the core build, and anonymized data for testing, before deploying securely in production. Plan for a phased integration with real data once the PoC has been validated. This is how we were able to build and deploy an AI PoC for Nike in just 3 weeks.
Deploy and iterate
The first version isn’t the end goal. The idea is to get something functional, test it, get feedback and stakeholder buy-in and cross-functional alignment, iterate, and scale what works.
Rapid cycles of building and deploying are critical to identify and address challenges early, ensuring the solution delivers business value.
Don’t spend months working on a perfect solution and risk missing your window of opportunity. Focus on shipping fast, learning, and iterating.
Conclusions
Modernizing your front-end tech stack for AI isn’t a risk – it’s a strategy to future-proof your business. As a digital innovation leader, your focus is on results: start small, validate impact quickly, and maximize the impact of what’s working. The goal isn’t just to adopt AI but to strategically align it with your business objectives, setting the stage for scalable, future-ready platforms that drive innovation and customer engagement.
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