The arrival of AI in front-end workflows is reshaping our practices: component generation, automated documentation, assisted refactoring, intelligent testing, unified design tokens… But when teams work across different stacks (React, Vue, Angular, Web Components, Svelte…), how can we ensure that AI produces something usable, coherent, and maintainable?
This session explores:
• The current limitations of AI in a heterogeneous front-end landscape
• Biases and pitfalls when letting AI generate code without guardrails
• How to structure a multi‑framework environment to make AI genuinely useful
• Why micro‑frontends are becoming an ideal catalyst for leveraging AI
• How to measure concrete ROI: velocity, quality, debt reduction, team autonomy
• A real‑world case study on implementing an AI‑driven pipeline in a fragmented context
• Practical examples: isolated component generation, automated standardization, AI‑augmented CI/CD
The goal is to demonstrate that AI is not a gimmick but a value multiplier — provided it is integrated into an architecture that embraces technological diversity. And that is precisely what micro‑frontends enable.

Modern front-end development has never been more powerful — or more fragmented. With multiple frameworks, heterogeneous stacks, distributed teams, and ever‑faster delivery cycles, maintaining application coherence has become a daily challenge.
In this landscape, generative AI and micro‑frontends open a new path: automating, harmonizing, and accelerating interface production while improving maintainability and overall ROI.
This talk offers a concrete return of experience on integrating AI into a fragmented front-end environment, and shows how micro‑frontends become a strategic lever to take advantage of fragmentation rather than suffer from it.