The Data Table was the most complex component in this initiative. It needed to support multiple entities, dense operational data, and complex interaction states while remaining adaptable for Arabic-first and RTL environments. Previously, a component of this scale required: • 4 working days for design (research, benchmarking, structuring variants) • 5 working days for front-end implementation Using ChatGPT, I analysed enterprise-scale data table systems to identify scalable patterns and best practices. With a cleaned system architecture and structured prompts based on our token documentation, I generated Data Table variants using Figma Make aligned with our naming and design logic. My front-end colleague then used Claude Code to translate the structured output into production-ready components. The results were significant: • Design time reduced from 4 days → 3 hours • Development time reduced from 5 days → 5 hours More importantly, we established a new collaborative framework: • AI-compatible naming conventions • Token-enforced component structure • Reusable prompt templates • A documented AI-to-code handoff model This transformed our workflow from manual component crafting into an AI-accelerated production pipeline.