AI Coding Collaboration Models in Multiple UI Design Scenarios

As AI coding technologies fully permeate the product development and research chain, the collaboration boundaries between design and development are being continuously reshaped. The role of designers and their working paradigms are undergoing unprecedented disruption. Traditional design delivery models and one‑way requirement transfer chains can no longer support the pace of agile iteration in real‑world projects, let alone unlock the efficiency gains and value increments brought by AI technologies.

This session focuses on the core challenge of how designers can achieve efficient cross‑team collaboration with product managers (PMs) and R&D engineers (RDs) in real business projects under the context of AI coding. Centered on two pillars — "design‑driven collaboration" and "AI‑powered efficiency" — we will systematically deconstruct and share practical insights across four dimensions: role cognition, process restructuring, method implementation, and value elevation. Using real projects of varying complexity as entry points, the session will systematically unpack two business‑validated AI collaboration methodologies.

Main Content:

1. Collaboration Pain Points — Current Challenges and Development Opportunities
1.1 Repetitive work and human resource waste in traditional serial workflows
1.2 Misalignment between collaboration models and the rapid iteration rhythm of AI coding
1.3 A new closed‑loop process enabled by A2UI as a "unified workbench"

2. Practical Deconstruction — Building an Efficient Design‑to‑Development Closed Loop with AI Coding
2.1 For a mature social product: a design‑driven collaboration workflow based on Figma Make
2.2 For an AI product portfolio: an in‑depth AI coding collaboration workflow based on Cursor
2.3 Pitfall avoidance guides and efficiency tips for each role in cross‑team collaboration

3. Role Reconstruction — New Paths for Designer Value Growth Catalyzed by AI Coding
3.1 Identity transformation: from "design deliverer" to "product‑development value hub" — reshaping the cognitive framework
3.2 Model innovation: from "one‑way output" to "full‑chain collaboration" — redefining delivery logic
3.3 Capability upgrade: a new competency model for designers driven by AI coding

4. Future Thinking — Design Thinking as Rules
4.1 Design systems evolving from "static component libraries" to "AI‑powered dynamic code assets"
4.2 Designers' core assets shifting from "design mockups" to "reusable AI collaboration paradigms"
4.3 The core metric for cross‑role collaboration moving from "delivery efficiency" to "business value closure"

In the new era of AI‑driven product development and research, designers must reshape their mindset, keep pace with AI technology trends, identify pain points from real‑world collaboration challenges, build reusable methodological frameworks in human‑AI collaboration, and redefine their long‑term value within the product‑development ecosystem. Truly sustainable capability growth does not come from faster design output, but from a deeper understanding of the full chain, more precise cross‑role alignment, and more effective implementation of AI technologies to create value.

1、Icebreaking session: Introduction of the speaker and team, workshop overview
2、Methodology and case sharing: Systematic presentation of the two practice methods, demonstrating collaboration workflows for both 0‑to‑1 startup products and 1‑to‑N mature products through real project experiences
3、Hands‑on instruction: Practical application of AI tools such as Figma Make and Cursor
4、Interactive session: Group practice and sharing by writing rules/skills (or building a harness)
5、Q&A and summary

1、Junior/intermediate/senior interaction designers and experience designers
2、Mid‑level/senior visual designers
3、Other roles in the product‑development chain, including product managers and R&D engineers
4、Professionals in the AI industry and anyone interested in the AI field

1、For design practitioners: Deeply master two AI workflows, break through the functional boundaries of design roles, achieve code‑based delivery; learn how to collaborate efficiently with PMs and RDs in an AI coding context to amplify personal value.
2、For product managers: Gain insight into new models of product‑development collaboration in the AI coding era; learn how to use these collaboration methods to effectively shorten the product cycle from design to development, improving implementation efficiency and quality.
3、For R&D engineers: Understand how designers' work is evolving in the AI coding era; learn how to seamlessly interface with designers on AI‑generated, implementable code — reducing communication overhead and development friction, improving R&D efficiency, and jointly driving products to flow efficiently through the product‑development system.