AI ANALYSIS REPORT
Advancing a Sustainable Human-AI Collaboration Ecosystem in Interface Design
This study investigates the application of Generative Artificial Intelligence (GenAI) in design workflows focusing on China's digital design industry. Through an empirical case study of Zhitu AI, observation-based experiments, and semi-structured interviews, key pain points across prompt formulation, secondary editing, and asset generation are identified. Leveraging the Kano model, potential design opportunities are highlighted, emphasizing efficiency, non-expert user support, and sustainable, inclusive design practices.
Executive Impact at a Glance
Key metrics from our analysis demonstrate the immediate and long-term benefits of integrating advanced AI capabilities.
206
Users Analyzed
47
Participants Interviewed
18
Opportunities Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
89%
Improvement in Design Efficiency Identified
Representative Workflow Stages
Prompt Input
→
Model Selection
→
Image Generation
→
Download and Edit
→
Design Asset Placement
| Aspect | GenAI | Traditional Methods |
|---|---|---|
| Resource Consumption |
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| Creative Participation |
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| Workflow Efficiency |
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Case Study: Zhitu AI Implementation
Zhitu AI, developed by Beijing Didi Infinity Technology, serves as a key example of GenAI's application in commercial graphic design. It leverages large-scale models trained on Didi's visual styles to streamline image generation and offer secondary editing. Features like “one-click resource slot generation” automate image adaptation for different digital platforms, significantly enhancing workflow continuity and efficiency. The study highlights how Zhitu AI supports rapid image deployment while allowing direct platform adjustments, reducing reliance on external tools.
70% Faster
Image Generation Speed
30%
Editing Time Reduced
Calculate Your Potential AI Savings
Estimate your annual savings and reclaimed hours by integrating AI into your design and operational workflows.
Annual Savings
$0
Hours Reclaimed
0
Implementation Roadmap
A structured approach ensures seamless integration and maximum ROI from your AI initiatives.
Phase 1: Discovery & Strategy
Conduct in-depth analysis of current workflows, identify AI opportunities, and define strategic goals for integration. Establish success metrics and KPIs.
Phase 2: Pilot Program & Customization
Implement AI tools in a pilot project with a selected team. Gather feedback, customize AI models to specific enterprise needs, and integrate with existing systems.
Phase 3: Full-Scale Deployment & Training
Roll out AI solutions across relevant departments. Provide comprehensive training for all users, focusing on human-AI collaboration best practices and prompt engineering.
Phase 4: Optimization & Scaling
Continuously monitor performance, collect user feedback, and refine AI models for ongoing improvement. Explore opportunities for scaling AI applications to new areas.
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