Common Applications, Tools, and Practical Scenarios of AIGC in Design and Business

1. Common AIGC Technology Application Areas and Tools

In recent years, AIGC (AI‑generated content) has exploded, advancing from basic natural language processing to sophisticated image, audio, and video generation, continuously pushing technical boundaries.

Image generation: Models such as DALL‑E, Midjourney, and Stable Diffusion can create high‑quality images from textual prompts, covering realistic scenes to fantastical styles.

Natural language processing: GPT series excel at text generation and dialogue, understanding complex instructions and producing coherent, logical content, laying a solid foundation for design‑related applications.

2. Practiced AIGC Scenarios in Design

2.1 AIGC in Creative Design

Workflow example – 2025 North Science Conference visual design: Designers leveraged AI to produce a high‑quality, efficient main visual.

1.1 STEP 1: Theme Conceptualization

Communicate with the brand committee, extract keywords, provide style references, and finalize the design theme.

1.2 STEP 2: Concept Sketch & Inspiration Expansion

Using tools like Midjourney or liblib, a brief text prompt yields multiple sketches with varied composition, color, and visual elements, dramatically accelerating creative ideation.

1.3 STEP 3: Material Creation & Processing

Image generation: Stable Diffusion generates custom images based on style, theme, and detail requirements, suitable for product renders, backgrounds, or character illustrations.

Image optimization: AI‑enhanced tools (e.g., AI‑powered Photoshop/Illustrator) upscale low‑resolution images without quality loss and intelligently fill missing details.

1.4 STEP 4: Final Refinement & Output

After fine‑tuning image details and adding thematic text, the design is completed. The section below shows before‑and‑after optimization comparisons.

2.2 AIGC in User‑Experience Design

2.1 Early Product Research & User Study

AIGC automates data collection, user‑persona generation, competitor analysis, and demand forecasting, improving research efficiency, reducing cost, and providing solid data for subsequent design.

2.2 User Feedback Analysis & Design Optimization Suggestions

By statistically analyzing raw data, AIGC identifies pain points and offers actionable design recommendations, helping designers refine solutions and enhance user satisfaction.

3. Future Business Applications of AIGC

3.1 AIGC Boosting "Wokeke" Blue‑Collar Recruitment Platform

AI provides suggestions for visual design; designers refine these ideas into final concepts.

3.2 AIGC Supporting "Fengchong" Pet‑Service Platform

3.2.1 Image Content Generation

Tools like internal AI (Shunshou Chuang) and Midjourney create appealing pet images; text generators (DeepSeek, Doubao) produce engaging copy such as "MBTI Cat Hierarchy" series, increasing interaction.

3.2.2 Video Content Generation

AI video tools (e.g., Jianying) automatically add subtitles, voice‑overs, and effects to produce short pet‑themed videos, lowering production costs.

3.2.3 Ongoing Business Support Ideas

1) Content optimization: AI analyzes trending pet content, generates topic ideas and scripts aligned with platform tone.

2) Data‑driven refinement: AI evaluates performance metrics (views, likes, conversion) and suggests continuous content strategy improvements.

4. How to Better Harness AIGC

1) Become an "AI Tuner" – understand underlying technology and tool operation

Just as a sound engineer knows instrument mechanics, mastering prompt engineering, parameter settings, and iterative practice improves output quality.

2) Develop a Critical Eye – cultivate aesthetic judgment

Assess AI‑generated assets against design goals, brand tone, and audience expectations, ensuring they meet contemporary aesthetic standards.

3) Be a Cross‑Disciplinary Generalist – integrate knowledge from multiple fields

Leverage data insights (e.g., button click rates, page retention) to let AI analyze and propose solutions, then translate conclusions into concrete design proposals.

In summary, we must act as both AI coaches and user advocates while also shaping our own creative output.