5 Key Steps to AI Product Commercialization
Practical insights from building AI products from zero to one, covering market validation, tech stack selection, and growth strategies.
As a product manager with 7 years of experience, I've accumulated valuable insights from taking multiple AI products from concept to commercialization.
1. Precise User Need Positioning
AI product success starts with understanding real pain points. Before building inChat, we conducted 200+ user interviews and discovered creators' biggest pain wasn't "lack of AI" but "AI output doesn't meet expectations."
2. Choose the Right Tech Stack
Not every problem needs GPT-4. Select models based on scenarios:
- Simple tasks: Lightweight models + fine-tuned prompts
- Complex reasoning: Large models + RAG architecture
- Real-time interaction: Streaming output + caching strategies
3. Build MVP for Quick Validation
Our PhotoSongAI project completed MVP in 2 weeks, validating core hypotheses through A/B testing and avoiding 3 months of wasted development.
4. Data-Driven Growth
In the QuJianYou project, funnel analysis and user behavior tracking increased conversion rates by 10%.
5. Continuous Iteration
AI products aren't one-time deliveries but continuous learning processes. Build feedback loops to make products better over time.
Follow me for more AI product and entrepreneurship insights.